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Mathematical Contest in Modeling (MCM)?, High School Mathematical Contest in Modeling (HiMCM)?, and Interdisciplinary Contest in Modeling(ICM)?are registered trade marks o? COMAP, Inc.Vol. 33, No.3????012 Table of Contents Guest EditorialNetwork Science: What’s Math Got to Do with It? Chris Arney .............................................................................. 185Editor’s NoteAbout This Issue........................................................................... 192MCM Modeling ForumResults of the 2012 Mathematical Contest in ModelingWilliam P. Fox ........................................................................... 193 A Close Look at Leaves Bo Zhang, Yi Zhang, and TianKun Lu ......................................... 205 Judges’Commentary: The Outstanding Leaf Problem Papers Peter Olsen......................................................... ....................... 223 ComputingAlong the Big Long River Chip Jackson, Lucas Bourne, and Travis Peters ............................ 231 Judges’Commentary: The Outstanding RiverProblem Papers Marie Vanisko ........................................................................... 247 Author’s Commentary: The Outstanding RiverProblem Papers Catherine A. Roberts.................................................................. 253 Judges’Commentary: The Giordano Award forthe RiverProblem Marie Vanisko and Richard D. West ............................................ 259ICM Modeling ForumResults of the 2012 Interdisciplinary Contest in Modeling Chris Arney .............................................................................. 263 Finding Conspirators in the Network via Machine Learning Fangjian Guo, Jiang Su, and Jian Gao .......................................... 275 Judges’Commentary: Modeling forCrime Busting Chris Arney and Kathryn Coronges............................................ 293Reviews............................................................................... 305Guest Editorial185Guest EditorialNetwork Science: What’s Math Got to Do with It?1Chris ArneyDept. of Mathematical Sciences U.S. Military Academy West Point, NY10996david.arney@usma.eduIntroductionThis year’s ICMR?problem involved network science,or more precisely, a component ofnetwork science—socialnetwork analysis. My post-contest re?ections have led me to believe it is time for the mathematics community to engage in this emerging subject to build a rigorous mathematical foun- dation for this important science and to join in performing mathematical modeling and interdisciplinary problem solving. Some people call network science a “new” emerging discipline, yet, as we know, mathematicians have been developing graph (network) theory for centuries, and scientists and engineers have been modeling networks for decades. What is new is that the traditional techniques have been re- placed by an entirely new arsenal of mathematics, science, and modeling associated with networks. Others callnetwork science the “new” operations research in that it con- nects quantitative concepts and elements from several disciplines such as mathematics, computer science, and information science with the qualita- tive models from sociology and other social sciences. By its very nature, network science is interdisciplinary and involves emerging areas of science such as complex adaptive systems, cooperative game theory, agent-based modeling, data analytics, and social network analysis.1With both appreciation and apologies to Tina Turner and her emotional song “What’s Love Got To Do With It” and fulltongue-in-cheek realization that unlike “love,” mathematics is certainly not a second-hand emotion. TheUMAPJournal33(3)(2012)185–191. c ?Copyright2012by COMAP,Inc. Allrightsreserved. Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for pro?t or commercial advantage and that copies bear this notice. Abstracting with credit is permitted, but copyrights for components of this work owned by others than COMAP must be honored. To copy otherwise, to republish, to post on servers, or to redistribute to lists requires prior permission from COMAP.186TheUMAP Journal 33.3 (2012) From another, perhaps simpler, perspective, you could merely call net- work science a form of applied mathematics—applied dynamic graph the- ory with additional data elements and attributes. Or perhaps from a mod- eling perspective, it is simply modeling with a highly structured, entity- linked complex adaptive system framework. I do not pretend to know exactly what network science is, or where it ?ts in with today’s scienti?c world, or what it will become. However, I believe that much of the strength of network modeling is in its ability to embrace the complexity of the real world. For me, that makes network science an important and empowering form of interdisciplinary modeling and problem solving—worthy of ICM problems and much more. In particular, I hope that the mathematics community does not ignore it. Network science needs the engagement of the mathematics community to produce its underlying framework and to invent new mathematics and computational techniques for analysis of its complex structures, develop- ment of its synergistic processes, and organizing of its overwhelming data. Likewise, mathematics needs network science to establish the relevance of mathematics in the modern information-based world. As the ICM teams discovered, network science is exciting, relevant, enjoyable, and modern— elements that mathematics currently desperately needs to bolster its future place in society.Mathematical ElementsWhat are the mathematical elements of network science? One way to de?ne a network is to establish its?components (nodes, links, data, processes);?properties (dynamic, functional, layered); and?applications(logistics,?ow,transportation,Internet,metabolicnetworks,social networks, organizational networks—perhaps there are just too many categories to list!). Another way is to use the concept of a mathematical graph (the nodal- link structure) with its nontrivial topological features and then classify the various types of graphs that occur (random, scale free, small world, scale rich) and the data (often heavy-tailed)that need to be mined and analyzed. A foundationalresearch management report on network science offered a layered approach of network roles—physical, communicative, informa- tional, biological and social/ cognitive—that connect together to produce the overallweb-like network framework . No matter whatde?nition or theoreticalframeworkisused,network sci- enceis inherently and essentially mathematicalat its core;there is plenty for applied mathematicians to do. Most networks are suf?ciently complexthatGuest Editorial187 simply relying on visualization produceserroneousintuitiveconceptions— or, worse yet, complete misunderstanding. De?ning,computing,and mea- suring well-de?ned properties can counter those misguided perceptions and improvenetwork modeling and analysis. Indispensablerolesfor math- ematical modelers in network science are?working with social scientists to build explicative and empirical models,?creating appropriate measures for important applications, and??nding appropriate properties and formalizing their measurement sys- tems and calculations. Like many other mathematical modeling con- structs, these properties can be classi?ed as structural or functional; lo- cal, global, or regional; discrete or continuous; dynamic or static; and deterministic or stochastic. The network science world needs mathematicians to help sort out these characteristics and do even more.Signi?canceNetwork science has become a major global research thrust (with fund- ing potential equal to or exceeding that of mathematics and many sciences) in the research agenda of governments, societies, militaries, businesses, and organizations. Its publication and citation qualities and quantities are signi?cant. Just look at the remarkable citation record of the works by someone such as Albert-László Barabási to see this subject’s in?uence in science. New societies, new conferences, and new journals with a network science theme are emerging at a dazzling pace. Network science is reaching the popular press and also entering the business world with tremendous fanfare.Mathematicians’EngagementOne could argue that all the Mathematics Awareness Month themes since 1997,when the theme was “Mathematics and the Internet,” have been related to networks in some way. The theme for 2004, “The Mathematics of Networks,” established a?rm connection, and I still use the myriad networks on that year’s poster as examples to students of the variety and beauty of networks. However, while mathematicians are certainly aware of network science, I still do not see much real engagement by the U.S. mathematics community. Recent meetings of the Mathematical Associa- tion ofAmericaand theAmerican MathematicalSocietyshow only minimal mathematically-connected networkresearch. TheSocietyfor Industrialand188TheUMAP Journal 33.3 (2012) Applied Mathematics and the Institute for Operations Research and Man- agement Science and their members are slightly more engaged, although “when it comes to the research agenda now popularized by network sci- ence, has been an underutilized resource” . In my opinion,mathematics is a vastly underutilized and unfortunately often missing part of network science. In a comment on Alderson , Nagurney adds that “it is not just the network topology and associated sta- tistical aspects of networks that matter but?ows that must be incorporated into network modeling as well as behavior” . Alderson and colleagues also wrote about mathematics and its engagement in Inter- net research that “surprising little attention has been paid in the mathemat- ics and physics communities ... in the Internet research arena” . Of course, there is much more to network science than the In- ternet, but it is a signi?cant network that most of the world confronts many times and in many ways every day.Family TreeIt may be worth looking at a family tree for network science. While these relationships are subjective and incomplete, one can see inFigure 1a ?ow that brings together many elements ofmathematics. The mathematics community should not miss out on an opportunity as rich and stimulating asnetwork science. Ultimately,themathematicalelementsofthisdiscipline will be accomplished somehow and by someone. I suggest that this work be done by mathematicians—and the sooner, the better.Network Science forUndergraduatesDoes network science extend to the undergraduate curriculum? Based on their interest in this year’s ICM problem, I believe that undergraduates would respond in the af?rmative. In some institutions, network science is even making inroads in establishing undergraduate programs,and courses and programs traditionally offered at the graduate level are entering the undergraduate realm. My mathematics students tell me that they want to learn network science. As analysis ofsocialmedia and online games are be- ginning to be seen to be parts ofnetwork science modeling,student interest in network science is growing in leaps and bounds. The bottom line is that network science is highly popular with students. Ihave taught three differ- ent network science courses over the past four semesters and am designing a fourth for the Fall semester of 2012. I usually have to turn away students or add more sections. This past Spring,Iteam-taught socialnetwork analy- sis with a sociologist and enjoyed the interdisciplinary modeling aspects ofGuest Editorial189Figure 1.Disciplinary connection network modelshowing some ofthe links between mathematics and network science.this exciting subject. Last year’s award-winning publication for INFORMS was a network science book by Easley and Kleinberg from Cornell written for undergraduates . Network science is on the map of undergraduate education.Worldwide InterestThe ICM data show there may be differences among nations in per- ceptions or interests in interdisciplinary modeling and network modeling. The U.S. has always been slightly behind the rest the rest of the world in ICM/ MCMR?interest ratio, as measured by the proportion of teams who choose the ICM problem rather than one of the MCM problems. Usually, there is about half as much interest in the ICM from U.S. teams compared to teams from the rest of the world; this year, that ratio dropped to about one-third. I do not know why this is so or if this phenomenon has any real signi?cance. Perhaps American students have a more disciplinary focus on mathematics or just haven’t been exposed to as many network or interdis- ciplinary ideas. Whatever the reason, I personally hope that all students (high school,undergraduate and graduate)in every nation have the oppor- tunity to study some aspects of networks, and that the mathematics they learn in doing so goes to excellent use.190TheUMAP Journal 33.3 (2012)ExhortationMathematicians, let’s not miss this opportunity. Take another look at network science and see where you can contribute. Talk to colleagues in other disciplinesand form teamstolearn,study,research,teach,and engage in this enjoyable and important?eld of network science.ReferencesAlderson, David L. 2008. Catching the “network science” bug: Insight and opportunity for the operations researcher.Operations Research56 (5) (September-October 2008): 1047–1065.http://www.informs.org/ content/download/255771/2414490/file/networks.pdf. Easley, D., and J. Kleinberg. 2010.Networks,Crowds, and Markets: Reasoning AboutaHighlyConnectedWorld.New York: CambridgeUniversityPress. Nagurney, Anna. 2008. Comment on Alderson .Operations Re- search (OnlineForum Commentary)56 (5) (October–November 2008) on- line commentary.http://www.informs.org/content/download/ 255775/2414506/file/nagurney.pdf. National Research Council. 2006.Network Science.Washington, DC: Na- tional Academies Press. Willinger, Walter, David Alderson, and John C. Doyle. 2009. Mathematics and the Internet: A source of enormous confusion and great potential. Notices of the American Mathematical Society56 (5) (May 2009): 286–299.http://www.ams.org/notices/200905/rtx090500586p.pdf.Guest Editorial191About the AuthorChrisArneygraduated from WestPointand served as an intelligence of?cer in the U.S. Army. His aca- demic studies resumed at Rensselaer Polytechnic In- stitute with an M.S. (computer science) and a Ph.D. (mathematics). He spent most of his 30-year military career as a mathematics professor at West Point, be- fore becoming Dean ofthe SchoolofMathematicsand Sciences and Interim Vice President for AcademicAf- fairs at the College of Saint Rose in Albany, NY. Chris then moved to RTP (Research Triangle Park), NC, where he served for various durations as chair of the Mathematical Sciences Division, of the Network Sciences Di- vision, and of the Information Sciences Directorate of the Army Research Of?ce. Chris has authored 22 books, written more than 120 technical arti- cles, and given more than 250 presentations and 40 workshops. His techni- cal interests include mathematicalmodeling,cooperative systems,pursuit- evasion modeling,robotics, arti?cial intelligence,military operations mod- eling, and network science;his teaching interests include using technology and interdisciplinary problems to improve undergraduate teaching and curricula. He is the founding director of COMAP’s Interdisciplinary Con- test in Modeling (ICM)R?. In August 2009, he rejoined the faculty at West Point as the Network Science Chair and Professor of Mathematics.192TheUMAP Journal 33.3 (2012)Editor’s NoteAbout This IssueThis year we had 5,000 (!) participating teams in the two contests com- bined; the 18 (!) Outstanding papers ran to over 500 manuscript pages. Editing and publishing all the Outstanding papers, which we once did, is simply not possible any more. Hence, as in 2010 and 2011, we are able to present in the pages of the Journalonly oneOutstandingentry for each oftheMCM and ICM problems. The selection of which papers to publish re?ected editorial considerations and was done blind to the af?liations of the teams. Allofthe18Outstandingpapersappearin theiroriginalform on the2012 MCM-ICM CD-ROM,which also has the press releases for the two contests, the results, the problems, unabridged versions of the Outstanding papers, and some of the commentaries. Information about ordering is athttp: //www.comap.com/product/cdrom/index.htmlor at (800) 772-6627.Results of the2012 MCM193MCM Modeling ForumResults of the 2012 Mathematical Contest in ModelingWilliam P. Fox, MCM DirectorDept. of Defense Analysis Naval Postgraduate School 1 University Circle Monterey, CA 93943–5000wpfox@nps.eduIntroductionA total of 3,697 teams of undergraduates from hundreds of institutions and departments in 16 countries spent a weekend in February working on applied mathematicsproblemsin the28th MathematicalContestin Modeling(MCM)R?. The 2012 MCM began at 8:00 P.M. EST on Thursday, February 9, and ended at 8:00 P.M. EST on Monday, February 13. During that time, teams of up to three undergraduates researched, modeled, and submitted a solution to one of two open-ended modeling problems. Students registered, obtained contest materials, downloaded the problem and data, and entered completion data through COMAP’s MCM Website. After a weekend of hard work, solution papers were sent to COMAP on Monday. Two of the top papers appear in this issue ofTheUMAP Journal, together with commentaries. In addition to this special issue ofThe UMAP Journal, COMAP offers asupplementary2012MCM-ICM CD-ROMcontaining thepress releasesfor thetwo contests,theresults,theproblems,unabridged versionsoftheOutstanding papers, and judges’ commentaries. Information about ordering is athttp: //www.comap.com/product/cdrom/index.htmlor at (800) 772–6627.Results and winning papers from the?rst 27 contests were published in special issues ofMathematical Modeling(1985–1987) andThe UMAP Journal(1985–2011). The 1994 volume ofTools for Teaching, commemorating the tenth anniversary of the contest, contains the 20 problems used in the?rst 10 years of the contest and a winning paper for each year. That volume and the specialTheUMAPJournal33(3)(2012)193–204. c ?Copyright2012by COMAP,Inc. Allrightsreserved. Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for pro?t or commercial advantage and that copies bear this notice. Abstracting with credit is permitted, but copyrights for components of this work owned by others than COMAP must be honored. To copy otherwise, to republish, to post on servers, or to redistribute to lists requires prior permission from COMAP.194TheUMAP Journal 33.3 (2012)MCM issuesoftheJournalfor thelast few yearsare availablefrom COMAP.The 1994volumeisalsoavailableon COMAP’sspecialModelingResourceCD-ROM.Also available isThe MCM at 21CD-ROM, which contains the 20 problems from the second 10 years of the contest, a winning paper from each year, and advice from advisors of Outstanding teams. These CD-ROMs can be ordered from COMAP athttp://www.comap.com/product/cdrom/index.html. This year, the two MCM problems represented signi?cant challenges:?Problem A, “The Leaves of a Tree,” asked teams to model the leaves on atree, classifying their shapes, investigating the effect of leaf shape on tree pro?le and branching, and estimating the leaf mass of a tree.?Problem B, “Camping Along the Big Long River,” asked teams to design a management plan for scheduling recreational multi-day rafting tours down a long stretch of a river. The goal was to maximize the number of trips, optimize campsite usage, and offer an optimal mix of trip lengths. COMAP also sponsors:?The MCM/ ICM Media Contest (see p. 202).?TheInterdisciplinary Contest in Modeling (ICM)R?,which runsconcurrently with MCM and next year will offer a modeling problem involving network science. Results of this year’s ICM are on the COMAP Website athttp: //www.comap.com/undergraduate/contests. The contest report, anOutstanding paper, and commentaries appear in this issue.?The High School Mathematical Contest in Modeling (HiMCM)R?, whichoffers high school students a modeling opportunity similar to the MCM. Further details are athttp://www.comap.com/highschool/contests.2012 MCM Statistics?3,697 teams participated (with 1,329 more in the ICM)?8 high school teams (0.5%)?341 U.S. teams (9%)?2,428 foreign teams (91%), from Canada, China, Finland, Germany, India, Indonesia, Ireland, Malaysia, Mexico, Palestine, Singapore, South Africa, South Korea, Spain, Turkey, and the United Kingdom?10 Outstanding Winners (0.5%)?17 Finalist Winners (0.5%)?405 Meritorious Winners (11%)?1,048 Honorable Mentions (28%)?2,211 Successful Participants (60%)Results of the2012 MCM195Problem A:The Leaves of a Tree“How much do the leaves on a tree weigh?” How might one estimate the actual weight of the leaves (or for that matter any other parts of the tree)? How might one classify leaves? Build a mathematicalmodel to describe and classify leaves. Consider and answer the following:?Why do leaves have the various shapes that they have??Do the shapes “minimize” overlapping individual shadows that are cast, so as to maximize exposure? Does the distribution of leaves within the “volume” of the tree and its branches effect the shape??Speaking of pro?les, is leaf shape (general characteristics) related to tree pro?le/ branching structure??How would you estimate the leaf mass of a tree? Is there a correlationbetween the leaf mass and the size characteristics of the tree (height, mass, volume de?ned by the pro?le)? In addition to your one-page summary sheet, prepare a one-page letter to an editor of a scienti?c journal outlining your key?ndings.Problem B: Camping along the Big Long RiverVisitorsto theBig Long River (225miles)can enjoy scenicviewsand exciting white water rapids. The river is inaccessible to hikers, so the only way to enjoy it is to take a river trip that requires several days of camping. River trips all start at First Launch and exit the river at Final Exit, 225 miles downstream. Passengers take either oar- powered rubber rafts, which travel on average 4 mph or motorized boats, which travel on average 8 mph. The trips range from 6 to 18 nights of camping on the river, start to?nish. The government agency responsible for managing this river wants every trip to enjoy a wilderness experience, with minimal contact with other groups of boats on the river. Currently,Xtrips travel down the Big Long River each year during a six- month period (the rest of the year it is too cold for river trips). There areYcamp sites on the Big Long River, distributed fairly uniformly throughout the river corridor. Given the rise in popularity of river rafting, the park managers have been asked to allow more trips to travel down the river. They want to determinehow theymightschedulean optimalmixoftrips,ofvaryingduration (measured in nights on the river)and propulsion (motor or oar)that willutilize the campsites in the best way possible. In other words, how many more boat trips could be added to the Big Long River’s rafting season?196TheUMAP Journal 33.3 (2012)The river managers have hired you to advise them on ways in which to develop the best schedule and on ways in which to determine the carrying capacity of the river, remembering that no two sets of campers can occupy the same site at the same time. In addition to your one-page summary sheet, prepare a one-page memo to the managers of the river describing your key?ndings.The ResultsThe solution papers were coded at COMAP headquarters so that names and af?liations of the authors would be unknown to the judges. Each paper was then read preliminarily by two “triage” judges at either Appalachian State University (Leaf Problem) or at the National Security Agency (River Problem) or at Carroll College (River Problem). At the triage stage, the summary and overall organization are the basis for judging a paper. If the judges’ scores diverged for a paper, the judges conferred; if they still did not agree, a third judge evaluated the paper. AdditionalRegionalJudgingsiteswerecreated attheU.S.MilitaryAcademy, the Naval Postgraduate School, and Carroll College to support the growing number of contest submissions. Final judging took place at the Naval Postgraduate School, Monterey, CA. The judges classi?ed the papers as follows:Honorable Successful Outstanding Finalist Meritorious Mention Participation Total Leaf Problem 4 8 226 482 862 1,582 River Problem 69 179 566 1,349 2,109 10 17 405 1,048 2,211 3,691We list here the 10 teams that the judges designated as Outstanding;the list of all participating schools, advisors, and results is at the COMAP Website.Outstanding TeamsInstitution and Advisor Team Members Leaf Problem“How to Measure the Weight of Leaves on a Tree ” Hong Kong Baptist University Kowloon, Hong Kong Alex Wing Kee Mok Xiaotian Wu Qingran Li Jin LiangResults of the2012 MCM197“A Close Look at Leaves” Shanghai Foreign Language School Shanghai, China YiJung Wang Bo Zhang Yi Zhang TianKun Lu “Geometrical Tree” National University of Singapore Singapore Weizhu Bao Wenji Xu Jing Zhang Jingyi Lu “The Secrets of Leaves” Zhejiang University Hangzhou, China Zhiyi Tan Cheng Fu Danting Zhu Hangqi ZhaoRiverProblem“Best Schedule to Utilize the Big Long River” Peking University Beijing, China Liu Xu Feng Nan Bi Chenwei Tian Yuan Liu “Computing Along the Big Long River” Western Washington University Bellingham, WA Edoh Y. Amiran Chip Jackson Lucas Bourne Travis Peters “Optimization of Seasonal Throughput and Campsite Utilization on the Big Long River” University of Colorado Boulder, CO Anne M. Dougherty Stephen M. Kissler Christopher Corey Sean Wiese “Getting Our Priorities Straight” Bethel University Arden Hills, MN Nathan Gossett Michael D. Tetzlaff Blaine Goscha Jacob Smith198TheUMAP Journal 33.3 (2012)“Optimal Scheduling for the Big Long River” University of Colorado Boulder, CO Anne M. Dougherty Tracy Babb Christopher V. Aicher Daniel J. Sutton “C.A.R.S.: Cellular Automaton Rafting Simulation” University of Louisville Louisville, KY Changbing Hu SurajKannan Joshua Mitchell James JonesAwards and ContributionsEach participatingMCM advisor and team member received a certi?cate signed by the Contest Director and the appropriate Head Judge. INFORMS, the Institute for Operations Research and the Management Sciences,recognized the team from the ShanghaiForeign Language School, China (Leaf Problem) and the team of Babb, Aicher, and Sutton from the University of Colorado (River Problem) as INFORMS Outstanding teams and provided the following recognition:?a letter of congratulations from the current president of INFORMS to each team member and to the faculty advisor;?a check in the amount of $300 to each team member;?a bronze plaque for display at the team’s institution, commemoratingteam members’achievement;?individual certi?cates for team members and faculty advisor as a per-sonal commemoration of this achievement;and?a one-year student membership in INFORMS for each team member,which includes their choice of a professional journal plus theOR/MS Todayperiodical and the INFORMS newsletter. The Society for Industrialand Applied Mathematics (SIAM)designated one Outstanding team from each problem as a SIAM Winner. The SIAM Award teams were from Zhejiang University (Leaf Problem) and the Uni- versity of Louisville (River Problem). Each team member was awarded a $300 cash prize, and the teams received partial expenses to present their results in a special Minisymposium at the SIAM Annual Meeting in Min- neapolis, MN in July. Their schools were given a framed hand-lettered certi?cate in gold leaf. The MathematicalAssociation ofAmerica (MAA)designated one North American team from each problem as an MAA Winner. The Winner for theResults of the2012 MCM199 Leaf Problem was a Finalist team from Cornell University with members Dennis Chua, Jessie Lin, and Alvin Wijaya, and advisor John R. Callister. The winner for the River Problem was the Outstanding team of Kissler, Corey, and Wiese from the University of Colorado. With partial travel sup- port from theMAA,theteamspresented their solution at a specialsession of the MAA Mathfest in Madison,WIin August. Each team member was pre- sented a certi?cate by an of?cial ofthe MAA Committee on Undergraduate Student Activities and Chapters.Ben Fusaro AwardOne Meritorious or Outstanding paper is selected for the Ben Fusaro Award, named for the Founding Director of the MCM and awarded for the ninth time this year. It recognizes an especially creative approach; details concerning the award, its judging, and Ben Fusaro are in Vol. 25 (3) (2004): 195–196. The Ben Fusaro Award Winner, for the Leaf Problem, was the Outstanding team from the National University of Singapore. A commentary about it appears in this issue.Frank Giordano AwardFor the?rst time, the MCM is designating a paper with the Frank Giordano Award. This award goes to a paper that demonstrates a very good example of the modeling process in a problem featuring discrete mathematics—this year, the River Problem. Having worked on the con- test since its inception, Frank Giordano served as Contest Director for 20 years. The Frank Giordano Award for 2012 went to the Outstanding team from Western Washington University in Bellingham, WA.JudgingDirector William P. Fox, Dept. of Defense Analysis, Naval Postgraduate School, Monterey, CA AssociateDirectors Patrick J. Driscoll, Dept. of Systems Engineering, U.S. Military Academy, West Point, NY Kelly Black, Mathematics Dept., Clarkson University, Potsdam, NYLeaf ProblemHead Judge Patrick Driscoll, Dept. of Systems Engineering, U.S. Military Academy, West Point, NY200TheUMAP Journal 33.3 (2012) AssociateJudges William C. Bauldry, Chair-Emeritus, Dept. of Mathematical Sciences, Appalachian State University, Boone, NC (Head Triage Judge) Karen Bolinger, Dept of Mathematics, Clarion University, Clarion, PA Tim Elkins, Dept. of Systems Engineering, U.S. Military Academy, West Point, NY(INFORMSJudge) J. Douglas Faires, Youngstown State University, Youngstown, OH (MAA Judge) Ben Fusaro, Dept. of Mathematics,Florida State University, Tallahassee,FL (SIAM Judge) Michael Jaye, Dept. of Defense Analysis, Naval Postgraduate School, Monterey, CA Mario Juncosa, RAND Corporation, Santa Monica, CA (retired) Peter Olsen, Johns Hopkins Applied Physics Laboratory, Baltimore, MD John Scharf, Dept. of Mathematics, Engineering, and Computer Science, Carroll College, Helena, MT (Fusaro Award Judge) Michael Tortorella, Dept. of Industrial and Systems Engineering, Rutgers University, Piscataway, NJ(Problem Author) Dan Zwilliger, Raytheon, Boston, MA Regional Judging Session at the U.S. Military Academy Head Judge Patrick J. Driscoll, Dept. of Systems Engineering AssociateJudges Tim Elkins, James Enos, Kenny McDonald, and Elizabeth Schott, Dept. of Systems Engineering Paul Steve Horton, Dept. of Mathematical Sciences Jack Picciuto, Of?ce of Institutional Research —all from the United States Military Academy at West Point, NY Paul Heiney, Dept of Mathematics, U.S. Military Academy Preparatory School, West Point, NY Ed Pohl, Dept. of Industrial Engineering Tish Pohl, Dept. of Civil Engineering —both from University of Arkansas, Fayetteville, AR Triage Session at Appalachian State University Head TriageJudge William C. Bauldry, Chair, Dept. of Mathematical Sciences AssociateJudges Bill Cook, Ross Gosky, Jeffry Hirst, Katie Mawhinney, Trina Palmer, Greg Rhoads, Ren′e Salinas, Tracie McLemore Salinas, Kevin Shirley, and Nate Weigl —all from the Dept. of Mathematical Sciences, Appalachian State University, Boone, NCResults of the2012 MCM201 Amy H. Erickson and Keith Erickson —Dept. of Mathematics, Georgia Gwinnett College, Lawrenceville, GA Steven Kaczkowski and Douglas Meade —Dept. of Mathematics, University of South Carolina, Columbia, SCRiverProblemHead Judge Maynard Thompson, Mathematics Dept., University of Indiana, Bloomington, IN AssociateJudges Peter Anspach, National Security Agency, Ft. Meade, MD (Head Triage Judge) Robert Burks, Operations Research Dept., Naval Postgraduate School, Monterey, CA Jim Case, Baltimore, MD (SIAM Judge) Veena Mendiratta, Lucent Technologies, Naperville, IL Greg Mislick, Operations Research Dept., Naval Postgraduate School, Monterey, CA Scott Nestler, Operations Research Dept., Naval Postgraduate School, Monterey, CA Jack Picciuto, Of?ce of Institutional Research, U.S. Military Academy, West Point, NY Kathleen M. Shannon, Dept. of Mathematics and Computer Science, Salisbury University, Salisbury, MD (MAA Judge) Dan Solow, Case Western Reserve University, Cleveland, OH (INFORMSJudge) Marie Vanisko, Dept.ofMathematics,Engineering,and Computer Science, Carroll College, Helena, MT (Giordano Award Judge) Richard Douglas West, Francis Marion University, Florence, SC (Giordano Award Judge) Regional Judging Session at the Naval Postgraduate School Head Judges William P. Fox, Dept. of Defense Analysis Frank R. Giordano, Dept. of Defense Analysis AssociateJudges Michael Jaye, Dept. of Defense Analysis Robert Burks, Greg Mislick, and Scott Nestler, Operations Research Dept. —all from the Naval Postgraduate School, Monterey, CA Joanna Bieri, University of Redlands, Redlands, CA Rich West, (retired) PA202TheUMAP Journal 33.3 (2012) Triage Session at Carroll College Head Judge Marie Vanisko AssociateJudges Terry Mullen and Kelly Cline —all from Dept. of Mathematics, Engineering, and Computer Science, Carroll College, Helena, MT Triage Session at the National Security Agency Head TriageJudge Peter Anspach, National Security Agency (NSA), Ft. Meade, MD AssociateJudges Jim Case, Dean McCullough, and judges from within NSASources of the ProblemsThe Leaf Problem was contributed by Lee Zia (Program Director, Na- tional Science Foundation Division of Undergraduate Education). The River Problem wascontributed by CatherineRoberts(Dept.ofMathematics and Computer Science, College of the Holy Cross, Worcester, MA).AcknowledgmentsMajor fundingfor theMCMisprovided by theNationalSecurityAgency (NSA) and by COMAP. Additional support is provided by the Institute for Operations Research and the Management Sciences (INFORMS), the Soci- ety for Industrial and Applied Mathematics (SIAM), and the Mathematical Association of America (MAA). We are indebted to these organizations for providing judges and prizes. We also thank for their involvement and un?agging support the MCM judges and MCM Board members, as well as?Two Sigma Investments.“This group of experienced, analytical, and technical?nancial professionals based in New York builds and operates sophisticated quantitative trading strategies for domestic and interna- tional markets. The?rm is successfully managing several billion dol- lars using highly-automated trading technologies. For more information about Two Sigma, please visithttp://www.twosigma.com.”Results of the2012 MCM203CautionsTothereader of research journals: Usually a published paper has been presented to an audience, shown to colleagues, rewritten, checked by referees, revised, and edited by a jour- nal editor. Each paper here is the result of undergraduates working on a problem over a weekend. Editing (and usually substantial cutting) has taken place; minor errors have been corrected, wording has been altered for clarity or economy, and style has been adjusted to that ofThe UMAP Journal. The student authors have proofed the results. Please peruse these students’efforts in that context. Tothepotential MCM advisor: It might be overpowering to encounter such output from a weekend of work by a small team of undergraduates, but these solution papers are highly atypical. A team that prepares and participates will have an enrich- ing learning experience, independent of what any other team does. COMAP’sMathematicalContestin Modelingand InterdisciplinaryCon- test in Modeling are the only international modeling contests in which students work in teams. Centering its educational philosophy on mathe- matical modeling, COMAP serves the educational community as well as the world of work by preparing students to become better-informed and better-prepared citizens.Editor’s NoteThe complete roster of participating teams and results has become too longtoreproducein theJournal. Itcan now befound attheCOMAPWebsite, in separate?les for each problem:http://www.comap.com/undergraduate/contests/mcm/contests/ 2012/results/2012-Problem-A.pdf http://www.comap.com/undergraduate/contests/mcm/contests/ 2012/results/2012-Problem-B.pdf204TheUMAP Journal 33.3 (2012)Media ContestThis year, COMAP again organized an MCM/ ICM Media Contest. Over the years, contest teams have increasingly taken to various forms of documentation of their activities over the grueling 96 hours—frequently in video, slide, or presentation form. This material has been produced to provide comicrelief and let off steam, as well as to provide some memories days, weeks, and years after the contest. Weloveit, and we want to encour- age teams (outside help is allowed) to create media pieces and share them with us and the MCM/ ICM community. The media contest iscompletely separatefrom MCM and ICM. No matter how creative and inventive the media presentation, it hasnoeffect on the judging of the team’s paper for MCM or ICM. We do not want work on the media project to detract or distract from work on the contest problems in any way. This is a separate competition, one that we hope is fun for all. Further information about the contest is athttp://www.comap.com/undergraduate/contests/mcm/media.html. There were 11 entries, from Zhejiang University, United States Military Academy, Dalian Maritime University, and Beijing Institute of Technology. Outstanding Winners:?United States Military Academy, joint entry from three teams (Nolan Miles, Andrew Lopez, Benjamin Garlick, Brian Kloiber, Calla Glavin, Kailee Kunst, Samuel Ellis, Tanner Robertson, Robert Hume)?Zhejiang University (Jiajun Chen, Yuchen Lei, Canyang Jin) Finalists:?Dalian Maritime University (Chengcheng Bi, Xuefu Bai, Bo Han)?Dalian Maritime University(Zuchen Tang, Zihao Yu, Bowen Zhang) The remaining entries were awarded Honorable Mention. Complete results, including links to the Outstanding and Finalist videos, are athttp://www.comap.com/undergraduate/contests/mcm/contests/ 2012/results/media/media.html.A CloseLook at Leaves205A Close Look at LeavesBo Zhang Yi Zhang Tiankun LuShanghai Foreign Language School Shanghai, China Advisor: YiJung WangAbstract We construct four models to study leaf classi?cation, relationships be- tween leaf shape and leaf distribution, correlations between leaf shape and tree pro?le, and total leaf mass of a tree. Model 1 deals with the classi?cation of leaves. We focus primarily on the most conspicuous characteristic of leaves, namely, shape. We create seven geometric parameters to quantify the shape. Then we select six common types of leaves to construct a database. By calculating the deviation index of the parameters of a sample leaf from those of typical leaves, we can classify the leaf. To illustrate this classi?cation process, we use a maple leaf as a test case. Model 2 studies the relationship between leaf shape and leaf distribu- tion. First, we simplify a tree into an idealized model and then introduce the concept of solar altitude. By analyzing the overlapping individual shad- ows through considering the relationship between leaf length and internode length under different solar altitudes, we?nd that the leaf shape and distri- bution are optimized to maximize sunlight exposure according to the solar altitude. We apply the model to three test types of trees. Model 3 discusses the possible association between tree pro?le and leaf shape. Based on the similarity between the leaf veins and branch structure of trees,weproposethatleafshapeisatwo-dimensionalmimicofthetreepro?le. Employing the method of Model 1, we set several parameters re?ecting the general shape of each tree and compare them with those of its leaves. With the help of statistical tools, we demonstrate a rough association between tree pro?le and leaf shape. Model 4 estimates the total leaf mass of a tree given size characteristics. Carbon dioxide (CO2) sequestration rate and tree age are introduced to es- tablish the link between leaf mass and tree size. Since a unit mass of a leaf sequesters CO2at a constant rate, the CO2sequestration rate has a quadratic relationship with the age of the tree, and the size the tree experiences logistic growth.TheUMAPJournal33(3)(2012)205–222. c ?Copyright2012by COMAP,Inc. Allrightsreserved. Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for pro?t or commercial advantage and that copies bear this notice. Abstracting with credit is permitted, but copyrights for components of this work owned by others than COMAP must be honored. To copy otherwise, to republish, to post on servers, or to redistribute to lists requires prior permission from COMAP.206TheUMAP Journal 33.3 (2012)IntroductionWe tackle four main subproblems:?classi?cation of leaves,?the relationship between leaf distribution and leaf shape,?the relationship between the tree pro?le and the leaf shape, and?calculation of the total leaf mass of a tree. To tackle the?rst problem, we select a set of parameters to quantify the characters of the leaf shape and use the leaf shape as the main standard for our classi?cation process. For the second question, we use the overlapping area that one leaf’s shadow casts on the leafdirectly under it as the link between the leafdistri- bution and the leaf shape, since the leaf shape affects the overlapping. We assume that the leaf distribution tries to minimize the overlapping area. As for the third question, we set parameters for the tree pro?le and comparethosewith theparametersfor thetree’sleafshapetojudgewhether there is a relation between tree pro?le and leaf shape. We use age to link the size of tree and the total weight of its leaves, because the tree size has an obvious relationship with its age and the age affectsatree’ssequestration ofcarbon dioxide,which affectsthetotalweight of a tree’s leaves.Assumptions?The trees are all individual (“open grown”) trees, such as are typically planted along streets, in yards, and in parks. Our calculation does not apply to densely raised trees, as in typical reforestation projects where large numbers of trees are planted close together.?The shape of the leaves does not re?ect special uses for the trees, such as to resist extremely windy, cold, parched, wet, or dry conditions, or to produce food.?The type of the leaf distribution (leaf length and internode distance rela- tion) re?ects the tree’s natural tendency to sunlight.?The tree pro?le that we consider is the part above ground, including thetrunk, the branches, and leaves.?All parts of a leaf can lie?at, and the thickness or protrusion of veins canbe neglected.?Leaves are the only part of the tree that reacts in photosynthesis andrespiration, so that the carbon dioxide sequestration of a tree is the sum of the sequestration of the leaves.A Close Look at Leaves207?The sequestration of a tree or a leaf is the net amount of CO2?xed in a tree, which is the difference between the CO2released in respiration andthe CO2absorbed in photosynthesis.?The trees are in healthy, mature, and stable condition. Trees of the samespecies have same characteristics.Model 1: Leaf Classi?cationDecisive ParametersTo classify the shape of a leaf, we set seven parameters and establish adatabase for comparison.RectangularityWe de?ne the ratio of the area of the leaf to the area of its minimumbounding rectangle as the leaf’srectangularity(Figure 1).Figure 1. Figure 2. Figure 3. Figure 4. The photographs of leaves inFigures 1–4are reproduced (with overlays by the authors of this paper) from Knight et al. , by kind permission of that paper’s authors.Aspect RatioThe aspect ratio is the ratio of the height of the minimum bounding rectangle to its width. (Figure 2).CircularityTo evaluate how round a leaf is, we consider that ratio if the ex-circle tothe in-circle. (Figure 3).Form FactorForm factor, a famous shape description parameter, is calculated as FF= 4πA P2,208TheUMAP Journal 33.3 (2012) whereAis the area of the leaf andPis its perimeter.Edge Regularity Area IndexAlthough the aspect ratio and the rectangularity of two leaves may be similar, the contour or the exact shape of two leaves may vary greatly. To take the different contour of the leaf into consideration, we join every convexdot along thecontour and develop what wecalltheboundingpolygon area. The ratio between the leaf area and this bounding polygon area is the edge regularity area index. The closer this ratio is to 1, the less jagged and smoother the leaf’s contour is (Figure 3).Edge Regularity PerimeterIndexSimilarly,we develop another parameter,theboundingpolygon perimeter, the perimeter of the polygon when we join the convex dots of a leaf. We de?ne the ratio of the bounding polygon perimeter to the perimeter of the leaf to be theedgeregularity perimeter index. The smaller this ratio, the more jagged and irregular the contour of the leaf is (Figure 3).Proportional IndexSince it is also highly critical to capture the spatial distribution of dif- ferent portions of a leaf along its vertical axis, we divide the minimum bounding rectangle into four horizontal blocks of equal height, and then calculate the proportion of the leaf area in a particular region to the total leaf,which we refer to as theproportionalindex (PI)for that region (Figure 4). Hence, the PI is a vector of length four.Common Types of LeavesWe develop a database of the six most common leaf types in North America (Figure 6), using the seven parameters discussed above.Table 2gives the values of the parameters for each leaf type, as measured from scans of photos of leaves in Knight et al. .ComparisonGiven a speci?cleaf,we calculatethe seven characteristicsofit and com- pare them with our database by calculating the squared deviation of each parameter of the given leaf from the corresponding standard parameter of each category. We realize that some of the parameters are somehow more important than others. So in an effort to make our model more accurateA Close Look at Leaves209Figure 5.The six most common seen leaf types in North America. (The photos, from Knight et al. , are reproduced by kind permission of that paper’s authors.) Table 1. Parameter values for the six leaf types. Type 1 2 3 4 5 6 Rectangularity 0.6627 0.5902 0.6250 0.4772 0.4876 0.6576 Aspect Ratio 0.8615 0.6600 0.1800 0.6383 0.4792 0.3111 Circularity 0.8140 0.5432 0.4564 0.3454 0.3123 0.3311 Form Factor 0.9139 0.6206 0.2823 0.2470 0.3662 0.4956 ER Area Index 0.9322 0.8780 0.9091 0.8500 0.7880 0.8895 ER Perimeter Index 0.8727 0.8889 0.9384 0.8602 0.8231 0.9903 PI10.0649 0.0769 0.1179 0.1909 0.1299 0.2920 PI20.2958 0.3555 0.2208 0.3892 0.3606 0.4187PI30.3439 0.4243 0.4139 0.3047 0.4123 0.2677PI40.2954 0.1433 0.2474 0.1152 0.0970 0.0220and reliable, we introduce a weightedindex of deviationID, withID=7?i=1wiIi,where eachIiis the squared deviation, except thatI7= 1 44?j=1(PIj?PInew,i)2.We determine the weights via the Analytical Hierarchy Process (AHP) . We build a7×7matrix reciprocal matrix by pair comparison:210TheUMAP Journal 33.3 (2012)? ? ? ? ? ? ? ? ?R AR C FF ERAI ERPI PI R1 1/3 1 1/4 1/2 1/2 1/7AR3 1 3 1 2 2 1/3C1 1/3 1 1/4 1/2 1/2 1/7FF4 1 4 1 3 3 1/2ERAI2 1/2 2 1/3 1 1 1/4ERPI2 1/2 2 1/3 1 1 1/4PI7 3 7 2 4 4 1 ? ? ? ? ? ? ? ? ? .The meaning of the number in each cell is explained inTable 2. The numbers themselves are based on our own subjective decisions.Table 2.The multiplication table ofD10. Intensity of Value Interpretation 1 Requirementsiandjhave equal value. 3 Requirementihas a slightly higher value thanj. 5 Requirementihas a strongly higher value thanj. 7 Requirementihas a very strongly higher value thanj. 9 Requirementihas an absolutely higher value thanj. 2, 4, 6, 8 Intermediate scales between two adjacent judgments. Reciprocals Requirementihas alowervalue thanj.We then input the matrix into a Matlab program that calculates the weightwiof each factor, as given inTable 3.Table 3. AHP-derived weights. Factor R AR C FF ERAI ERPI PI Weight 0.0480 0.1583 0.0480 0.2048 0.0855 0.0855 0.3701We test the consistency of the preferences for this instance of the AHP. For good consistency :?Theprincipaleigenvalueλmaxofthematrixshould beclosetothenumbernof alternatives, here 7; we getλmax= 7.05.?The consistency index CI =(λmax?n)/(n?1)should be close to 0; weget CI= 0.009.?The consistency ratio CR = CI/ RI (where RI is the average value of CIfor random matrices) should be less than 0.01; we get CR= 0.006. Hence, our decision method displays perfectly acceptable consistency and the weights are reasonable.A Close Look at Leaves211Model TestingWe use a maple leafofFigure 6to test our classi?cation model. Visually, it resembles Category 4 most.Figure 6.Test maple leaf.Now we test this hypothesis with our model. First, we process theimage of the leaf, calculating rectangularity, aspect ratio, circularity, form factor, edge regularity area index, edge regularity perimeter index, and the proportional index, with values as inTable 2. The values of the seven parameters are shown inTable 4.Table 4. Parameter values for the sample maple leaf. Factor R AR C FF ERAI ERPI PI1PI2PI3PI4Measured 0.355 0.908 0.269 0.157 0.625 0.379 0.097 0.463 0.431 0.009 valueFinally, we use our weights to calculate the index of deviationIDof the maple leaf from each of the six categories of leaves considered earlier. We show the results inTable 5.Table 5. Index of deviation of maple leaf from six common leaf categories. Category 1 2 3 4 5 6 Index of deviationID0.27 0.12 0.230.080.24 0.18Sincetheindexofdeviation between thegiven mapleleafand Category4 is smallest, the model predicts that the maple leaf falls into Category 4— which conclusion is consistent with our initial hypothesis.ConclusionOur model is robust under reasonable conditions, as can be seen from the testing above. However, since our database contains only the six commonly-seen leaf types in North America, the variety in the database has room for improvement.212TheUMAP Journal 33.3 (2012)Model2: LeafDistributionandLeafShapeIntroductionGeneticand environmentalfactors contribute to the pattern ofleafveins and tissue, thereby determining leaf shape. In this model, we investigate how leaf distribution in?uences leaf shape.Idealized Leaf Distribution ModelWe construct an idealized model that immensely simpli?es the complex situation: The tree is made up of a branch perpendicular to the ground surface, and two identical leaves grown on the branch ipsilaterally (on the same side) and horizontally. The leaves face upward and point toward the sun in the sky. We suppose that the tree is at latitudeL(Northern Hemisphere). Let the greatest average solar altitude in a year, which is attained at noon on the vernal equinox, beα. Figure 7illustrates our primitive model of a tree at noon on the vernal equinox.Figure 7.Primitive model of a tree, at noon on the vernal equinox.Analysis of Overlapping AreasOur focus is the partly shaded leaf inFigure 7. The output of the model is what proportion of the leaf (PL) is shaded. We divide the situation into three scenarios, depending on the in?uence of the angleαon PL.A Close Look at Leaves213SolarAltitude Near90?This situation usually takes place in tropical regions, where leaf shapes are typically broad and wide and the tree crown usually contains only one layer of leaves. This can be explained in terms ofFigure 7: Withαnear 90?, the shaded part of the lower leaf would be too big to supply enoughsolar energy for photosynthesis, and the greatest absorption of energy can be achieved by a broad leaf shape.SolarAltitude Near0?This situation usually takes place in frigid zones, where leaves are typ- ically acicular (needle-shaped) and the tree crown contains dense layers of closely-grown leaves. In terms ofFigure 7: Withαnear 0?, the shaded part ofthe lower leafwould approach zero, allowing a much more concentrated distribution of leaves than in other situations. In addition, the maximum absorption of energy can be best achieved by needle-like leaves.SolarAltitude within Normal RangeThis scenario is typical in the temperate zone on earth, where sunlight irradiates the leaves in a tilted way. It is also the case in which our idealized model is the most suitable. Another crucial factor that we control in this case is the distancehbetween the two points connecting the leaves and the branch. We assume that a tree’s leaf distribution tries to minimize the overlappingarea between leaves,soour modelinvestigatesthequantitative relationship between the overlapping area and the shape of the leaf. To simplify the model, we model the leaf as a rhombus, whose major axis has lengthLmajorand whose minor axis has lengthLminor. Also, we ?x the area of the leaf asA, to ensure constant exposure area to the sun. With area?xed, now we only need to change the length of the major axis to change the shape of the leaf (seeFigure 8).Figure 8.Two leaves of the same area but different lengths of major axis.214TheUMAP Journal 33.3 (2012) Also, since we have?xed the area of the leaf and just adjust its shape, the minimum proportion of the lower leaf shaded isE= AoverlappingA ,whereAoverlappingis the smallest overlapping area. The most ef?cient situation is for both leaves to be totally exposed to sunlight, as inFigure 9a: For some valueh=h0, we achieveE= 0.Figure 9a.Upper leaf does not overlap lower one. Figure9b.Upper leafoverlapslower one.What ifh h0, as inFigure 9b? We can easily give the relationship amongh,Lmajor, andEfor a given?xed solar altitudeα:E= ? Lmajortanα?h Lmajortanα ?2= ? 1? h Lmajortanα ?2.For?xedhandα, the overlap area increases as the length of the leaf increase. The closerLmajoris toh/tanα, the smaller the overlap.From our discussion, the best leaf distribution occurs whenh=h0,which meansh=Lmajortanα.Model TestingWe need to test whether this relation between leaf distribution and leafshape is right. We offer data on leaf lengthLmajorand internode distancehof several kinds of trees and use our formula to calculate the respectivesolar altitudes of the trees. By converting the solar altitude into latitude, we can predict the origin of a tree! We choose species native to China:?Ligustrum quihoui Carr.(waxy-leaf privet or Quihou privet, a semi- evergreen to evergreen shrub);A CloseLook at Leaves215?Osmanthusfragrans(sweet olive,tea olive,or fragrant olive,an evergreen shrub or small tree that is the city?ower of Hangzhou, China); and?Camelliajaponica(Japanese camellia) as our test trees.Table 6shows the results.Table 6. Test of model for leaf shape as a function of latitude. Tree kindLmajorhCalculated Latitude tanαPredicted True Ligustrum quihoui Carr.2 2.5 1.25 38.7?35–35?Osmanthus fragrans10 18.5 1.85 28.4?23–29?Camelliajaponica6 9 1.50 33.7?32–36?The predicted latitudes of origin are close to the true latitudes, con- ?rming our hypothesis of a relationship between leaf distribution and leaf shape.Model 3: Tree Pro?le and Leaf ShapeHypothesisSince?the vein structure determines the leaf shape;?the branch structure determines the tree pro?le; and?to some degree, the leaf veins resemble branches, we have a wild hypothesis that the leaf shape is two-dimensional mimic of the tree pro?le.Comparison of Leaf Shape and Tree ContourThe leafshape is two-dimensional,so it is relatively easy to study its pa- rameters. However, the tree pro?le is three-dimensional, so it is important to?nd a two-dimensional characteristic of a tree to use for comparison. Since the longitudinal section of a particular tree re?ects its general size characteristics, we focus on that.Tree Pro?le Classi?cationIn theleafclassi?cation model,thereare6generalclassesofleaves. Since we are comparing only the general resemblance between leaf and tree, we216TheUMAP Journal 33.3 (2012) incorporate Class 5 (elliptic leaf with serrated margin) into Class 2 (elliptic leaf,smooth margin). As a result,we get 5classes ofleaves and 5respective types of trees:?Class 1: Cordate (Texas redbud)?Class 2 and Class 5: Elliptic (camphor tree)?Class 3: Subulate (pine)?Class 4: Palmate (oak)?Class 6: Obovate (mockernut hickory)Parameters of the TreeWe appoint three parameters for the longitudinal section that can be compared with those of the leaf shape, namely, rectangularity, aspect ratio, and circularity. Table 7shows the measurements for both trees and leaves.Table 7. Comparison of leaf parameters and tree parameters. Class 1 2 and 5 3 4 6 Rectangularity (R) Leaf 0.6627 0.5902 0.6250 0.4772 0.6576 Tree 0.6281 0.6846 0.5180 0.5292 0.6238 Aspect Ratio (AR) Leaf 0.8615 0.6600 0.1800 0.6383 0.3111 Tree 0.7914 0.7243 0.6601 0.7980 0.6750 Circularity (C) Leaf 0.6396 0.5698 0.1834 0.3069 0.2889 Tree 0.5800 0.5928 0.2895 0.4070 0.3866For each of the parameter types, we drew a scatterplot, calculated the correlation, and investigated the statistical signi?cance of the resulting line of best?t. Aspect ratio (AR) and circularity (C) were each statistically signi?cant, pointing to linear relationships;rectangularity (R) was not.ConclusionThetestsofaspect ratio and circularity support thetheory that leafshape is a two-dimensional mimic of the tree contour. Thus, the shape of leaf resembles the shape of tree to some extent.A Close Look at Leaves217Model 4: Leaf MassIntroductionA simple way to calculate the total leaf mass is to multiply the number of leaves by the mass of a single leaf. Our method is more accurate and less demanding, in that our model is (surprisingly!) independent of these two factors but dependent on a more reliable factor of a grown tree: photosyn- thesis. Our methodology of estimating the leaf mass of a tree is based on three variables:?tree age;?growth rate, which is determined by tree species; and?general type (hardwood or conifer). In other words, given the age and type of a tree, we can estimate the total mass of leaves. In this model, CO2is used as a calculating medium.Leaf Mass and Tree AgeLeaf Mass and CO2SequestrationTrees sequester CO2from the atmosphere in their leaves but mostly elsewhere in the tree. A tree’s ability to sequester CO2is measured in termsof massASof CO2(in pounds)per gram of leaf. Hardwood trees sequestermore CO2per gram of leaf than conifers.A tree’s ability to sequester CO2is different from its ability to absorb it,sincethe tree also releasesCO2into the atmosphere as part ofits respiration.In other words, CO2sequestration = CO2absorption?CO2release. Now we need only to estimate the weight of CO2sequestered by the tree and then calculate the total mass of the leaves as the ratio of the mass of CO2sequestered to the mass of CO2sequestered per leaf:mleaves= mCO2AS.CO2Sequestration and Tree AgeThe relationship between the amount of CO2sequestered, the age of a tree, and the type of tree is given in a table by the Energy Information Administration , which also divides trees based on their growth rate: fast, moderate, or slow. For each growth rate, we graphed the annual sequestration rate vs. age of the tree and?tted a quadratic model (seeFigure 10for conifer example).218TheUMAP Journal 33.3 (2012)Figure 10.Annual CO2sequestration rates, in pounds of carbon per tree per year, for three rates of growth of conifer trees of increasing age.We were surprised to?nd that the curves?t the data perfectly! (This fact strongly suggests that the original table values were not measured but calculated from such a model.) From the equations of the?tted curves, we can easily estimate the CO2sequestered for a tree ofa given age and growth rate and consequently calculate the mass of the leaves.Tree Age and Tree SizeAbove, we used the age of a tree as a link between the two leaf massand the size characteristics of the tree. Since we now know the relationship between the age ofa tree (ofa particular growth rate)and its totalleafmass, now we only need to work out the relationship between the age of the tree and the size characteristics of it. Tree size is the accumulation of growth, which is a biological phenomenon of increase with time. In its life cycle, a tree experiences logisticgrowth, leading to a model for its “size,” or pro?le,P(height, mass, diameter) asP=k1? 1?ek2A?k3,henceA=k4ln ? 1?k5Pk6? ,whereAis the age of the tree and thekiare constants that depend on the species of tree.Leaf Mass and Tree SizeFinally, we get to answer the question of whether there is a relationshipbetween leaf mass and tree size characteristics. Putting together our earlier models, we have the relationships inFigure 11.A Close Look at Leaves219Figure 11.According to our earlier results,leafmassand treeagearerelated to each other through CO2sequestration, and we have just determined a functionbetween tree age and tree size.Strengths and WeaknessesModel 1Strengths:Our modelis based on quantitativeanalysis,so the classi?cation processis both objective and ef?cient. Our model is based on categories of leaf types that are the most typical and common.Weakness:We divide leaves into only six categories, which may not cover all leaf types.Model 2Strengths:Wehavetaken intoconsideration threeclimateconditions(tropicalzone,temperate zone,and frigid zone)in discussing the relationship between the leaf distribution and the leaf shape. The results of our model conform to the data that we found.220TheUMAP Journal 33.3 (2012)Weakness:We consider the leaf distribution on a single branch but have not con- sidered the inner-in?uence between different leaves of different branches.Model 3Strength:The whole process uses data and quantitative analysis as foundations,so the output is objective and reasonable.Weakness:We have limited categories of tree pro?les.Model 4Strength:We use the carbon sequestration rate and age as the media to calculatethe total mass of leaves, which is better than trying to estimate the number of leaves and the average weight of each.Weakness:The data are from a source that does not refer to the method of arriving at the data.Letterto a Science Journal EditorDear Editor: We present to you our key?ndings. We?rst focus on the possible in?uence on leafshape ofthe leafdistribu- tion on the tree. For survival reasons, a tree should develop an optimal leaf distribution and shape pattern that adjust to the speci?c region of its ori- gin, thereby gaining the most nutrients for photosynthesis by maximizing the exposure area to sunshine. We demonstrate a mathematical relation- ship among solar altitude, leaf shape, and leaf distribution. Based on this ?nding, we may be able to determine the best location for replanting or assisted-migration of a tree species by observing its leaf distribution. Our second key?nding is a rough relationship between the tree’s pro- ?le and its leaves. In fact, we hypothesize that a leaf is a two-dimensional mimic of the tree. For several trees, we compared the shape of the leaf and the contour of the tree,?nding similarities between certain characteristics.A CloseLook at Leaves221 This?nding is another instance of the natural world containing examples of self-similarity, a mathematical concept that means that an object is ap- proximately similar to a part of itself, as is the case for the mathematical objects of the Koch snow?ake and the Mandelbrot set. The third part of our study deals with the relationship between tree size characteristics and the total mass of the leaves. The two are linked by the CO2sequestration rate and the age of the tree. Hence, we can estimate the total mass of the leaves given some pro?le parameters of a tree, such as its height, diameter, volume, age, and type. This?nding might have potential for agricultural and environmental uses, such as a new method to estimate tea production or wood production, or estimation of the CO2sequestration effect of a forest as an alleviator of global warming. In hope of publishing our research in your journal, we enclose our re- search paper for you to examine and judge. We are convinced that our research on leaves promises to contribute to a variety of areas. Sincerely yours, Team 14990AcknowledgmentThe authors thank David Knight, James Painter, and Matthew Potter of the Dept. of Electrical Engineering at Stanford University for permission to reproduce photos of leaves from their paper Knight et al. .ReferencesAlonso, Jos′ e Antonio, and Ma Teresa Lamata. 2006. Consistency in the analytic hierarchy process: A new approach.International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems14 (4): 445–459.http://hera.ugr.es/doi/16515833.pdf. Du,Ji-Xiang,Xiao-Feng Wang,and Guo-Jun Zhang. 2007. Leafshapebased plant species recognition.Applied Mathematics and Computation185 (2) (February 2007): 883–893. Energy Information Administration, U.S. Department of Energy. 1998. Method for calculating carbon sequestration by trees in urban and sub- urban settings.ftp://ftp.eia.doe.gov/pub/oiaf/1605/cdrom/pdf/ sequester.pdf. Im, C., H. Nishida, and T.L. Kunil. 1998. Recognizing plant species by leaf shapes—a case study of theAcerfamily. InProceedings of 1998 IEEE222TheUMAP Journal 33.3 (2012) International Conference on Pattern Recognition, Brisbane, August 1998, vol. 2, 1171–1173. Knight, David, James Painter, and Matthew Potter. 2010. Automatic plant leaf classi?cation for a mobile?eld guide: An android application.http://www.stanford.edu/~jpainter/documents/Plant%20Leaf% 20Classification.pdfandhttp://www.stanford.edu/class/ee368/Project_10/Reports/Knight_Painter_Potter_PlantLeafClassification.pdf. Saaty, Thomas L. 1982.Strategy and Organization, The Analytical Hierarchy ProcessforDecisionsin aComplex World. Belmont,CA:Lifetime Learning Pub. Tsukaya, Hirokazu 2006. Mechanism of leaf-shape determination.Annual Review of Plant Biology57 (1): 477–496. Wang,Z.,Z.Chi,and D.Feng. 2003. Shape based leafimage retrieval.IEEE Proceedings: Vision, Image, and Signal Processing150 (1) (February 2003): 34–43. Team members Tiankun Lu, Bo Zhang, and Yi Zhang.Judges’Commentary223Judges’Commentary: The Outstanding Leaf Problem PapersPeter Olsen, P.E.Commander, US Coast Guard Reserve Baltimore, MDpcolsen@gmail.comA manager would rather live with a problem he cannot solve than accept asolution hedoes not understand.—Robert E.D. “Gene” Woolsey IntroductionProblem A of the 2012 MathematicalContest in Modeling (MCM)tmwaswritten by Lee Zia, who posed a challenging problem, “How can you mea- sure the weight of leaves on a tree?” and several equally challenging sub- problems. The problems were easy to state, but there were no traditional approaches. Successful teams would have to combine existing models, data, and new ideas in creative and original ways. The results were gratifying. The judges were impressed by the variety of approaches submitted by the teams. The approaches were creative and the models showed each team’s ability to use their own new ideas to re?ne and extend work that had gone before. No two of the Outstanding papers shared the same model. Some share parts and data; but those are emphasized, combined, and used in different ways. Existing work, often quickly?ndable on Google, forms the scaffold on which each team built their own model. The?nal structures were a pleasure to behold.Problem Statement“How much do the leaveson a tree weigh?” How might oneestimatethe actual weight of the leaves (or for that matter any other parts of the tree)?TheUMAPJournal33(3)(2012)223–229. c ?Copyright2012by COMAP,Inc. Allrightsreserved. Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for pro?t or commercial advantage and that copies bear this notice. Abstracting with credit is permitted, but copyrights for components of this work owned by others than COMAP must be honored. To copy otherwise, to republish, to post on servers, or to redistribute to lists requires prior permission from COMAP.224TheUMAP Journal 33.3 (2012) How might one classify leaves? Build a mathematical model to describe and classify leaves. Consider and answer the following:?Why do leaves have the various shapes that they have??Do the shapes “minimize” overlapping individualshadows that are cast, so as to maximize exposure? Does the distribution of leaves within the “volume” of the tree and its branches effect the shape??Speaking of pro?les, is leaf shape (general characteristics) related to tree pro?le/ branching structure??How would you estimate the leaf mass of a tree? Is there a correlationbetween the leaf mass and the size characteristics of the tree (height, mass, volume de?ned by the pro?le)? In addition to your one-page summary sheet, prepare a one-page letter to an editor of a scienti?c journal outlining your key?ndings.DataFor some of the subproblems, such as the leaf classi?cation, real data could be obtained easily. For others, such as the calculation of the mass of the leaves of the tree, it was dif?cult or impossible to obtain real data. In these latter cases, the teams showed creativity?nding and using secondary sources.Criteria forJudgingHere are some of the issues that kept papers from the?nal rounds:?Errors in mathematics, which quickly took them out of further consider- ation.?Including mathematics that didn’t?t the?ow of the presentation. In afew cases, mathematics appears to have been inserted to make a paper look more credible or to take the place of other work that had led to a dead end.?Changing notation, sometimes even within a single section.?Using unde?ned or poorly de?ned symbols, or using symbols before de?ning them.?Incomplete expressions, either because the team made an error or be-cause the expression did not survive the word-processor. (One of the Outstanding papers addressed in this commentary had a few incomplete expressions, probably because they didn’t survive the word-processor,Judges’ Commentary225 but the coherence of its model and the strength of its presentation over- came that defect.)Modeling IssuesThis problem required two different types of model:?Increasing abstraction:The leaf classi?cation model abstracted from an immense number of natural leaf characteristics a set of arti?cial ones small enough to be useful for classi?cation.?Decreasing abstraction:The leaf mass problem took abstract models, ap- plied them to data, and got concrete numerical results Some models were dif?cult to understand; poor writing was the most common cause. Another cause was the use of inapposite mathematics. If the mathematics was a result of a “drive-by” insertion,?tting it into the model could be dif?cult. Here are a few of the modeling issues that hurt some papers’chance of entering the?nal rounds:?Questionable,con?icting,orunjusti?ablyspeculativeassumptions. Good papers did not assume any spherical cows (“a metaphor for highly sim- pli?ed scienti?c models of complex real life phenomena” ).?Dependence ondeus ex machina: an assumption, equation, reference, or procedure invoked without explanation or context. Often the invocation would start with the phrase “It is well-known that...” It may be well- known to those who know it well, but that is unlikely to be the customer or client.?Confusing, missing, or misplaced model de?nitions; model de?nitions are more complex and more important than mathematical ones, since they must not only name thede?niendumbut also specify what it is and what it is to be used for.?Failure to reach a conclusion.?Con?icting subproble models with unexplained con?icts between as- sumptions.?Unexplained inconsistencies in data.?An unclear, incomplete, or unrepresentative letter to the journal editor.?A poor abstract: –too much detail, so much that it was dif?cult to see the overall struc- ture of the model or the strategy for using it; or226TheUMAP Journal 33.3 (2012) –too little detail, so that it was dif?cult for the reader to what was actually to be done; or –an incomplete abstract, presenting only part of the problem.?Poor presentation, including bad prose style, poor vocabulary, and dis- organized explanations. Good presentation won’t get a bad paper into the?nals, but poor presentation may keep a good one out. (The weight given to this criterion varies among the judges.)Letterto a Journal EditorThe one-page letter to a journal editor was an important part of the problem. Its goal was to give insight into whether or not the teams could explain their results clearly, simply, and directly. The most important crite- rion of modeling is whether or not the models are used, either to increase understanding directly (through use) or indirectly (through publications, conferences,or professionaltools such as software). A modelthat cannot be understood will not be used (see the quotation from Woolsey at the head of this commentary). A good letter should present an overview of the problem, technique, and results in a single page. The clarity of each team’s letter is one indication of how their model might fare in the real world.The Outstanding PapersHong Kong Baptist UniversityThis team’s entry was nicely laid out and easy to follow. The tree- classi?cation models appeared to be traceable back to the?rst principles of physics. Each model’s development began with a clear description of the ap- proach the team intended to follow. For example, in the leaf classi?cation subproblem theapproach wasto reduceallleafstructuresto a oneofseveral polar coordinate functional shapes. These easily can be distinguished. The team’s solution to the problem of?nding the mass of leaves on a tree was unique. The team used the structural properties of the tree, not properties of the tree canopy directly. The advantage of this approach is that the team did not need any information about the size or density of the canopy,the properties ofindividualleaves,or the number or distribution of the leaves. Knowing each branch’s modulus of elasticity and its de?ection under load provided enough information so that its leaf load could be inferred from thebranch’sde?ection. Conceptually,thissolution wasmuch simpler than most of the others. As a practical matter, users of this solution might?nd dif?culty in obtaining some of the data, such as the de?ection ofJudges’Commentary227 an unloaded branch; but if they could, this would an ef?cient and elegant solution. The presentation was excellent for all models. The prose, graphics, and equations?owed seamlessly throughout the paper. The team’s letter to the editor was the paper’s one weakness. The team employed a very high-level approach, laying out the overall goals for the problem, but without giving insight into the models’operational details.Shanghai Foreign Language SchoolThis paper had a particularly strong beginning. Within the space of three pages, the team?reorganized the problem into four consolidated subproblems,?stated their assumptions clearly and succinctly, and?provided a table listing their model’s parameters and their symbols. The team’s leaf classi?cation model used seven simple measurement procedures involving 10parameters,the most complicated ofwhich is area. The measurementscan be conducted on-siteusing only a sheet of?ne-ruled graph paper. Only one parameter requires calculation: division of the area of a fractional leaf segment by the leaf’s entire area. (In times past, this could have been done by eye with a simple nomograph. Now people will stop and key data into calculators.) As with the team from Hong Kong Baptist University, the model for estimating leaf mass has an unusual approach. The model does not rely on direct measurements of leaf characteristics or tree size. This can be used to show that leaf-mass and tree size are correlated. The challenge in using this model is determination of the rate of sequestration of carbon-dioxide. The modelusessequestration data from a U.S.DepartmentofEnergy document. The last section of the paper contained a clear and well-organized sum- mary list of each problem’s strengths and weaknesses. The team’s letter to the editor was clear and concise. It covered the high- levelstatement ofthe problem,then gave enough detailofthe solution plan that an knowledgeablebut non-expertreader could feelconversantwith the approach.National University of SingaporeThis team’s leaf classi?cation algorithm is the simplest of the four de- scribed in this commentary. It has four steps:?project the leaf onto a grid;?determine the grid squares covered by the projection to determine if theleaf has convexities:228TheUMAP Journal 33.3 (2012)?If it convex, it is a palmate leaf, exit;?if it is not convex, then perform further classi?cation. The leafmass is calculated based on the team’s vector tree modeloftree- structureand their insolation model. Thevector treemodelrepresentsa tree as three-dimensional vectors; daughter branches are obtained by applying a linear transform to the parents. This entry made excellent use of graphics in presenting their models and results. This team’s letter to the editor successfully wove their research, their results, and their ideas about further research into a single clear narrative.Zhejiang UniversityThis paper presented a neural-net-based leaf classi?er that was most sophisticated of all of the leaf classi?cation schemes. The input layer had 4 nodes, the middle layer 10 nodes, and the output layer had 1 node. The team divided a sample of leaves into four classes. They trained the network on 32 exemplars of each class, then tested the network on 8 other leaves drawn at random from the entire ensemble. The network misclassi?ed 1 of the 8. In general, it’s impossible to tell how a back-propagation reaches its results; but it’s reasonable to hypothe- size that more training data might have corrected the one misclassi?cation. The leaf mass estimation was the most traditional of these four papers. It was based directly on the leaf mass constant, a known value that varies with treespecies,and an estimateofthevolumeofan approximatingregular solid.SummaryThese four solutions had strong similarities—importantly, not in the solutions themselves. Models work when they provide understandable bases for reasonable decisions. All four solutions met that criterion and several others:?They were presented clearly. –The descriptive text was clear. There were comparatively few errors in grammar, vocabulary, or style; and these didn’t interfere with the reader’s understanding. –Graphics were appropriate and clear. They supported the argument being made. The appropriate text referred to them.?The models were appropriate to the problem to be solved, in that –the assumptions and goals were clearly stated;Judges’Commentary229 –thephysicswascorrectand appropriate—therewerenodeiexmachina or spherical cows; –there was no extraneous mathematics air-dropped into the model— the solution was organized in sections; and –the graphics were easy to?nd.ReferenceArney, Chris, and Kathryn Coronges. 2012. Modeling for crime busting. TheUMAP Journal33 (3) (2012) 291–302. Wikipedia. 2012. Spherical cow.http://en.wikipedia.org/wiki/Spherical_cow. Woolsey, Robert E.D. 2003.Real World Operations Research: The Woolsey Papers. Marietta, GA: Lionheart Publications.AcknowledgmentsThis paper bene?tted from insights in the Judges’Commentary by Chris Arney and Kathryn Coronges in this issue.About the AuthorA graduate of the U.S. Coast Guard Academy, Peter Olsen retired fromthe Coast Guard Reserveas a Commander in 1997,after 27years service,ac- tive and reserve. His most challenging assignment was to build the quanti- tativemodelused to allocateresourcesfor theExxon Valdezoil-spillcleanup. Of the model, Vice Admiral Robbins, the on-scene coordinator, wrote that it was completed on time, it was used by the people who paid for it, and its predictions were borne out by events.230TheUMAP Journal 33.3 (2012)Computing Along theBig Long River231Computing Along the Big Long RiverChip Jackson Lucas Bourne Travis PetersWestern Washington University Bellingham, WA Advisor: Edoh Y. AmiranAbstract We develop a model to schedule trips down the Big Long River. The goal is to optimally plan boat trips of varying duration and propulsion so as to maximize the number of trips over the six-month season. We model the process by which groups travel from campsite to campsite. Subject to the given constraints, our algorithm outputs the optimal daily schedule for each group on the river. By studying the algorithm’s long-term behavior, we can compute a maximum number of trips, which we de?ne as the river’s carrying capacity. We apply our algorithm to a case study of the Grand Canyon, which has many attributes in common with the Big Long River. Finally, we examine the carrying capacity’s sensitivity to changes in the distribution of propulsion methods, distribution of trip duration, and the number of campsites on the river.IntroductionWe address scheduling recreational trips down the Big Long River so as to maximize the number of trips. From First Launch to Final Exit (225 mi), participants take either an oar-powered rubber raft or a motorized boat. Trips last between 6and 18nights,with participants camping at designated campsites along the river. To ensure an authentic wilderness experience, at most one group at a time may occupy a campsite. This constraint limits the number of possible trips during the park’s six-month season. We model the situation and then compare our results to rivers with similar attributes,thus verifying that our approach yields desirable results. Our model is easily adaptable to?nd optimal trip schedules for rivers of varying length, numbers of campsites, trip durations, and boat speeds.TheUMAPJournal33(3)(2012)231–246. c ?Copyright2012by COMAP,Inc. Allrightsreserved. Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for pro?t or commercial advantage and that copies bear this notice. Abstracting with credit is permitted, but copyrights for components of this work owned by others than COMAP must be honored. To copy otherwise, to republish, to post on servers, or to redistribute to lists requires prior permission from COMAP.232TheUMAP Journal 33.3 (2012)De?ning the Problem?How should trips of varying length and propulsion be scheduled to maximize the number of trips possible over a six-month season??How many new groups can start a river trip on any given day??What is the carrying capacity of the river—the maximum number ofgroups that can be sent down the river during its six-month season?Model OverviewWe design a model that?can be applied to real-world rivers with similar attributes (i.e.,the GrandCanyon);?is?exible enough to simulate a wide range of feasible inputs; and?simulates river-trip scheduling as a function of a distribution of triplengths (either 6, 12, or 18 days), a varying distribution of propulsion speeds, and a varying number of campsites. The model predicts the number of trips over a six-month season. It also answers questions about the carrying capacity of the river, advantageous distributions of propulsion speeds and trip lengths, how many groups can start a river trip each day, and how to schedule trips.ConstraintsThe problem speci?es the following constraints:?Trips begin at First Launch and end at Final Exit, 225 miles downstream.?There are only two types of boats: oar-powered rubber rafts and motor- ized boats.?Oar-powered rubber rafts travel 4 mph on average.?Motorized boats travel 8 mph on average.?Group trips range from 6 to 18 nights.?Trips are scheduled during a six-month period of the year.?Campsites are distributed uniformly along the river.?No two groups can occupy the same campsite at the same time.Computing Along theBig Long River233Assumptions?We can prescribe the ratio of oar-powered river rafts to motorized boats that go onto the river each day. Therecan beproblems if toomany oar-powered boats arelaunchedwith short trip lengths.?The duration of a trip is either 12 days or 18 days for oar-powered rafts, and either 6 days or 12 days for motorized boats. Thissimpli?cation stillallowsourmodeltoproducemeaningfulresults whileletting us comparetheeffect of varying trip lengths.?There can only be one group per campsite per night. This agrees with thedesires of theriver manager.?Each day, a group can only move downstream or remain in its current campsite—it cannot move back upstream. Thisrestrictsthe?ow ofgroupstoasingledirection,greatly simplifying how wecan movegroups from campsitetocampsite.?Groups can travel only between 8a.m.and 6p.m., a maximum of 9 hours of travel per day (one hour is subtracted for breaks/ lunch/ etc.). Thisimpliesthat perday, oar-poweredraftscan travelat most 36miles, and motorized boats at most 72 miles. This assumption allows us to determinewhich groups can reasonably reach agiven campsite.?Groups never travelfarther than the distancethat they can feasibly travel in a single day: 36 miles per day for oar-powered rafts and 72 miles per day for motorized boats.?We ignore variables that could in?uence maximum daily traveldistance, such as weather and river conditions. Thereis noway of accurately including thesein themodel.?Campsites are distributed uniformly so that the distance between camp- sites is the length of the river divided by the number of campsites. Wecan thusrepresent theriverasan array ofequally-spacedcampsites.?A group must reach the end of the river on the?nal day of its trip: –A group will not leave the river early even if able to. –A group will not have a?nish date past the desired trip length. This assumption?ts what we believe is an important standard for the river manager and for thequality of thetrips.234TheUMAP Journal 33.3 (2012)Table 1. Notation. Symbol Meaning gigroupi titrip length for groupi, measured in nights;6≤ti≤18dinumber of nights groupihas spent on the riverYnumber of campsites on the river cYlocation of campsiteYin miles downstream;0 cY225 c0campsite representing First Launch (used to construct a waitlist of groups)c?nalcampsite (which is always “open”) representing Final Exitlilocation of groupi’s current campsite in miles down the river;0 li225aiaverage distance that groupishould move each day to be on schedule;ai= 225/timimaximum distance that groupican travel in a single dayPipriority of groupi;Pi= (di/ti)(li/225)Gcset of groups that can reach campsitecRratio of oar-powered rafts to motorized boats launched each day Xcurrent number of trips down Big Long River each year Mpeak carrying capacity of the river (maximum number of groups that can be sent down the river during its six-month season) Ddistribution of trip durations of groups on the riverMethodsWe de?ne some terms and phrases: Open campsite:A campsiteisopen ifthereisnogroup currentlyoccupying it: Campsitecnis open if no groupgiis assigned tocn. Moving to an open campsite:For a groupgi, its campsitecn, moving to some other open campsitecm?=cnis equivalent to assigninggito thenew campsite. Since a group can move only downstream, or remain at their current campsite, we must havem≥n. Waitlist:The waitlist for a given day is composed ofthe groups that are not yet on the river but will start their trip on the day when their ranking on the waitlist and their ability to reach a campsitecincludes them in the setGcof groups that can reach campsitec, and the groups are deemed“the highest priority.” Waitlisted groups are initialized with a current campsite value ofc0(the zeroth campsite), and are assumed to have priorityP= 1until they are moved from the waitlist onto the river. Off the River:We consider the?rst space off of the river to be the “?nal campsite”c?nal, and it is always an open campsite (so that any number of groups can be assigned to it. This is consistent with the understanding that any number of groups can move off of the river in a single day.Computing Along theBig Long River235The Farthest Empty CampsiteOur schedulingalgorithm usesan arrayasthedata structuretorepresent the river, with each element of the array being a campsite. The algorithm begins each day by?nding the open campsitecthat is farthest down the river, then generates a setGcof all groups that could potentially reachcthat night. Thus,Gc={gi|li+mi≥c},whereliis the group’s current location andmiis the maximum distancethat the group can travel in one day.?The requirement thatmi+li≥cspeci?es that groupgimust be able to reach campsitecin one day.?Gccan consist of groups on the river and groups on the waitlist.?IfGc=?, then we move to the next farthest empty campsite—located upstream,closer to the start ofthe river. The algorithm always runs from the end of the river up towards the start of the river.?IfGc?=?,then thealgorithm attemptstomovethegroup with thehighest priority to campsitec. The scheduling algorithm continues in this fashion until the farthest empty campsite is the zeroth campsitec0. At this point, every group that was able to move on the river that day has been moved to a campsite, and we start the algorithm again to simulate the next day.PriorityOnce a setGchas been formed for a speci?c campsitec, the algorithm must decide which group to move to that campsite. ThepriorityPiis ameasure of how far ahead or behind schedule groupgiis:?Pi1: groupgiis behind schedule;?Pi1: groupgiis ahead of schedule;?Pi=1: groupgiis precisely on schedule. We attempt to move the group with the highest priority intoc. Some examples of situations that arise, and how priority is used to re- solve them, are outlined inFigures 1and2.Priorities and OtherConsiderationsOur algorithm always tries to move the group that is the most behind schedule, to try to ensure that each group is camped on the river for a236TheUMAP Journal 33.3 (2012)Downstream?→ Campsite 1 2 3 4 5 6 Group A B C Open Open Farthest PriorityPA= 1.1PB=1.5PC= 0.8open campsite Figure 1.The scheduling algorithm has found that the farthest open campsite is Campsite 6 and Groups A, B, and C can feasibly reach it. Group Bhas the highest priority, so we move Group Bto Campsite 6. Downstream?→ Campsite 1 2 3 4 5 6 Group A Open C Open Farthest B Priority PA=1.1PC= 0.8open campsite Figure 2.As the scheduling algorithm progresses past Campsite 6, it?nds that the next farthest open campsite is Campsite 5. The algorithm has calculated that Groups A and C can feasibly reach it; sincePA PC, Group A is moved to Campsite 5.number of nights equal to its predetermined trip length. However, in some instances it may not be ideal to move the group with highest priority to the farthest feasible open campsite. Such is the case if the group with the highest priority isaheadof schedule (P 1). We provide the following rules for handling group priorities:?Ifgiisbehindschedule,i.e.Pi1,then movegitoc,its farthest reachableopen campsite.?Ifgiisaheadof schedule, i.e.Pi1, then calculatediai, the number ofnights that the group has already been on the river times the average distance per day that the group should travel to be on schedule. If the result is greater than or equal(in miles)to the location ofcampsitec,then movegitoc. Doing so amounts to movinggionly in such a way that itis no longer ahead of schedule.?Regardless ofPi, if the chosenc=c?nal, then do not movegiunlessti= di. This feature ensures thatgi’s trip will not end before its designated end date. Theonecasewhereagroup’spriorityisdisregarded isshown inFigure3.Scheduling SimulationWe now demonstrate how our model could be used to schedule river trips. In the following example, we assume 50 campsites along the 225-mile river, and we introduce 4 groups to the river each day. We project the tripComputing Along theBig Long River237Downstream?→ Campsite 170 171 ... 223 224 Off Group D Open Open Open Open Farthest PriorityPD=1.1open campsite tD= 12dD= 11 Figure 3.The farthest open campsite is the campsite off the river. The algorithm?nds that Group D could move there, but Group D hastD dD—that is, Group D is supposed to be on the river for 12 nights but so far has spent only 11—so Group D remains on the river, at some campsite between 171 and 224 inclusive.schedules of the four speci?c groups that we introduce to the river on day 25. We choose a midseason day to demonstrate our model’s stability over time. The characteristics of the four groups are:?g1: motorized,t1= 6;?g2: oar-powered,t2= 18;?g3: motorized,t3= 12;?g4: oar-powered,t4= 12. Figure 5shows each group’s campsite number and priority value for each night spent on the river. For instance, the column labeledg2gives campsite numbers for each of the nights ofg2’s trip. We?nd that eachgiis off the river after spending exactlytinights camping, and thatP→1asdi→ti, showing that as time passes our algorithm attempts to get (andkeep) groups on schedule.Figures 6and7display our results graphically. These?ndings are consistent with the intention of our method; we see in this small-scale simulation that our algorithm produces desirable results.Case StudyThe Grand CanyonThe Grand Canyon is an ideal case study for our model, since it shares many characteristics with the Big Long River. The Canyon’s primary river raftingstretch is226miles,ithas235campsites,and itisopen approximately six months of the year. It allows tourists to travel by motorized boat or by oar-powered river raft for a maximum of12or 18days,respectively . Using the parameters of the Grand Canyon, we test our model by run- ning a number ofsimulations. We alter the number ofgroups placed on the water each day, attempting to?nd the carrying capacity for the river—the238TheUMAP Journal 33.3 (2012)Figure 4.Visual depiction of scheduling algorithm.Computing Along theBig Long River239 Campsite numbers and priority values for each group Number of nights spent on river ?? ?? ?? ?? ?? ?? ?? ?? 1 ????????????????????????????2 ?????????????????????????????3 ??????????????????????????????4 ????????????????????????????????5 ????????????????????????????????6 ?????????????????????????????7 ??????????????????????????????8 ??????????????????????????9 ??????????????????????????11 ?????????????????????????12 ??????????????????????????13 ???????????????????????14 ??????????????????????15 ??????????????16 ?????????????17 ??????????????18 ??????????????19 ??????????????20 ????????????Figure 5.Schedule for example of groups launched on Day 25.? ? ?? ?? ?? ?? ?? ?? ?? ?? ?? ? ? ? ? ? ? ? ? ? ? ???????????????????? ???????? ?????? ????? ????? ? ????? ? ????? ? ????? ?Figure 6.Movement of groups down the river based onFigure 5. Groups reach the end of the river on different nights due to varying trip-duration parameters.240TheUMAP Journal 33.3 (2012)? ? ?? ?? ?? ?? ?? ?? ?? ?? ?? ? ? ? ? ? ? ? ? ? ? ???????????????????? ???????? ?????? ????? ????? ? ????? ? ????? ? ????? ?Figure 7.Priority values of groups over the course of each trip. Values converge toP= 1due to the algorithm’s attempt to keep groups on schedule.maximum number ofpossibletrips over a six-month season. The main con- straint is that each trip must last the group’s planned trip duration. During its summer season, the Grand Canyon typically places six new groups on the water each day ,so we use this value for our?rst sim- ulation. In each simulation, we use an equal number of motorized boats and oar-powered rafts, along with an equal distribution of trip lengths. Our model predicts the number of groups that make it off the river (completed trips), how many trips arrive past their desired end date (late trips), and the number of groups that did not make it off the waitlist (total left on waitlist). These values change as we vary the number ofnew groups placed on the water each day (groups/ day).Table 2. Results of simulations for the number of groups to launch each day. Simulation Groups/ day Trips Left on Completed Late waitlist 1 6 996 2 8 1328 3 10 1660 4 12 1992 5 14 2324 6 16 2656 7 17 2834 8 18 2988 9 19 3154 5 10 20 3248 10 43 11 21 3306 14 109Computing Along theBig Long River241 Table 1indicates that a maximum of 18 groups can be sent down the river each day. Over the course of the six-month season, this amounts to nearly 3,000 trips. Increasing groups/ day above 18 is likely to cause late trips (some groups are stillon the river when our simulation ends)and long waitlists. In Simulation 1, we send 1,080 groups down river (6 groups/ day×180days)but only 996groupsmakeit off;theother groupsbegan near the end of the six-month period and did not reach the end of their trip before the end of the season. These groups have negligible impact on our results and we ignore them.Sensitivity Analysis of Carrying CapacityManagersoftheBig Long River arefaced with a similar task to that ofthe managers of the Grand Canyon. Therefore, by?nding an optimal solution for the Grand Canyon, we may also have found an optimal solution for the Big Long River. However, this optimal solution is based on two key assumptions:?Each day, we put approximately the same number of groups onto the river; and?the river has about one campsite per mile. We can make these assumptions for the Grand Canyon because they are true for the Grand Canyon, but we do not know if they are true for the Big Long River. To dealwith theseunknowns,wecreateTable 3. Itsvaluesaregenerated by?xing the numberYof campsites on the river and the ratioRof oar- powered rafts to motorized boats launched each day, and then increasing the number of trips added to the river each day until the river reaches peak carrying capacity.Table 3. Capacity of the river as a function of the number of campsites and the ratio of oarboats to motorboats. Number of campsites on the river 100 150 200 250 300 1:4 1360 1688 2362 3036 3724 Ratio 1:2 1181 1676 2514 3178 3854 oar : motor 1:1 1169 1837 2505 3173 3984 2:1 1157 1658 2320 2988 3604 4:1 990 1652 2308 2803 3402The peak carrying capacities inTable 3can be visualized as points in a three-dimensional space, and we can?nd a best-?t surface that passes (nearly) through the data points. This best-?t surface allows us to estimate242TheUMAP Journal 33.3 (2012) the peak carrying capacityMof the river for interpolated values. Essen- tially, it givesMas a function ofYandRand shows how sensitiveMis tochanges inYand/ orR.Figure 7is a contour diagram of this surface.Figure 7.Contour diagram of the best-?t surface to the points ofTable 3.The ridge along the vertical lineR= 1 : 1predicts that for any givenvalue ofYbetween 100 and 300, the river will have an optimal value ofMwhenR= 1 : 1. Unfortunately, the formula for this best-?t surface israther complex, and it doesn’t do an accurate job of extrapolating beyond the data ofTable 3;so it is not a particularly usefultoolfor the peak carrying capacity for other values ofR. The best method to predict the peak carrying capacity is just to use our scheduling algorithm.Sensitivity Analysis of Carrying Capacity reRandDWe have treatedMas a function ofRandY, but it is still unknown to ushowMis affected by the mix of trip durations of groups on the river (D).Computing Along theBig Long River243 For example, if we scheduled trips of either 6 or 12 days, how would this affectM? The river managers want to know what mix of trips of varying duration and speed will utilize the river in the best way possible. We use our scheduling algorithm to attempt to answer this question. We?x the number of campsites at 200 and determine the peak carrying capacity for values ofRandD. The results of this simulation are displayed inTable 4.Table 4.Carrying capacity of the river by trip lengths and boat type. Distribution of trip lengths 12 only 12 or 18 6 or 12 6, 12, or 18 1:4 2004 1998 2541 2362 Ratio 1:2 2171 1992 2535 2514 oar : motor 1:1 2171 1986 2362 2505 2:1 1837 2147 2847 2320 4:1 2505 2141 2851 2308Table 4is intended to address the question ofwhat mix oftrip durations and speeds will yield a maximum carrying capacity. For example: If the river managers are currently scheduling trips of length?6, 12, or 18: Capacity could be increased either by increasingRto be closer to 1:1 or by decreasingDto be closer to “6 or 12.”?12 or 18: DecreaseDto be closer to “6 or 12.”?6 or 12: IncreaseRto be closer to 4:1.ConclusionThe river managers have asked how many more trips can be added totheBig Long River’sseason. Without knowing thespeci?csofhow theriver is currently being managed, we cannot give an exact answer. However, by applyingour modeltoa studyoftheGrand Canyon,wefound resultswhich could be extrapolated to the context of the Big Long River. Speci?cally, the managers of the Big Long River could add approximately(3,000?X)groups to the rafting season, whereXis the current number of trips and 3,000 is the capacity predicted by our scheduling algorithm. Additionally, we modeled how certain variables are related to each other;M,D,R, andY. River managers could refer to our?gures and tables to see how they could change their current values ofD,R, andYtoachieve a greater carrying capacity for the Big Long River. We also addressed scheduling campsite placement for groups moving down the Big Long River through an algorithm which uses priority values to move groups downstream in an orderly manner.244TheUMAP Journal 33.3 (2012)Limitations and ErrorAnalysisCarrying Capacity OverestimationOur model has several limitations. It assumes that the capacity of the river is constrained only by the number of campsites, the trip durations, and the transportation methods. We maximize the river’s carrying capac- ity, even if this means that nearly every campsite is occupied each night. This may not be ideal, potentially leading to congestion or environmental degradation of the river. Because of this, our model may overestimate the maximum number of trips possible over long periods of time.Environmental ConcernsOur case study of the Grand Canyon is evidence that our model omits variables. We are con?dent that the Grand Canyon could provide enough campsites for 3,000 trips over a six-month period, as predicted by our algo- rithm. However, since the actual?gure is around 1,000 trips ,the error is likely due to factors outside ofcampsite capacity, perhaps environmental concerns.Neglect of RiverSpeedAnother variable that our model ignores is the speed of the river. River speed increases with the depth and slope of the river channel, making our assumption of constant maximum daily travel distance impossible . When a river experiences high?ow, river speeds can double, and entire campsites can end up under water . Again, the results of our model don’t re?ect these issues.ReferencesC.U. Boulder Dept. of Applied Mathematics. n.d. Fitting a surface to scat- tered x-y-z data points.http://amath.colorado.edu/computing/Mathematica/Fit/. Jalbert, Linda, Lenore Grover-Bullington, and Lori Crystal, et al. 2006. Colorado River management plan. 2006.http://www.nps.gov/grca/ parkmgmt/upload/CRMPIF_s.pdf. NationalPark Service. 2008. Grand Canyon NationalPark. High?ow river permitinformation.http://www.nps.gov/grca/naturescience/high_ flow2008-permit.htm. . 2011. Grand Canyon National Park. 2011 Campsite List.http: //www.nps.gov/grca/parkmgmt/upload/2011CampsiteList.pdf.Computing Along theBig Long River245 Sullivan, Steve. 2011. Grand Canyon River Statistics Calendar Year 2010.http://www.nps.gov/grca/planyourvisit/upload/Calendar_ Year_2010_River_Statistics.pdf. Wikipedia. 2012. River.http://en.wikipedia.org/wiki/River.Memo to Managers of the Big Long RiverIn response to your questions regarding trip scheduling and river ca- pacity, we are writing to inform you of our?ndings. Our primary accomplishment is the development of a scheduling al- gorithm. If implemented at Big Long River, it could advise park rangers on how to optimally schedule trips of varying length and propulsion. The optimal schedule will maximize the number of trips possible over the six- month season. Our algorithm is?exible, taking a variety of different inputs. These include the number and availability of campsites, and parameters associ- ated with each tour group. Given the necessary inputs, we can output a daily schedule. In essence, our algorithm does this by using the state of the river from the previous day. Schedules consist of campsite assignments for each group on the river, as well those waiting to begin their trip. Given knowledge of future waitlists, our algorithm can output schedules months in advance,allowing managementto scheduletheprecisecampsitelocation of any group on any future date. Sparing you the mathematical details, allow us to say simply that our algorithm uses a priority system. It prioritizes groups who are behind schedule by allowing them to move to further campsites, and holds back groups who are ahead of schedule. In this way, it ensures that all trips will be completed in precisely the length of time the passenger had planned for. But scheduling is only part of what our algorithm can do. It can also compute a maximum number of possible trips over the six-month season. We call this the carrying capacity of the river. If we?nd we are below our carrying capacity, our algorithm can tell us how many more groups we could be adding to the water each day. Conversely, if we are experiencing river congestion, we can determine how many fewer groups we should be adding each day to get things running smoothly again. An interesting?nding of our algorithm is how the ratio of oar-powered river rafts to motorized boats affects the number oftrips we can send down- stream. When dealing with an even distribution oftrip durations (from 6to 18 days), we recommend a 1:1 ratio to maximize the river’s carrying capac- ity. If the distribution is skewed towards shorter trip durations, then our model predicts that increasing towards a 4:1 ratio will cause the carrying capacity to increase. Ifthe distribution is skewed the oppositeway,towards longer trip durations, then the carrying capacity of the river will always be246TheUMAP Journal 33.3 (2012) less than in the previous two cases—so this is not recommended. Our algorithm has been thoroughly tested, and we believe that it is a powerful tool for determining the river’s carrying capacity, optimizing daily schedules,and ensuring that people will be able to complete their trip as planned while enjoying a true wilderness experience. Sincerely yours, Team 13955Team members Chip Jackson, Lucas Bourne, and Travis Peters, and team advisor Edoh Amiran.Judges’Commentary247Judges’Commentary: The Outstanding RiverProblem PapersMarie VaniskoDept. of Mathematics, Engineering, and Computer Science Carroll College Helena, MT 59625mvanisko@carroll.eduProblem Overview and General RemarksThis year’s problem dealt with scheduling variable-length river trips down a 225-mile stretch of a particular river, using either oar-powered rubber rafts (at 4mph)or motor boats (at 8mph). A?xed starting point and a?xed ending point were speci?ed for all trips, with campsites distributed fairly uniformly down the river corridor. Minimal contact between groups of visitors was desired, and no two groups could share the same campsite. The goal was to maximize the number of trips over a six-month period, utilizing both types of transportation and allowing for trip lengths of 6 to 18 nights on the river. In addition to the executive summary, teams were required to write a memo to the managers of the river trips, advising them on the optimal scheduling of trips of various lengths over the six-month period, and taking the carrying capacity of the river into account. The teams’ approaches varied greatly, especially regarding the num- ber of campsites available—a factor that had a signi?cant impact on the number of trips that could be scheduled. Many teams found that the “Big Long River” greatly resembled a stretch of the Colorado River in the Grand Canyon, and some used that as a case study for their models. Simulations are available for scheduling trips on that river, but teams had to address all of the issues raised in the problem statement and come up with a solution that demonstrated their own creativity. The judges looked for that and for carefully-explained mathematical model-building with sensitivity analysis that went beyond what is found in the literature.TheUMAPJournal33(3)(2012)247–251. c ?Copyright2012by COMAP,Inc. Allrightsreserved. Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for pro?t or commercial advantage and that copies bear this notice. Abstracting with credit is permitted, but copyrights for components of this work owned by others than COMAP must be honored. To copy otherwise, to republish, to post on servers, or to redistribute to lists requires prior permission from COMAP.248TheUMAP Journal 33.3 (2012)Executive Summary and MemoTheexecutivesummaryisofcriticalimportance,especiallyin earlyjudg- ing. It should?motivate the reader;?be polished, with a good synopsis of key results;?give an overview of the model(s) used, together with the rationale forusing such a model and the primary results obtained from that model; and?state speci?c results obtained for the optimal solution. Teams were also asked to write a memo appropriate for the manager of the Big River boat tours. Whereas the executive summary usually con- tains technical details, this memo was intended for a nontechnical person who wanted to know how best to schedule trips. Hence, the memo was supposed to give speci?cs on how to schedule trips of various types to best accommodate as many groups as possible. Vague generalizations were of little or no value.DocumentationIn comparison with previous contests, the judges were pleased to ob- servea noticeableimprovementin how referenceswereidenti?ed and in the speci?cprecision oftheir documentation. Considering the online resources available, proper documentation was an especially important factor in this year’s problem. Despite the improvement, many papers contained charts and graphs from Web sources with no documentation. All graphs and tables should?have labels and/ or legends;?provide information about what is discussed in the paper;?be “called out” in the text of the paper, so as to refer the reader to them; and?be explained in the text, including their signi?cance. Thebestpapersused graphstohelp clarifytheirresults,and thosepapers also documented trustworthy resources whenever used.AssumptionsTeams made many assumptions about travel along the river. Some were appropriate and played integral roles in the models used;others wereJudges’Commentary249 super?uous. Some teams assumed that there would always be enough customers to?ll any trips scheduled; other teams used probability distri- butions to describe the demand for different trips at different times of the season. Either approach could beused,but each led to different results. The carrying capacity of the river was dependent on the number of campsites available and the types of trips to be scheduled. Since this is a modeling contest, much weight is put on whether or not the model could be used (with modi?cation) in the real world. Therefore, assumptions required for simpli?cation could not be totally unrealistic. Also, clear writing and exposition is essential to motivate and explain as- sumptions, and to derive and test models based on those assumptions.The Model(s)One can arrive at a fairly complete solution to this problem with pencil and paper alone. Problem solvers should at least consider this possibility before launching a simulation! Some teams began with a simple model, then improved it to accommodate the requirements better. Teams should be aware that it is not the quantity of models considered that is important, but rather the quality of the model selected and its applicability to the case at hand. At a minimum, the solutions should try to come up with a mix of trips that seem reasonable. Most teams recognized that for a 225-mile river, a motor boat could run the entire distance in 6 to 8 nights, whereas a raft powered by oars would need 12 to 18 nights. While it is true that requiring only the shortest trip lengths would permit the most boats to get down the river, it was important to consider that not all groups would choose to travel that way. Some teams considered a pro?t incentive when scheduling trips of varying duration on the river and used selected numbers from the Grand Canyon boat trips as a guide. For example, the cost of the trip might be a constant?xed cost plus an amount based on the number of nights on the river. In that case, shorter trips might allow more boats to launch and be optimal in terms of pro?t. Or perhaps it would be more valuable to people to get more time in this pristine wilderness, so they would pay a premium for the longer trips—in which case it might be worth sending fewer boats down the river. Many teams ignored cost/ pro?t issues. Teams assigned campsites so as to ensure that no two sets of campers occupied the same site at the same time. At the end of each night, the teams had to be sure that all crafts camped in reasonable locations and that the model did not require a boat to travel too far in any one day. Many teams measured the percentage of campsites occupied each night as a help in determining an optimalnumber ofcampsites and how good the solution was from a manager’s perspective.250TheUMAP Journal 33.3 (2012) In addition to having no two groups at the same campsite, minimum contact between groups also implies minimizing crafts passing one another on the river. Teams that took this into account showed true diligence. Some teams even measured the average number of such contacts. Although ne- glecting this aspect was not a fatal?aw, proper consideration of the cross- ings gave the model added value.Testing the Model—Simulations and Sensitivity AnalysisMCM teams are getting better at carrying out simulations,and this tech- nique was of great value for the Big River problem. However, to carry out a simulation properly, criteria had to be speci?ed for scheduling trips of varying length. A good?owchart with examples was very powerful in clarifying how a simulation was to be carried out. Some teams used a well- de?ned prioritization scheme that assured that no two groups stayed at the same campsite on any given night and rejected assignments that violated that criterion. Sensitivity analysis was an essential ingredient. The better papers con- sidered how their solution was impacted by changing the number ofcamp- sites and by changing the types of trips. This included varying the ratio of motor boats to oar-powered rafts and varying the ratio of trip durations. The graphical demonstration of the results of such sensitivity analysis was a powerful way to communicate the outcomes and to check for patterns of optimality. Although sensitivityanalysiscould haveincluded issuesassociated with boating accidents, inclement weather, and?ash?oods, most papers only alluded to such possibilities. Few teams considered anything but constant speeds for the river?ow and the boats. Some teams considered extending the hours of travel.Strengths and WeaknessesA strong paper must assess its strengths and its weaknesses. One of the greatest strengths of any model is how well it re?ects the real world situ- ation. Hence, using a case study to validate a model is a powerful means to make that case. Most papers recognized the limitations of their mod- els in failing to consider weather, river, and individual camper issues. A strong solution might mention among weaknessesthat assigning campsites is something of a limitation, because an accident that prevents a boat from reaching its assigned campsite could mess up the model. A more realistic model would say that a given boat will go at most—rather than exactly—nJudges’Commentary251 miles per day; and a?exible model would ensure that a boat could?nd an open campsite if it didn’t make it to its goal campsite.Concluding RemarksMathematical modeling is an art. It is an art that requires considerable skill and practice in order to develop pro?ciency. The big problems that we face now and in the future will be solved in large part by those with the talent, the insight, and the will to model these real-world problems and continuously re?ne those models. The judges are very proud of all participants in this Mathematical Contest in Modeling and we commend you for your hard work and dedication.About the AuthorMarie Vanisko is a Mathematics Professor Emerita from Carroll College in Helena, Montana, where she taught for more than 30 years. She was also a Visiting Professor at the U.S. Military Academy at West Point and taught for?ve years at California State University, Stanislaus. She chairs the Board of Directors at the Montana Learning Center on Canyon Ferry Lake and serves on the Engineering Advisory Board at CarrollCollege. She has been a judge for the MCM for 17 years and for the HiMCM for eight years.252TheUMAP Journal 33.3 (2012)Author’s Commentary253Author’s Commentary: The Outstanding RiverProblem PapersCatherine A. RobertsDept. of Mathematics and Computer Science College of the Holy Cross Worcester MA 01610croberts@holycross.eduThis MCM problem was inspired by a research project for the Grand Canyon NationalPark in Arizona,U.S.A.My collaboratorsand Ideveloped a mathematicalmodel to simulate white-water rafting traf?c along the 225- mile Colorado River corridor within the national park. The National Park Service manages access to the river, guided by a document called the Col- orado River Management Plan (CRMP).This research program began with efforts to revise the 1989 CRMP in the late 1990s. Our model was used as a tool by river managers at the National Park Service to explore options for scheduling rafting traf?c. At the time, every year (almost entirely over the summer months) more than 19,000 people rafted the river on trips guided by 16 licensed commer- cial companies, while approximately 3,500 private boaters paddled them- selvesdown theriver. Demand for accessto theriver far exceeded supply— a waiting list for a private river trip pass had over 7,000 names on it, and a quarter of those people had already waited over a dozen years. The hope was that this mathematical model would provide insight into alternativemanagement scenariosso that park managerscould make smart decisions that would enable as many visitors as possible to enjoy the river, while at the same time maintaining standards for a wilderness experience. Some simpli?cations were built into the MCM Problem, compared to the actual situation on the Colorado River.?The campsites on the Colorado River are not distributed evenly through- out the river corridor. Indeed,there’s a big congestion problem in a reach of the river with few campsites and many popular attraction sites. Some campsites are not suitable for motorized boats.TheUMAPJournal33(3)(2012)253–257. c ?Copyright2012by COMAP,Inc. Allrightsreserved. Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for pro?t or commercial advantage and that copies bear this notice. Abstracting with credit is permitted, but copyrights for components of this work owned by others than COMAP must be honored. To copy otherwise, to republish, to post on servers, or to redistribute to lists requires prior permission from COMAP.254TheUMAP Journal 33.3 (2012)?It is permissible to have more than one group camping at the same site, although theColoradoRiver ManagementPlan dictatesthattheschedule should minimize any camping within sight or sound of another party.?There are two?xed-points on the river corridor—places where passen- gers are exchanged via hiking in-and-out of the canyon or traveling via helicopter. A trip with an exchange must make it to their site at a prede- termined dateand time. Otherwise,therearenoassigned campsites—it’s really impossible to assign a rafting trip a speci?ed set of campsite loca- tions because so much (?ash?oods, boat spills, accidents, health prob- lems) can interfere with a party’s ability to reach a certain location at a ?xed time. Moreover, the river culture is such that assigned campsites would be anathema. The model uses a Geographical Information System (GIS) to divide the river into 90-meter cells. We assigned each cell speci?c attributes (camp- site, lunch spot, dangerous rapid, hiking trail, waterfall, etc.). We used hundreds of trip diaries and personal interviews with river guides to de- termine appropriate weights for the popularity of camping and attraction sites along the river corridor. Trip diaries also helped us estimate the av- erage rate of travel of motor and oar boats through various reaches of the river (when the river corridor narrows, the water’s velocity increases and so boats travel through faster). The model captures the complex dynamics of human visitors interacting with the environment and each other. It is both temporal and spatial as it carefully tracks every move that every trip makes. Our model, titled the Grand Canyon River Trip Simulator (GCRTSim), was programmed in VisualBasic. A user can build any imaginable launch schedule and “run” the season down the virtual river. The results are then analyzed and judged against criteria established by the Park Service. Our model leveraged a number of mathematical theories and ideas.?Intelligentagenttheory: Each trip hasan assigned “personality”and makes all of its decisions consistent with that personality to optimize each day. Thus, a short commercial trip would be less likely to choose a long hike when it needs more time just to paddle down the river. Each trip is an intelligent agent operating within a complex system.?Decision theory: Each trip makes decisions based on a?xed set of choices (e.g., to stop to camp or to continue to the next campsite). The model measures the utility gained from each choice and seeks to maximize the total utility for each trip (e.g., best campsites, key attractions, low crowds).?Game theory: Strategic behavior and bargaining rules come into play as each trip seeks to in?uence the decisions of other trips. For example,can one trip claim a downstream campsite earlier in the day by communi- cating its desire with the other trips that it encounters?Author’s Commentary255?Essentially, the GCRTSim model boils down to aconstrained optimization problem where the success of the entire season depends on individual decisions made by all of the trips, and the outcome depends on the com- bined strategies. For the National Park Service to manage the Grand Canyon rafting season successfully, the sum of all the individual deci- sions over the course of the entire season contributes to an overall utility that must be maximized. The GCRTSim model suggested that the best solution was to expand the rafting season into the shoulder months in the spring and fall. The new CRMP was authorized in 2006, and the new approach to scheduling river trips has been in place since 2008. The number of private launches was dramatically increased without lowering the commercial use. The waiting list was converted to a lottery system that appears to be in favor with the private boaters. Yet, even with more trips being sent down the river each year, the overall crowding at any particular moment was reduced because the trips were spread out over additional months. The number of trips on the river at any one time was reduced from a high of 70 to a high of 60, so the perception of visitors is that the river is less crowded now than it used to be. It is also quieter, since the number of months in which motorized rafts and helicopter exchanges are allowed have been cut in half. A rafter going through the Grand Canyon NationalPark on the Colorado River will enjoy a genuine wilderness experience.Photo Credit: Catherine A. Roberts.256TheUMAP Journal 33.3 (2012)ReferencesBieri, Joanna A., and Catherine A. Roberts. 2000. Using the Grand Canyon River Trip Simulator totestnew launch scheduleson theColoradoRiver. AWIS Magazine29 (3): 6–10.http: //mathcs.holycross.edu/~croberts/publications/AWIS.PDF. Gimblett, R., T.C. Daniel, C.A. Roberts, and M. Ratliff. 1998. Update on river research at the Grand Canyon: Grand Canyon River Trip Simu- lator Project.(ColoradoRiver) Soundings: Newsletter of theColoradoRiver Management Planning Process(May 1998): 1–2. Gimblett, H. Randy, Catherine A. Roberts, Terry C. Daniel, Michael Ratliff, Michael J. Meitner, Susan Cherry, Doug Stallman, Rian Bogle, Robert Allred, Dana Kilbourne, and Joanna Bieri. 2000. An intelligent agent- based modeling for simulating and evaluating river trip scheduling scenarios along the Colorado River in Grand Canyon National Park. InIntegrating GIS and Agent-Based Modeling Techniques for Simulating Social and Ecological Processes, edited by H. Randy Gimblett, 245–275. New York: Oxford University Press.http://mathcs.holycross. edu/~croberts/RESEARCH/GCRTSim/SantaFe.PDF. O’Brien, Gary, and Catherine Roberts. 1999. Evaluation of river beach car- ryingcapacityinformation utilized bytheGrand Canyon RiverTrip Sim- ulator: Analysis and recommendationsfor future study. Grand Canyon Science Center (CA8210-99-002), Final Report.http://mathcs. holycross.edu/~croberts/RESEARCH/Beach/BEACH.PDF. Roberts, Catherine A. 2002a. How can a computer program aid the Col- orado River planning process?The Waiting List: The Grand Canyon Pri- vateBoatersAssociationQuarterly5(4): 6–8.http://mathcs.holycross. edu/~croberts/RESEARCH/GCRTSim/waitinglist.pdf. . 2002b. A computer model for the Colorado River Management Plan.TheRiver Management Society News(Winter 2002): 6–7. . 2007. Environmental mathematical modeling: Grand Canyon. Math Horizons15 (1) (September 2007): 10–13.http://www.maa.org/ mathhorizons/MH-Sep2007_GrandCanyon.pdf. , and Joanna A. Bieri. 2001. Impacts of low?ow rates on recre- ational rafting traf?c on the Colorado River in Grand Canyon National Park. Final Report. Bureau of Reclamation, Grand Canyon Monitoring and Research Center.http://www.gcmrc.gov/library/reports/ cultural/Recreation/roberts2001.pdf. 2008. Summarized inSyn-opses of Studies Completed in Association with theLow Steady Summer Flow Experimental Release from Glen Canyon Dan in WY2000, edited by B.E. Ralston and J.L. Waring, 58–61. Washington, DC: U.S. Department of Interior and U.S. Geological Survey.Author’s Commentary257 Roberts,CatherineA.,and Randy Gimblett. 2001. Computer simulation for rafting traf?c on the Colorado River. InProceedings of the5th Conference of Research on theColoradoPlateau, 19–30. Washington, DC:U.S. Geolog- ical Survey.http://mathcs.holycross.edu/~croberts/RESEARCH/ GCRTSim/USGS.PDF. Roberts,CatherineA.,Doug Stallman,and Joanna A.Bieri. 2002. Modeling complex human-environment interactions: The Grand Canyon river trip simulator.Journal of Ecological Modeling153 (2): 181–196.http:// mathcs.holycross.edu/~croberts/RESEARCH/GCRTSim/EcoMod. pdf.About the AuthorCatherine Roberts is Chair of the Dept. of Mathematics and Computer Science at the College of the Holy Cross and Editor-in-Chief of the journalNatural Resource Modeling. She has an A.B. magna cum laude from Bowdoin College in mathematics and art his- tory and a Ph.D. from Northwestern University in applied mathematics and engineering sciences. She has served on numerous committees of the American Mathematical Society and the Association for Women in Mathe- matics, and she is an Associate Editor of thisJournal.258TheUMAP Journal 33.3 (2012)GiordanoAward Commentary259Judges’Commentary: The Giordano Award forthe RiverProblemMarie VaniskoDept. of Mathematics, Engineering, and Computer Science Carroll College Helena, MT 59625mvanisko@carroll.eduRichard D. WestMathematics Dept. Francis Marion University Florence, SC 29501rwest@fmarion.eduIntroductionFor the?rst time in its history, the MCM is designating a paper with the Frank Giordano Award. This designation goes to a paper that demon- strates a very good exampleofthe modeling process in a problem involving discrete mathematics. Havingworked on thecontestsinceitsinception,FrankGiordanoserved as Contest Director for 20 years. As Frank says: It was my pleasure to work with talented and dedicated profession- als to provide opportunities for students to realize their mathematical creativity and whet their appetites to learn additional mathematics. The enormous amount of positive feedback I have received from par- ticipants and faculty over the years indicates that the contest has made a huge impact on the lives of students and faculty, and also has had an impact on the mathematics curriculum and supporting laborato- ries worldwide. Thanks to all who have made this a rewarding and pleasant experience!TheUMAPJournal33(3)(2012)259–262. c ?Copyright2012by COMAP,Inc. Allrightsreserved. Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for pro?t or commercial advantage and that copies bear this notice. Abstracting with credit is permitted, but copyrights for components of this work owned by others than COMAP must be honored. To copy otherwise, to republish, to post on servers, or to redistribute to lists requires prior permission from COMAP.260TheUMAP Journal 33.3 (2012) The Frank Giordano Award for 2012went to the Outstanding team from Western Washington University (WWU) in Bellingham, WA. This solution paper was characterized by?a high-quality application of the complete modeling process, including assumptions with clear justi?cations, a well-de?ned simulation, a case study, and sensitivity analysis;?originality and creativity in the modeling effort to solve the problem as given; and?clear and concise writing, making it a pleasure to read.The RiverProblemThis year’s problem dealt with scheduling variable-length river tripsdown a 225-mile stretch of a particular river, using either oar-powered rubber rafts (at 4 mph) or motor boats (at 8 mph). Fixed starting and ending points were speci?ed for all trips, with campsites distributed fairly uniformly down the river corridor. Minimal contact between groups of visitors was desired and no two groups could share the same campsite. The goal was to maximize the number of trips over a six-month period utilizing both types of transportation and allowing for trip lengths of 6 to 18 nights on the river. In addition to the executive summary, teams were required to write a memo to the managers of the river trips, advising them of the carrying capacity of the river and how to schedule trips of various lengths over the six-month period. The approaches that teams took varied greatly, especially with regard to the number of campsites available. That factor had a signi?cant impact on the number of trips that could be scheduled. Many teams found that the “Big Long River” greatly resembled a stretch of the Colorado River in the Grand Canyon, and some looked at this as a case study for their models. Simulations for scheduling trips on the Colorado River were available, but teams had to address all the issues raised and come up with a solution that demonstrated their own creativityTheWesternWashingtonUniversityPaperExecutive Summary Sheet and MemoAlthough well written, this team’s one-page sheet at the start was an abstract rather than a one-page executive summary. Typically, an executive summarycontainsmoreinformation (and often moresensitiveinformation) than the abstract does. This team’s one-page summary was too short andGiordanoAward Commentary261 did not state results, but, to the team’s credit, it did motivate the reader to read on. Although it should have contained more speci?cs with regard to the scheduling,the team’s memo, written in an appropriate nontechnical man- ner, was done much better.AssumptionsOne of the?rst things that made this paper stand out from the others was that assumptions were not merely listed but each one was justi?ed. Assumptions were reasonable,and it was noted how the assumptions were to be used in the algorithm. This is something that is most important in the modeling process, but something that is frequently overlooked, so the team is to be commended for their thoroughness in this regard.The Model and MethodsTheteam used a schedulingalgorithm. Thevariableswerewell-de?ned; and it was clear how they arrived at their constraints, based on the stipu- lations stated in the problem. This was one of the few papers that allowed for groups to stay at a camp for more than one night, but that worked well for their algorithm and did not con?ict with the problem statement. Using a very speci?c de?nition for the priority that one group would have over another group, the team was able to assign campsites in a successful man- ner. Interestingly, they started at the end of the river;and using the priority list, they placed the groups in campsites each night. One drawback with their model was that they did not consider crossings ofgroups while on the river.Testing TheirModelsThe?owchart for the team’s scheduling algorithm was clari?ed by the useofexamplesand simulations. Thecasestudy,using data from theGrand Canyon, enabled them to validate their model. They considered many different numbers of campsites, ranging from 50 to 235. With regard to the ratio of the types of boats and lengths of trips, they carried out sensitivity analysis,although they limited their trip lengths to 6, 12,or 18nights on the river. The use of contour maps to demonstrate their results and to observe the “ridge” representing the 1:1 ratio of motor boats to oar-powered rafts was particularly noteworthy.262TheUMAP Journal 33.3 (2012)Recognizing Limitations of the ModelRecognizing the limitations of a model is an important last step in the completion of the modeling process. The students recognized that their algorithm would have to be modi?ed if the river speed were taken into account. They also acknowledged that their carrying capacity for trips might be overestimated and that they had not considered environmental concerns.References and BibliographyThe list of references was fairly thorough, and it was very good to see speci?c documentation of where those references were used in the paper.ConclusionThe careful exposition in the development of the mathematical modelmade this paper one that the judges felt was worthy of the Frank Giordano Award. The team is to be congratulated on their analysis, their clarity, and using the mathematics that they knew to create and justify their own creative model for scheduling camping trips along the Big Long River.About the AuthorsRich Westisa MathematicsProfessor Emeritusfrom FrancisMarion Uni- versity in Florence, South Carolina,where he taught for twelve years. Prior to his time at Francis Marion, he served in the U.S. Army for 30 years, 14 of which were spent teaching at the U.S. Military Academy. He is currently working on a National Science Foundation Grant on freshmen placement tests. He also serves as a Reading Consultant for AP Calculus and as a developmental editor for CLEP (College Level Equivalency Program) Cal- culus Exam. He has judged for both the MCM and HiMCM for over 10 years. Marie Vanisko is a Mathematics Professor Emerita from Carroll College in Helena, Montana, where she taught for more than 30 years. She was also a Visiting Professor at the U.S. Military Academy at West Point and taught for?ve years at California State University, Stanislaus. She chairs the Board of Directors at the Montana Learning Center on Canyon Ferry Lake and serves on the Engineering Advisory Board at CarrollCollege. She has been a judge for the MCM for seventeen years and for the HiMCM for eight years.Results of the2012 ICM263ICM Modeling ForumResults of the 2012 Interdisciplinary Contest in ModelingChris Arney, ICM DirectorDept. of Mathematical Sciences U.S. Military Academy West Point, NY10996david.arney@usma.eduIntroductionIn the 14th Interdisciplinary Contest in Modeling (ICM)R?,1,329teams from six countries spent a weekend in February working on an applied modeling problem involving a criminal network. This year’s contest began on Thursday, February 9, and ended on Monday, February 14, 2012. During that time, teams of up to three undergraduate or high school students researched, modeled, analyzed, solved, wrote, and submitted their solutions to an open-ended inter- disciplinary modeling problem involving a criminalconspiracy network. After theweekend ofchallengingand productivework,thesolution papersweresent to COMAP for judging. Seven of the papers were judged to be Outstanding by the expert panel of judges. COMAP’s Interdisciplinary Contest in Modeling (ICM) involves students working in teams to model and analyze an open interdisciplinary problem. Centeringitseducationalphilosophyon mathematicalmodeling,COMAPsup- ports the use of mathematical tools to explore real-world problems. It serves society by developing students as problem solvers in order to become better informed and prepared ascitizens,contributors,consumers,workers,and com- munity leaders. The ICM is an exampleofCOMAPsefforts in working towards these goals. Thisyear’sproblem waschallengingin itsdemand for teamsto utilizemany aspects of science, mathematics, and analysis in their modeling and problem solving. The problem required teams to investigate the relationships of theTheUMAPJournal33(3)(2012)263–273. c ?Copyright2012by COMAP,Inc. Allrightsreserved. Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for pro?t or commercial advantage and that copies bear this notice. Abstracting with credit is permitted, but copyrights for components of this work owned by others than COMAP must be honored. To copy otherwise, to republish, to post on servers, or to redistribute to lists requires prior permission from COMAP.264TheUMAP Journal 33.3 (2012)members of a criminal conspiracy network within a business organization through social network analysis of their message traf?c. It required teams to understand concepts from the informational and social sciences to build effective network and statistical models to analyze more than 400 messages, categorized into 15 topics, among 83 people. To accomplish their tasks, the students had to consider many dif?cult and complex disciplinary and interdis- ciplinary issues. The problem also included the customary requirements in the ICM to perform thorough analysis and research, exhibit creativity, and demon- strate effective communication. All members of the 1,329 competing teams are to be congratulated for their excellent work and dedication to interdisciplinary modeling and problem solving. Instructions to the teams included:?Your ICM submission should consist of a 1-page Summary Sheet and your solution cannot exceed 20 pages for a maximum of 21 pages.?As modelers, you have to deal with the data you have and through valid assumptions decide what to do with holes, irregularities,redundancies,and errors. Next year, we will continue the network science theme for the contest prob- lem. Teams preparing for the 2013 contest should consider reviewing interdis- ciplinary topics in the areas ofnetwork science and socialnetwork analysis and assemble teams accordingly.The Problem Statement: The Crime-Busting ProblemYour organization, the IntergalacticCrime Modelers (ICM),is investigating a conspiracy to commit a criminal act. The investigators are highly con?dent they know several members of the conspiracy, but hope to identify the other members and the leaders before they make arrests. The conspirators and the possible suspected conspirators all work for the same company in a large of?ce complex. The company has been growing fast and making a name for itself in developing and marketing computer software for banks and credit card com- panies. ICM has recently found a small set of messages from a group of 82 workers in the company that they believe will help them?nd the most likely candidates for the unidenti?ed co-conspirators and unknown leaders. Since the message traf?c is for all the of?ce workers in the company, it is very likely that some (maybe many)ofthe identi?ed communicators in the message traf?c are not involved in the conspiracy. In fact,they are certain that they know some people who are not in the conspiracy. The goal of the modeling effort will be to identify people in the of?ce complex who are the most likely conspirators. A priority list would be ideal so ICM could investigate, place under surveillance, and/ or interrogate the most likely candidates. A discriminate line separatingResults of the2012 ICM265conspirators from non-conspirators would also be helpful to distinctly catego- rize the people in each group. It would also be helpful to the district attorney (DA) if the model nominated the conspiracy leaders. Before the data of the current case are given to your crime modeling team, your supervisor gives you the following scenario (called Investigation EZ)that she worked on a few years ago in another city. Even though she is very proud of her work on the EZ case, she says it is just a very small, simple example, but it may help you understand your task. Her data follow: The 10 people whom she was considering as conspirators were: Anne#, Bob, Carol, Dave*, Ellen, Fred, George*, Harry, Inez, and Jaye# (* indicates prior known conspirators, # indicate prior known non-conspirators). Here is the chronology of the 28 messages that she had for her case, with a code number for the topic of each message that she assigned based on her analysis of the message: Anne to Bob: Why were you late today? (1) Bob to Carol: That darn Anne always watches me. I wasn’t late. (1) Carol to Dave: Anne and Bob are?ghting again over Bob’s tardiness. (1) Dave to Ellen: I need to see you this morning. When can you come by? Bring the budget?les. (2) Dave to Fred: I can come by and see you anytime today. Let me know when it is a good time. Should I bring the budget?les? (2) Dave to George: I will see you later — lots to talk about. I hope the others are ready. It is important to get this right. (3) Harry to George: You seem stressed. What is going on? Our budget will be ?ne. (2) (4) Inez to George: I am real tired today. How are you doing? ( 5) Jaye to Inez: Not much going on today. Wanna go to lunch today? (5) Inez to Jaye: Good thing it is quiet. I am exhausted. Can’t do lunch today — sorry! (5) George to Dave: Time to talk — now! (3) Jaye to Anne: Can you go to lunch today? (5) Dave to George: I can’t. On my way to see Fred. (3) George to Dave: Get here after that. (3) Anne to Carol: Who is supposed to watch Bob? He is goo?ng off all the time. (1) Carol to Anne: Leave him alone. He is working well with George and Dave. (1) George to Dave: This is important. Darn Fred. How about Ellen? (3) Ellen to George: Have you talked with Dave? (3) George to Ellen: Not yet. Did you? (3)266TheUMAP Journal 33.3 (2012)Bob to Anne: I wasn’t late. And just so you know — I am working through lunch. (1) Bob to Dave: Tell them I wasn’t late. You know me. (1) Ellen to Carol: Get with Anne and?gure out the budget meeting schedule for next week and help me calm George. (2) Harry to Dave: Did you notice that George is stressed out again today? (4) Dave to George: Darn Harry thinks you are stressed. Don’t get him worried or he will be nosing around. (4) George to Harry: Just working late and having problems at home. I will be ?ne. (4) Ellen to Harry: Would it be OK, if I miss the meeting today? Fred will be there and he knows the budget better than I do. (2) Harry to Fred: I think next year’s budget is stressing out a few people. Maybe we should take time to reassure people today. (2) (4) Fred to Harry: I think our budget is pretty healthy. I don’t see anything to stress over. (2) END of MESSAGE TRAFFIC Your supervisor points out that she assigned and coded only 5 different topics of messages:?1) Bob’s tardiness,?2) the budget,?3) important unknown issue but assumed to be part of conspiracy,?4) George’s stress, and?5) lunch and other social issues. Asseen in themessagecoding,somemessageshad two topicsassigned because of the content of the messages. The way that your supervisor analyzed her situation was with a network that showed the communication links and the types of messages.Figure 1is a model of the message network that resulted, with the code for the types of messages annotated on the network graph. Your supervisor points out that in addition to known conspirators George and Dave,Ellen and Carolwereindicted fortheconspiracybased on yoursuper- visor’s analysis, and later Bob self-admitted his involvement in a plea bargain for a reduced sentence, but the charges against Carol were later dropped. Your supervisor is still pretty sure that Inez was involved, but the case against her was never established. Your supervisor’s advice to your team is identify the guilty parties so that people like Inez don’t get off, people like Carol are not falsely accused, and ICM gets the credit so people like Bob do not have the opportunity to get reduced sentences.Results of the2012 ICM267Figure 1.Network of messages from EZ Case.The Current CaseYour supervisor has put together a network-like database for the current case,which has the same structure but is a bit larger in scope. The investigators have some indications that a conspiracy is taking place to embezzle funds from the company and use Internet fraud to steal funds from credit cards of people who do business with the company. The small example that she showed you for case EZ had only 10 people (nodes), 27 links (messages), 5 topics, 1 suspi- cious/ conspiracy topic, 2 known conspirators, and 2 known non-conspirators. So far, the new case has 83 nodes, 400 links (some involving more than 1 topic), over 21,000 words of message traf?c, 15 topics (3 have been deemed to be suspicious), 7 known conspirators, and 8 known non-conspirators. These data are given in the attached spreadsheet?les:Names.xls,Topics.xls, andMessages.xls1:?Names.xlscontains the key of node number to the of?ce workers’names.?Topics.xlscontainsthecodefor the15topicnumberstoa shortdescription ofthe topics. Because ofsecurity and privacy issues,your team willnot have direct transcripts of all the message traf?c.?Messages.xlsprovides the links of the nodes that transmitted messages and the topic code numbers that the messages contained. Several messages contained up to three topics.1These?les were available to contestants athttp://www.comap.com/undergraduate/ contests/mcm/contests/2012/problems/2012_ICM.zip.268TheUMAP Journal 33.3 (2012)To help visualize the message traf?c, a network model of the people and message links is provided inFigure 2. In this case, the topics of the messagesare not shown in the?gure as they were inFigure 1. These topic numbers are given in the?leMessages.xlsand described inTopics.xls.Figure 2.Visual of the network model of the 83 people (nodes) and 400 messages between these people (links).Requirements:Requirement 1:So far, it is known that Jean, Alex, Elsie, Paul, Ulf, Yao, and Harvey are conspirators. Also, it is known that Darlene, Tran, Jia, Ellin, Gard, Chris, Paige, and Este are not conspirators. The three known suspicious mes- sage topics are 7, 11, and 13. There is more detail about the topics in?le Top- ics.xls. Build a model and algorithm to prioritize the 83 nodes by likelihood of being part of the conspiracy and explain your model and metrics. Jerome, Delores, and Gretchen are the senior managers of the company. It would beResults of the2012 ICM269very helpful to know if any of them are involved in the conspiracy.Requirement2:How would the priority list change ifnew information comes to light that Topic 1 is also connected to the conspiracy and that Chris is one of the conspirators?Requirement3:A powerfultechnique to obtain and understand text informa- tion similar tothismessagetraf?ciscalled semanticnetworkanalysiswhereasa methodology in arti?cial intelligence and computationallinguistics it provides a structure and process for reasoning about knowledge or language. Another computationallinguisticscapability in naturallanguageprocessing is text anal- ysis. For our crime busting scenario, explain how semantic and text analyses of the content and context of the message traf?c, if you could obtain the orig- inal messages, could empower your team to develop even better models and categorizations of the of?ce personnel. Did you use any of these capabilities on the topic descriptions in?le Topics.xls to enhance your model?Requirement4:Your complete report will eventually go to the DA, so it must be detailed and clearly state your assumptions and methodology;but it cannot exceed 20 pages of write-up. You may include your programs as appendices in separate?les that do not count in your page restriction, but including these programs is not necessary. Your supervisor wants ICM to be the world’s best in solving white-collar,high-tech conspiracy crimesand wantsyour methodology will contribute to solving important cases around the world, especially those with very large databases of message traf?c (thousands of people with tens of thousands of messages and possibly millions of words). She speci?cally asked you to include a discussion on how more thorough network, semantic, and text analyses of the message contents could help with your model and recommendations. As part ofyour report to her, explain the network modeling techniques you have used and why and how they can be used to identify, prioritize, and categorize similar nodes in a network database of any type, not just crimeconspiraciesand messagedata. For instance,could your method?nd the infected or diseased cells in a biological network where you had various kinds of image or chemical data for the nodes indicating infection probabilities and already identi?ed some infected nodes?The ResultsThe 1,329 solution papers were coded at COMAP headquarters so that names and af?liations of the authors were unknown to the judges. Each paper was then read preliminarily by triage judges at the U.S. Military Academy at West Point, NY. At the triage stage, the summary, the model description, and overallorganization are the primary elements in judging a paper. Finaljudging by a team of modelers, analysts, and subject-matter experts took place in late March. The judges classi?ed the 1,329 submitted papers as follows:270TheUMAP Journal 33.3 (2012)Honorable Successful Outstanding Finalist Meritorious Mention Participant Total Crime-Busting 7 4 125 640 553 1,329Outstanding TeamsInstitution and Advisor Team Members“Social Network Analysis in Crime Busting” Northwesteren Polytechnical University Xi’an, China Bingchang Zhou Chen Dong Cunle Qian Jianjun Ma “Message Network Modeling for Crime Busting” Nanjing Univ. of Information Science and Technology Nanjing, Jiangsu, China Guosheng Cheng Yizhou Zhuang Senfeng Liu Liusi Xiao “Crime Busting by an Iterative Two-Phase Propagation Method” Shanghai Jiaotong University Shanghai, China Zulin Sun Xilun Chen Hang Qiu Chunzhi Yang “Finding Conspirators in the Network: Machine Learning with Resource-Allocation Dynamics” Univ. of Electronic Science and Technology of China Chengdu, Sichuan, China Tao Zhou Fangjian Guo Jiang Su Jian Gao “iRank Model: A New Approach to Criminal Network Detection” Mathematical Modeling Innovative Practice Base, Zhuhai College of Jinan University Zhuhai, Guangdong, China Jianwen Xie Yi Zheng Yi Zeng You Tian “Extended Criminal Network Analysis Model Allows Conspirators Nowhere to Hide” Huazhong University of Science and Technology Wuhan, Hebei, China Zhengyang Mei Dekang Zhu Junmin Yang Xiang Chen “Crime Ring Analysis with Electric Networks” Cornell University Ithaca, NY Alexander Vladimirsky Michael Luo Anirvan Mukherjee Myron ZhangResults of the2012 ICM271Awards and ContributionsEach participating ICM advisor and team member received a certi?cate signed by the Contest Director. Additional awards were presented to the team from Cornell University by the Institute for Operations Research and the Man- agement Sciences (INFORMS).JudgingContest DirectorsChris Arney, Dept. of Mathematical Sciences, U.S. Military Academy, West Point, NY Joseph Myers, Computing Sciences Division, Army Research Of?ce, Research Triangle Park, NCAssociateDirectorRodney Sturdivant, Dept. of Mathematical Sciences, U.S. Military Academy, West Point, NYJudgesDimitris Christopoulos, University of the West of England, Bristol,United Kingdom Kathryn Coronges, Dept. of Behavioral Sciences and Leadership, U.S. Military Academy, West Point, NY Kayla de la Haye, RAND Corporation, Santa Monica, CA Tina Hartley, Dept. of Mathematical Sciences, U.S. Military Academy, West Point, NY Brian Macdonald, Dept. of Mathematical Sciences, U.S. Military Academy, West Point, NY Christopher Marcum, RAND Corporation, Santa Monica, CA Robert Ulman, Network Sciences Division, Army Research Of?ce, Research Triangle Park, NCTriageJudgesChris Arney,John Bacon,Jocelyn Bell,Kevin Blaine,Nicholas Clark,Gabe Costa, Michelle Craddock, Kevin Cummiskey, Chris Eastburg, Michael Findlay, James Gatewood, Andy Glen, Tina Hartley, Alex Heidenberg, Steven Horton, Nicholas Howard, John Jackson, Bill Kaczynski, Phil La- Casse, Bill Pulleyblank, Elizabeth Russell, Mick Smith, James Starling, Rodney Sturdivant, Andrew Swedberg, Csilla Szabo, Ben Thirey, Johan Thiel, Chris Weld, and Shaw Yoshitani. —all of Dept. of Mathematical Sciences, U.S. Military Academy, West Point, NY; and272TheUMAP Journal 33.3 (2012)Joseph Myers, Army Research Of?ce, Research Triangle Park, NC Michelle Isenhour, George Mason University, VA Hise Gibson and Chris Farrell, U.S. Army; and Amanda Beecher, Dept. of Mathematics, Ramapo College of New Jersey, Mahwah, NJ.AcknowledgmentsWe thank:?the Institute for Operations Research and the Management Sciences (IN- FORMS)for its support in judging and providing prizes for a winning team, and?all the ICM judges for their valuable and un?agging efforts.CautionsTothereader of research journals:Usually a published paper has been presented to an audience, shown to colleagues, rewritten, checked by referees, revised, and edited by a journal editor. Each of the team papers here is the result of undergraduates working on a problem over a weekend. Editing (and usually substantial cutting) has taken place; minor errors have been corrected, wording has been altered for clarity or economy, and style has been adjusted to that ofThe UMAP Journal. The student authors have proofed the results. Please peruse these students’ efforts in that context.Tothepotential ICM advisor:It might be overpowering to encounter such output from a weekend of work by a small team of undergraduates, but these solution papers are highly atypical. A team that prepares and participates will have an enriching learning experience, independent of what any other team does.Editor’s NoteThe complete roster of participating teams and results has become too longtoreproducein theJournal. Itcan now befound attheCOMAPWebsite:http://www.comap.com/undergraduate/contests/mcm/contests/2012/results/2012_ICM_Results.pdfResults of the2012 ICM273About the AuthorChrisArneygraduated from WestPointand served as an intelligence of?cer in the U.S. Army. His aca- demic studies resumed at Rensselaer Polytechnic In- stitute with an M.S. (computer science) and a Ph.D. (mathematics). He spent most of his 30-year military career as a mathematics professor at West Point, be- fore becoming Dean ofthe SchoolofMathematicsand Sciences and Interim Vice President for AcademicAf- fairs at the College of Saint Rose in Albany, NY. Chris then moved to RTP (Research Triangle Park), NC, where he served for various durations as chair of the Mathematical Sciences Division, of the Network Sciences Di- vision, and of the Information Sciences Directorate of the Army Research Of?ce. Chris has authored 22 books, written more than 120 technical arti- cles, and given more than 250 presentations and 40 workshops. His techni- cal interests include mathematicalmodeling,cooperative systems,pursuit- evasion modeling,robotics, arti?cial intelligence,military operations mod- eling, and network science;his teaching interests include using technology and interdisciplinary problems to improve undergraduate teaching and curricula. He is the founding director of COMAP’s Interdisciplinary Con- test in Modeling (ICM)R?. In August 2009, he rejoined the faculty at West Point as the Network Science Chair and Professor of Mathematics.274TheUMAP Journal 33.3 (2012)Finding Conspirators275Finding Conspirators in the Network via Machine LearningFangjian Guo Jiang Su Jian GaoWeb Sciences Center University of Electronic Science and Technology of China Chengdu, Sichuan, China Advisor: Tao ZhouKey ConceptsMachine learning Logistic regression Semantic diffusion Bipartite graph Resource-allocation dynamics Kendall’s tau Problem Clari?cation:A conspiracy network is embedded in a network of employees of a com- pany, with each edge representing a message sent from one employee (node) to another and catego- rized by topics. Given a few known criminals, a few known non-criminals, and suspicious topics, we seek to estimate the probability of criminal in- volvement for other individuals and to determine the leader of the conspirators. Assumptions?Conspirators and non-conspirators are linearly separable in the space spanned by localfeatures (necessary for machine learning).?A conspirator is reluctant to mention to an out- sider topics related to crime.?Conspirators tend not to talk frequently witheach other about irrelevant topics.?The leader of the conspiracy tries to minimizerisk by restricting direct contacts.?A non-conspirator has no idea of who are con-spirators, hence treats conspirators and non- conspirators equally.TheUMAPJournal33(3)(2012)275–292. c ?Copyright2012by COMAP,Inc. Allrightsreserved. Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for pro?t or commercial advantage and that copies bear this notice. Abstracting with credit is permitted, but copyrights for components of this work owned by others than COMAP must be honored. To copy otherwise, to republish, to post on servers, or to redistribute to lists requires prior permission from COMAP.276TheUMAP Journal 33.3 (2012)Key TechniquesGradient Descent Revised LeaderRank Model Design and Justi?cationFor an unidenti- ?ed node (an employee not identi?ed as a conspir- ator or non-conspirator), we model the probabil- ity of conspiracy as a sigmoid function of a lin- ear combination of the node’s features (logistic re- gression). Those features are formulated from lo- cal topological measures and the node’s semantic messaging patterns. Parameters of the model are trained on a subset of identi?ed conspirators and non-conspirators. The performance of the model is enhanced by discovering potential similarities among topics via topic-word diffusion dynamics on a bipartite graph. We also perform resource- allocation dynamics to identify the leader of the conspirators; the identi?cation is supported by empirical evidence in criminal network research. Strengths and WeaknessesThe combination of topological properties and semantic af?nity among individuals leads to good performance. The time complexity of the algorithm is linear, so the method is suitable for large amounts of data. However, our model requires assistance from se- mantic network analysis to form an expert dictio- nary. Also, intrinsic differences among networks may hinder portability of the model’s features.IntroductionAs shown inFigure 1, criminals and conspirators tend to form organi- zational patterns, interconnected with one another for collaboration, while still maintaining social ties with the outside, thus providing a natural con- text for description and analysis via networks . Criminal networks can be captured from various information, resulting in different types of networks, where each node represents a person, and an edge is present when two nodes collaborate in the same task, share the same family name, or (as in this case) exchange messages . Since the nodes in the graph can be a mixture ofboth criminals and non- criminals, it is desirable to determine suspected criminals from topologi- cal properties of the network and other prior knowledge, which includes known criminals, known non-criminals, and information related to their interactions. Moreover, we desire a priority list of descending criminal likelihood so as to identify the primary leader of the organization.Finding Conspirators277Figure 1.The 83-employee network. Red (darker gray) nodes are known conspirators and the blue (lighter gray) nodes are known non-conspirators.Many authors have adopted centrality measures of the graph for ana- lyzing the characteristics of criminals. Criminals with high betweenness- centrality are usually brokers, while those with high degree-centrality en- joy better pro?t by taking higher risk . Morselli pro- posed that leaders ofa criminal organization tend to balance pro?t and risk by making a careful trade-off between degree-centrality and betweenness- centrality. However, centrality approaches, which utilize local properties, tend to overlook the complex topology of the whole network. Therefore, social network analysis (SNA)methods,including subgroup detection and block- modeling,have been introduced,which try to discover the hidden topolog- ical patterns by partitioning the big network into small closely-connected cliques . Despite the light that they shed on the internal structuresofcriminalnetworks,thesemethodsstillsuffer from intimidating complexity with large databases . We carefully combine the local-feature-based methods with approaches related to global topology of conspiracy networks. We propose a machine learning scheme to leverage local features, so as to estimate each node’s likelihood ofconspiracy involvement. We adopt dynamics-based methods, which are less computationally expensive than most other topology-based approaches, to help identify the lead conspirator and to discover semantic connections between topics. We start with the formulation of useful local features of a node in the network,which then lead to the machinelearning scheme. We feed a subset of known conspirators and non-conspirators as a training samples into the learning algorithm. We then use the algorithm to estimate the probability of being a conspirator for every other individual in the network.278TheUMAP Journal 33.3 (2012) Since highly suspicious topics are essential to the performance of ma- chine learning, we then try to discover similarities between topics, by per- forming simple source-allocation dynamics on the bipartite semantic net- work made up of topics and sensitive words. Those?ndings expand our knowledge on suspicious topics, thus enhancing the accuracy of our ma- chine learning model. To?nd the leader of the conspirators,we apply a dynamics-based rank- ing algorithm on a subgraph extracted from the network. Our?ndings are in agreement with empirical knowledge about the centrality balance of criminal leaders. Finally, we perform sensitivity analysis to test the robustness of our approach.A Machine Learning SolutionWe use machine learning mainly because of its adaptiveness and reor- ganization, which simulate humans’actions to obtain fresh knowledge. We describe the construction of our machine learning framework in detail, including feature formulation, core learning methods, and experi- mental results. Through statistical analysis on the results, we propose an enhancement based on semantic diffusion. We commence with several necessary assumptions:?All the data and information about the EZ case network and the 83-node network are relatively stable over a long period.?The contents of the communication among conspirators tends to be rel- evant about suspicious topics or some formal issues, rather than gossip.?The two networks feature similar core mechanisms for communication transmission.Feature formulation?CentralityWe exploit three types of centrality—degree centrality, betweenness centrality, and closeness centrality—to determine the center of the sus- picious network from different aspects:?Degreecentrality.Degree centrality indicates active- ness of a member, and a member who tends to have more links to others may be the leader. However, as explained in Xu and Chen , degree centrality is not quite reliable to indicate the team leader in a criminal network. For a graphG(V,E), the normalized degree centrality of nodeiisFinding Conspirators279CD(i) = ?|V| j=1ν(i,j) |V| ?1 , i?=j,(1) whereνis a binary indicator showing whether there exists a link be- tween twonodes. Sinceour graph isdirected,wecalculateseparately the in-degree and out-degree of every node.?Betweenness centrality.Betweenness centrality de- scribes how much a node tends to be on the shortest path between other nodes. A node with large betweenness centrality does not nec- essarily have large degree but illustrates the role of “gatekeeper”— someone who is more likely to be a intermediary when two other members exchange information. The normalized betweenness cen- trality isCB(i) = ?|V| j=1?|V|kjωj,k(i) |V| ?1 , k?=i,(2) whereωj,k(i)indicates whether the shortest path between nodejand nodekpasses through nodei.?Closeness centrality.Closeness centrality is usually utilized to measure how far away one node is from the others. Close- ness of a node is de?ned as the inverse of the sum of its distances to all other nodes and can be treated as a measure of ef?ciency when spreading information from itself to all other nodes sequentially. It indicates how easily an individual connects with other members. The normalized closeness centrality isCc(i) = ?|V| j=1ρ(i,j)?CcminCcmax?Ccmin, i?=j,(3) whereρ(i,j)is the length ofthe shortest path connecting nodesiandj.CcminandCcmaxare the minimum and maximum lengths of the shortest paths.f?Numberof known neighboring conspiratorsWe consider as a signi?cant feature the number of known neighboring conspirators of a node. The interaction among conspirators in a mes- sage network suggests a much stronger connectivity than that among non-conspirators: A conspirator is more likely to communicate with an accomplice. As shown inFigure 2, we calculate the ratio of known con- spirators among one’s adjacent neighbors, which measures proximity with known accomplices: The value is 1 if the individual connects with all the known conspirators, and 0 means that no conspirators connect to theindividual. Theknown suspiciouscliqueobviouslyrepresentsamore compact connectivity. Therefore, the more known conspirators among280TheUMAP Journal 33.3 (2012) an individual’s neighbors, the greater the possibility that the individual is an accomplice.30 1 00 00.25 0.50.75 0.25 0.5 0.75Known non-conspirators Known conspiratorsHarvey Elsie Alex Yao Ulf Paul Jean Chris Paige Derlene Gard Ellin Tran Este JiaFigure 2.Ratio of known conspirators among adjacent neighbors. To avoid the overlapping of names with a linear scale, we adopt a topographic map type of diagram, with a peak at the center and symmetric contour circles around it. The closer a person is to the center, the more likely that the person is a conspirator.?Numberof currentnon-suspicious messages from known conspirators Table 1shows the topics mentioned between known conspirators.1Aknown conspirator rarely talks with accomplices about topics irrelevant to their conspiracy, though a very small proportion of unknown topics appear. If most of the information received from a known conspirator is irrelevant, the receiver is probably not a conspirator.Table 1. Topics among known conspirators. Known conspiratorial topics have an asterisk and are highlighted in blue (light gray). Jean Alex Elsie Poul Ulf Yao Harvey Jean11*8 14 Alex1 13*11*3,7* Elsie11*13* Poul11*7*7*4 Ulf7*,11*,13*13* Yao13*7*,11*,13*7*,9 13*2,7* Harvey13*1Topic 16 in the raw data is regarded as wrong and thus discarded.Finding Conspirators281MethodsWe use logistic regression to model the probability of a node being in- volved in the conspiracy. We obtain the parameters ofthe model by using a gradient descent algorithm to solve an optimization problem on a training set.Logistic RegressionWe consider a training set{(x(i),y(i))}of sizem, wherex(i)is ann- dimensional feature vector andy(i)indicates the classi?cation of the node, i.e.,y(i)= 1for conspirators andy(i)= 0for non-conspirators. The nodesin the training set are drawn from the 15 known conspirators and non- conspirators. As a specialization of a generalized linear model for Bernoulli distri- bution, logistic regression estimates the probability of being a conspirator asP(y= 1|x;θ) =hθ(x) = 1 1 +e?θTx,(4) whereθ∈Rnis the parameter vector. Under the framework of the generalized linear model, themaximum a posteriori(MAP) estimate of the parameterθisminθJ(θ),(5) where the cost function is given byJ(θ) = 1 mm?i=1? ?y(i)log ? hθ(x)(i)? ? ? 1?y(i)?log ? 1?hθ(x(i)) ?? + λ 2mn?j=1θ2 j,(6) withλbeing a regularization parameter.Gradient DescentThe cost functionJ(θ)is minimized by gradient descent, which drivesθdown the locally steepest slope, in hope of reaching the global minimum of the cost function. At every iteration before convergence, a newθreplaces the oldθviaθ:=θ?α?θJ(θ),(7) whereαis a small positive constant.282TheUMAP Journal 33.3 (2012)Leave-One-Out Cross ValidationSince we are informed of the correct classi?cation of onlyN0nodes (N0= 15in our case), in a given round we only use (N0?1) of them asthe training set, while leaving one out for cross validation (C-V). At every round, the next correctly classi?ed node is left out and the others serve as the training set; then the trained hypothesis is tested on the left-out node. In this way, by averagingN0rounds without overlapping, the errors for both the training set and the cross validation set can be evaluated. Suppose,for example,that in thej-th round sample(x(j),y(j))is left out and the training set is given bySj={(x(l),y(l))|l= 1,2,···,j?1,j+ 1,···,N0}.(8) Using this training set, parameter vectorθ(j)is obtained, and the corre- sponding hypothesis is tested on bothSjand the left-out(x(j),y(j)), ob- taining this round’s training errorεSjand C-V errorεj.Hence, by averaging overj, the training error and C-V error areεS= 1 N0 N0?j=1εSj, ε= 1 N0 N0?j=1εj.(9)Setting the Regularization ParameterThe regularization parameterλ 0is selected to minimize the cross validation error, i.e.,λ= argminλ0ε.(10)ResultsBy training the logistic regression model with our leave-one-out cross validation strategy,λis optimally set to1.9and the overall C-V error isε= 0.27(with training errorεS= 0). Then, while?xing the chosenλ, we retrain the hypothesis on the maximum training set, making full use of known conspirators and non-conspirators.Table 2. Top 10 in the priority list (known conspirators excluded). Name Dolores* Crystal Jerome* Sherri Neal Christina Jerome William Dwight Beth Node No. 10 20 34 3 17 47 16 50 28 38 Probability of conspiracy .56 .51 .39 .32 .30 .27 .25 .25 .24 .23Finding Conspirators283 Thetrained hypothesisgivestheestimated probability for nodeibeing a conspirator, resulting in a priority list of suspects, ranked in descent order of criminal likelihood. The top 10 suspects are shown inTable 2, with managers marked by an asterisk. Figure 3illustrates the probability of criminal involvement estimated byhθ(x)versus the corresponding rank in the priority list, where three managers (Jerome, Dolores, and Gretchen)2are marked by circles.Dolores (manager) is indeed the person deserving highest suspicion, and Jerome (manager) is also likely to be involved in conspiracy.10 20 30 40 50 60 70 80 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1Rank in the priority listProbability of being criminal All the members Gretchen (manager) Jerome (manager) Dolores (manager)Figure 3.Probability of conspiracy vs. corresponding rank in the priority listSemantic Model EnhancementSemanticinformation ismoreimportanttohumansthan thecomplicated topology structure. For example,similar text information in their messages motivates us to conclude in the EZ case that Inez is similar to George, who is de?nitely a conspirator (for instance,“tired” when describing Inez and “stressed” when describing George). Similar cases can be also found in the 83-people network case: The word “Spanish” from known conspiratorial topic7ishighly suspiciousand appearsrepeatedlyin other unknown topics (e.g., topic 2 and 12). The contents about “computer security,” which is2Since several nodes are named either Gretchen or Jerome, we select those with bigger out- degrees to be managers, that is, Node 32 is Gretchen (manager) and Node 34 is Jerome (manager).284TheUMAP Journal 33.3 (2012) treated as part of the key in the whole conspiracy, is also active in many other unknown topics, such as 5 and 15. Hence, it is natural to train a computer to measure similarities among topics so as to reveal potential information.Conspiratorial Message 1 Conspiratorial Message 2 Conspiratorial Message x suspicious word 3 suspicious word n suspicious word 1 suspicious word 2 Conspiratorial Dictionary New conspiratorial Message New non-conspiratorial Message Undetermined Message 1 Undetermined Message 2 Undetermined Message 3 Undetermined Message 4 Undetermined Message m Legend:Figure 4.Framework of topic semantic diffusion.Lexicalambiguity exists widely among words, which can have different meanings depending on context. So it is not wise to abandon human intel- ligence and depend only on algorithms. Therefore,we draw the problem of topic semantic diffusion into a topic-similarity measurement task based on an expert dictionary. We form the bipartite network illustrated inFigure 4, between the conspiratorial dictionary constructed from the conspiratorial messages about known suspicious topics, and all of the information in the message traf?c. We exploit a resource allocation mechanism to extract the hidden information ofnetworks and unfold the similarity among different topics. The bipartite network is modeled as the bipartite graphG= (D,T,E), where?D={di}is the dictionary of suspicious words, shown in the middlecolumn inFigure 4;?T={tl}is the message set, which is divided into two categories: –messages with known status (left column inFigure 4), and –undetermined messages (right column inFigure 4);?Eis an edge set, with(di,tl)∈Eindicating that worddiin the conspir- atorial dictionaryDoccurred in messagetlof the message setT; Weinitially give1unit ofresourceto each known conspiratorialmessage inTand 0 to the remaining messages. The process of semantic diffusionFinding Conspirators285 includes two steps, namely the redistribution of resource from message topics to keywords, and that from keywords back to topics. We commence with the?rst allocation from setTto setD:f(di) =n?i=1ailf(tl) K(tl) .(11) Equation(11)expresses the calculation of the resource held bytlafter the ?rst step, whereK(tl)denotes the degree of the nodetl,f(x)denotes theresource carried byx, andailis de?ned asail= ? 1,(di,tl)∈E; 0,otherwise.(12) Theintuitiveexplanation ofStep 1is simply theprocessofredistributing resource fromTtoD, with the transferred amount regulated by the degree of nodes inT.This is followed by Step 2, which is to re?ect the resource?ow back toTfromDobeying the same rule but in the inverse direction, as shown fromthe middle column to the right column inFigure 4. So the resource?nally locates ontiand satis?esf?(ti) =m?l=1ailf(dl) K(dl) =m?l=1ailK(dl)n?j=1ajif(tj) K(tj) .(13) After this two-fold process, the amount of resource held by every element inTcan be interpreted as a score ofsimilarity. The rank ofa topicaccording to its score represents the degree of its similarity to the information in the dictionary—thatis,thehigher thescore,themore likely thetopicis a newly- found conspiratorial topic. We setD={’Spanish’, ’system’, ’network’, ’computer’, ’meeting’}as the conspiratorial dictionary, andTable 3illustrates the?nal result for all 15topics in the 83-people network case. The known suspicious topics are 7, 11, and 13. They are naturally the top three, and topic 5 is more suspicious than other unknown topics. Topics 2, 12, and 15 are among the group with the second highest possibility in unknowns;and the remaining topics tend to be irrelevant to the conspiracy. We then append topic 5 to the set of known conspiratorial topics and train the model again; the overall C-V error decreases from 0.27 to 0.13. Since Since topics 2, 12, and 15 are less similar to known suspicious topics, as shown inTable 3, appending them to model training does not evidently in?uence the correctness. The enhanced correctness here indicate that with enough keywords in the conspiratorial dictionary and enough topics with abundant contents, such a method is likely to perform very well. On the other hand, if we utilize the speaker instead of the keywords to construct a bipartite graph with the topics, we will also get similarity286TheUMAP Journal 33.3 (2012)Table 1. Rank of all topics based on similarity to known suspicious topics (known conspiratorial topics have an asterisk and are highlighted in blue). Rank Topic Number Similarity to known suspicious topics 111* 0.750 27* 0.667 313* 0.667 4 5 0.417 5 2 0.167 6 12 0.167 7 15 0.167 8 1,3,4,6,8,9,10,14 0among topics based on the transmitting speaker. However, the determina- tion ofthe relationship between differing results under these two standards is de?nitely beyond this paper. The resource allocation method is also highly ef?cient: Its time com- plexity of computation is linear in the number of edges of the graph, which enables good performance with huge amounts of data.Identifying the Leaderof the ConspiracyOur machine learning scheme tries to estimate the likelihood of a node committing conspiracy. However, the likelihood does not proportionally indicate leadership inside the network,because the identi?cation ofleaders is further complicated by the network’s topology. We adopt LeaderRank, a node-ranking algorithm closely related to net- work topology,to?nd theleader. Weextractfrom thenetworkthesubgraph connected by known suspicious topics. Because of its robustness against random noise, LeaderRank is appropriate for addressing criminal network problems, which usually suffer from incompleteness and incorrectness.LeaderRankThe LeaderRank algorithm is a state-of-the-art achievement on node rankingthatismoretolerantofnoisy data and robustagainstmanipulations than traditionalalgorithmssuch asHITSand PageRank . This method is mathematically equivalent to a random-walk mechanism on the directed network with adaptive probability, leading to a parameter-free algorithm readily applicable to any type of graph. We insert a ground node, which connects with every node through newly-added bidirectionallinks,in ordertomaketheentirenetworkstrongly connected, so that the random walk will de?nitely converge.Finding Conspirators287 For a graphG= (V,E), every node in the graph obtains 1 unit of re- source except the ground node. After the beginning of the voting process, nodeiat steptwillget an adaptivevoting scoreν(t)according to the voting from its neighbors:νi(t+ 1) =|V|+1?j=1μijDout(j) νi(t),(14) whereμijis a binary indicator with value 1 if nodeipoints tojand 0 otherwise.Dout(j)denotes the out-degree of nodej. The quotient of theabove two arguments could be considered as the probability that a random walker atigoes tojin the next step. Finally, the leadership score of nodeiisνi(Tc) +νgn(Tc)/|V|, whereνgn(Tc)is the score of the ground node at steady state.Suspicious Topic Subnetwork ExtractionWe extract from the network of company employees the subnetworkGTSconnected by suspicious topics only, so as to minimize the coupling ofthe company’s hierarchical structure to the conspiracy relations. Suppose thatTijdenotes the set of topics mentioned by messages from nodeito nodej, andTSdenotes the set of known suspicious topics (TS={7,11,13}). ThenGTSis the maximum subgraph of the original graphG,whereasTij?TS,for all(i,j)?ETS.(15)Edge ReverseTheoriginalLeaderRankalgorithm dealswith?ndingleadersin Internetsocial networks, where the direction of an edge has a dissimilar meaning from our case: If A points to (follows) B in Twitter, then B is considered to be a leader of A. However, in our communication network, an edge from A to Bsuggests that A has sent Ba message. Therefore,assuming that a leader in a criminal network tends to be a sender rather than a receiver, each edge inGTShas to be reversed to be compatible with LeaderRank’s design. We denote byG? TSthe reversed subnetwork induced by suspicious topics.ResultsBy running LeaderRank onG?TS, a ranking score is assigned to every node in this subgraph, which generates a list of possible leaders ranked in descent order, as shown inTable 4. Yao (node number 67) is ranked as the chief leader of the conspiracy.288TheUMAP Journal 33.3 (2012)Table 4. Partial results of LeaderRank onG? TS. Name LeaderRank score Yao 2.67 Alex 2.21 Paul 1.92 Elsie 1.62Empirical SupportEmpirical analysis of criminal networks?nds that a leader of a criminal organization tends to carefully balance degree-centrality and betweenness- centrality. It has been proposed that the leader usually maintains a high betweenness-centralitybut a relatively low degree-centrality,for enhancing ef?ciency while ensuring safety .Figure 5.The joint distribution of betweenness centrality and degree centrality. Yao is at the lower right.Figure 5illustrates the joint distribution of betweenness centralityCBand degree centrality (Din+Dout) for the 7 known conspirators and 10 other nodes with high conspiracy likelihood,where two dashed lines mark average values of the displayed nodes. Yao’s high betweenness-centrality with relatively low degree-centrality accord with the identity of a leader. Our conclusion that Yao is the leader is thus empirically supported.Finding Conspirators289DiscussionWeidentifytheleader ofthecriminalnetworkby performingtheLeader- Rankalgorithm on theextracted,edge-reversed,suspicious-topic-connected subgraph;and our?ndings are strengthened by empirical research results.Evaluating the ModelSensitivity AnalysisConsidering the usual incompleteness, imprecision, and even inconsis- tency in mapping criminalsocialnetworks ,the method for deducing criminality should be robust enough to cope with minor al- ternations of the network. Otherwise,there could be mistaken accusations. Therefore, we perform a sensitivity analysis on our approach. Requirement 2 of the problem statement provides an appropriate sce- nario for such a test: While other conditions remain unchanged, new in- formation indicates that Topic 1 is also connected to criminal activity, and Chris, who was considered innocent before, is now proven guilty.Priority ListBy applyingour methodstothesealtered conditions,we?nd thatamong the top 10 of the previous priority list (the 7 known conspirators excluded), 7 of them are still in the new top 10, while the remaining 3?nd their new places at 12th, 14th, and 16th. A more sophisticated measurement of the sensitivity of the priority list isKendall’s taucoef?cientτ . Given two priority lists{pk}= {p1,p2,···,pn}and{qk}={q1,q2,···,qn}—for example,p2= 5meansnode 2 is ranked 5th in the{pk}list—then?(i,j)(fori?=j) is aconcordant pairif their relative rankings agree in thetwo lists, i.e.,pi pjandqi qj, orpi pjandqi qj;?otherwise,ifpi pjbutqi qj,orpi pjbutqi qj(i,j)is adiscordantpair. Kendall’s tauis de?ned asτ=(number of concordant pairs)?(number of discordant pairs)1 2n(n?1) ,(16) which lies in , with1for perfect ranking agreement and?1for utter disagreement. The value ofKendall’s taufor the two priority lists of Requirement 1 and Requirement 2isτ= 0.86,justifying therobustnessofthemachinelearning approach.290TheUMAP Journal 33.3 (2012) Let us assume that known conspirators and non-conspirators are inde- pendently wrongly classi?ed with the same speci?c probability.Figure 6 depicts the expected Kendall’s tau vs.the misclassi?cation probability, sep- arately for conspirators and non-conspirators. Even if the misclassi?cation probability is as high as 0.5, Kendall’s tau does not drop below 0.8, sub- stantially proving the inherent stability of our methods.? ???? ??? ???? ??? ???? ??? ???? ??? ???? ??? ??? ???? ???? ???? ???? ??? ???? ???? ???? ???? ?????????????????????????????????????????????????? ????????????? ????????????????Figure 6.The expected Kendall’s tau declines as misclassi?cation probability increases.Probability In?ationFigure 7illustrates the change of estimated conspiracy probability due to the modi?ed conditions of Requirement 2, with the previous value asx-axis,and thenew asy-axis. Generally,mostnodesexhibita small“in?ation”in criminal probability, as indicated by the distribution of dots skewed from the diagonal line. The augmented probability is compatible with the new information that expands both the set of suspicious topics and known conspirators. The analysis suggests that our machine learning method is insensitive to minor alterations and can still produce reasonable results with new in- formation.ReferencesBaker, Wayne E., and Robert R. Faulkner. 1993. The social organization of conspiracy: Illegal networks in the heavy electrical equipment industry.Finding Conspirators2910 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1Probability of being a conspirator (Requirement 1)Probability of being a conspirator (Requirement 2 ) Training set Unknown nodes Chris Gretchen (manager) Jerome (manager) Dolores (manager)Figure 7.Criminal probabilities before and after the change of conditions.American Sociological Review58 (6) (December 1993): 837–860.http:// webuser.bus.umich.edu/wayneb/pdfs/networks/Conspiracy.pdf. Chen, Hao, and Burt M. Sharp. 2004. 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Leaders in social networks, thedeliciouscase.PloS ONE, 6 (6):292TheUMAP Journal 33.3 (2012) e21202.http://www.plosone.org/article/info:Adoi/10.1371/ journal.pone.0021202,doi:10.1371/journal.pone.0021202. Morselli, Carlo. 2010. Assessing vulnerable and strategic positions in a criminal network.Journalof Contemporary CriminalJustice26 (4) (Septem- ber 2010): 382–392.http://ccj.sagepub.com/content/26/4/382. short,doi:10.1177/1043986210377105. Sabidussi, Gert. 1966. The centrality index of a graph.Psychometrika31 (4): 581–603. Sen, Kumar Pranab. 1968. Estimates of the regression coef?cient based on Kendall’s tau.Journal of theAmerican Statistical Association63 (December 1968): 1379–1389. Wheat, Christopher. 2007. Algorithmic complexity and structural mod- els ofsocialnetworks.http://scripts.mit.edu/~cwheat/research/ modelsel.20070416. Xu, Jennifer, and Hsinchun Chen. 2003. Untangling criminal networks: A case study. InIntelligence and Security Informatics: Lecture Notes in Com- puter Science2665, edited by G. Goos, J. Hartmanis, and J. van Leeuwen, 232–248. New York: Springer, 2003. . 2005. Criminal network analysis and visualization.Communica- tionsoftheAssociation forComputingMachinery48(6)(June2005): 100–107. Zhou, Tao, Jie Ren, Mat′uˇs Medo, and Yi-Cheng Zhang. 2007. Bipar- tite network projection and personal recommendation.Physical Re- view E76 (4): 046115.http://doc.rero.ch/lm.php?url=1000,43, 2,20071213113651-JT/zhang_bnp.pdf.Jiang Su, Jian Gao, Tao Zhou (advisor), and Fangjian Guo.Judges’Commentary293Judges’Commentary: Modeling forCrime BustingChris ArneyDept. of Mathematical Sciences U.S. Military Academy West Point, NY10996david.arney@usma.eduKathryn CorongesDept. of Behavioral Sciences and Leadership U.S. Military Academy West Point, NY10996IntroductionThe new topicarea for this year’s Interdisciplinary Contest in Modeling (ICM) was network science. The shift was popular with the student teams, sincea record 1,329teamssubmitted papersin solution to a “crime-busting” problem. Network science and/ or social network analysis will continue to be the topic area for next year’s problem as well. So, for teams that enjoyed this year’s problem or want to prepare early for next year’s contest,prepare by studying network modeling and assemble a team with that subject in mind. The ICM continues to be an opportunity for teams of students to tackle challenging, real-world problems that require a wide breadth of under- standing in multiple academicsubjects. These elements are practically part of the de?nition of network science—an emerging subject that blends con- cepts, theories, structures, processes, and applications from mathematics, computer science, operations research, sociology, information science, and several other?elds. ICM problems are often open-ended and challenging. Some, like the one this year, could be termed “wicked,” in that there is not one correct answer nor a set or established method to model such a problem.TheUMAPJournal33(3)(2012)293–303. c ?Copyright2012by COMAP,Inc. Allrightsreserved. Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for pro?t or commercial advantage and that copies bear this notice. Abstracting with credit is permitted, but copyrights for components of this work owned by others than COMAP must be honored. To copy otherwise, to republish, to post on servers, or to redistribute to lists requires prior permission from COMAP.294TheUMAP Journal 33.3 (2012) ThecomplexnatureoftheICM problemsand theshort timelimit require effective communication and coordination of effort among team members. One of the most challenging issues for the team is how to best organize and collaborate to use each team member’s skills and talents. Teams that solve this organizationalchallenge often submit solutions that can rise to the?nal rounds of judging.The Criminal Network Analysis ProblemThe Information Age, along with its information-laden and highly- linked Internet,has brought us many amazing capabilities,along with new ways to commit crimes. This year’s problem focused on potential conspir- ators within a company’s communication network plotting to commit a crime. Some people were already identi?ed either as known conspirators or as known non-conspirators. The goal of the model was to identify the most likely conspirators from the remaining people in the network through the analysis of con?scated and categorized message traf?c. The many con- nections and links between the people and the messages made this an es- pecially appropriate topic for network modeling. The main tasks expected of the students were to:?Requirement 1:Build a model to prioritize the 83 people by likelihood of being part of the conspiracy and explain your model and metrics. Are any senior managers of the company involved in the conspiracy??Requirement 2:As new information comes to light, use your model to analyze this changing situation. A good network model is?exible and able to handle the changing nature, structure and information in a dynamic network setting.?Requirement 3:If you could obtain the original messages, explain how semanticand text analyses ofthe message traf?ccould help you develop even better models.?Requirement 4:Explain the network modeling techniques you devel- oped and how they can be used to identify, prioritize, and categorize nodes in a network involving other kinds of data sources, not just crime and message data. Does your model generalize to other important prob- lems in society? Again,this is the mark ofstrong models within network science and their potential to impact society.Judges’CriteriaThe panelofexpert judges were impressed both by the strength ofmany of the submissions of individual teams, and fascinated by the variety ofJudges’Commentary295 innovative approaches that students used to address the issues, challenges, and questions that were posed by the problem. The papers were rich in modelingmethodologyand creativity. In order toensurethattheindividual judges assessed submissions on the same criteria, a rubric was developed. The framework used to evaluate submissions is described below.Executive SummaryIt was important that studentssuccinctly and clearly explained the high- lights of their submissions. The executive summary should contain brief descriptions of both the modeling approach and the bottom-line results. The remaining report provides a more detailed statement of the contents of the executive summary. One mark of an Outstanding paper is a summary with a well-connected and concise description of the approach used, the results obtained and any recommendations.ModelingModels and measures were needed to classify the people in the organi- zation to identify conspirators. Many teams used probability or likelihood measures for criminal-like behavior of the people within the context of the known data. Other used decision-making criteria as their basic modeling framework. Some teams used the explicit structures of networks or graphs to determine classic local or global network metrics, properties, node clus- ters, or performance outcomes. For such a structure, critical assumptions, such as thedirectionality ofin?uenceand connection within thegraph,lead to viable network models. Other teams ignored some of the aspects of the network structure and performed data mining, element classi?cation, and discrimination. Those teams often found prioritization and ordering easier than discrimination. Where to draw the line and commit to predict a conspirator was some- timesdif?cult. Nomatterthemodelingframework,theassumptionsneeded for these models and the careful and appropriate development of these models were important in evaluating the quality of the solutions. The better submissions explicitly discussed why key assumptions were made and how assumptions affected the model development. Stronger submis- sions presented a balanced mix of mathematics and prose rather than a series of equations and parameter values without explanation. One major discriminator was the use or misuse of arbitrary parameters without any explanation or analysis. Establishing and explaining parameter values in models are at least as signi?cant as making and validating assumptions.296TheUMAP Journal 33.3 (2012)ScienceSemantic and text analysis are elements of the science of computational linguistics or natural language processing involving many challenging sci- enti?cand technologicalissuesrelated to the nature,valueand understand- ing of information and the production of knowledge or intelligence. Cur- rently, many information-rich systems and organizations are facing data deluge and overload. Vast amounts of unstructured textual data are often collected and held for practically impossible human analysis. The magni- tude ofdata makes this potentially valuable information at best a worthless distraction. Through natural language processing using semantic and text analysis the potentially valuable but hidden information can become visi- ble, understandable, organized, and useful. The ultimate goals of semantic and text analysis are to identify con- text, meaning, categorization, and entity attributes, and thereby produce human-ready synopses and standardized, interconnected, structured data (information networks). These highly sophisticated and complexprocesses are exactly what would be needed to model and solve this network con- spiracy problem. Some teams did effective research and insightful analysis that tackled the complexity of the problem and included elements of text or semantic analysis in their model or described how their model could ac- commodate such capability had the raw message data been available. No matter what modeling was performed by the teams, the interdisciplinary nature of this problem was fully revealed in this requirement. These areas of information science and analytics will experience signi?cant scienti?c and technological improvements in the future, and the ICM teams were exposed to this developing?eld in the context of their interdisciplinary science research.Data/Validity/SensitivityOnce the model was created, the use of test data and checks on the accuracy and robustness of the solution help to build con?dence in the modeling approach. Sensitivity analysis of models to determine the effects of changing data and errors can often be more meaningful than speci?c output values. This is especially true for highly-structured and powerful data-rich models like networks. Some network structures are highly robust and?exible while others are fragile and highly sensitive to data. While this is a challenging element of network modeling, it was important to address this issue in the report.Strengths/WeaknessesA discussion of the strengths and weaknesses of the models is often wherestudentsdemonstratetheir understandingofwhattheyhavecreated.Judges’Commentary297 The ability of a team to make useful recommendations fades quickly if team members do not understand the limitations or constraints of their assumptions or the implications oftheir modeling methodology. Networks are complex structures and, therefore, the strengths and weaknesses are often hidden from direct view or control of the modeler. Again, the better teams were able to discuss these elements despite these challenges.Communication/Visuals/ChartsTo clearly explain solutions, teams must use multiple modes of expres- sion including diagrams and graphs, and, in the case of this competition, English. A solution that could not be understood did not progress to the ?nal rounds of judging. The judges were delighted by the amazing array ofpowerfulcharts and graphs that explained both models and results.Fig- ures 1–3on the this page and the next are intended as samples to show the richness of this kind of graphical analysis and reporting.Figure 1.Teamsprovided informativegraphicschematicsto show therelationship and connections uncovered by their models. This graphic is from Team 12460 from Harbin Institute of Technology in Harbin, Heilongjiang, China.RecommendationsTeams were speci?cally asked to discuss their conspiratorial priorities and thepotentialinvolvementofsenior managersin their report that would be read by the district attorney. The ability of teams to evaluate the results of their analysis and make recommendations was important in identifying strong submissions.298TheUMAP Journal 33.3 (2012)Figure 2.This network portrayal vividly showing the likelihood of conspirators is from Team 16075 from Huazhong University of Science and Technology in Wuhan, Hubei, China.?Figure 3.Teams that performed data analysis often used probability charts like this one from Team13104 from Southeast University, Jiulonghu Campus, Nanjin, Jiangsu, China, to demonstrate their results.Judges’Commentary299Discussion of the Outstanding PapersAs you will discover in this section, many different approaches were used by ICM teams to model various aspects of the problem. Some teams used the basic structure of networks and their properties and computed classic centrality measures to tackle the issues. Some chose to model using a data mining framework. The Analytic Hierarchy Process (AHP) was a common method for addressing discrimination in the identi?cation ofa po- tential conspirator. As a result, the submissions this year were diverse and interesting to read. Overall, the basic modeling was often sound, creative, and sometimes quite powerful. Those that did not reach?nal judging gen- erally suffered from two shortcomings. Some lacked clear explanation or evidence to support their conclusions and recommendations. They seemed to jump from their modeling directly to the results without suf?cient anal- ysis. Others failed to connect their mathematical models to the aspects and basic elements of information science. In general, poor communica- tion was the most signi?cant discriminator in determining which papers reached the?nal judging stage. Although the outstanding papers used different methodologies, they all addressed the problem in a comprehen- sive way by embracing the complexity of the issues, data, questions, and team objectives. These papers were generally well written and presented explanations of their modeling procedures. In several outstanding papers, a unique or innovative approach distinguished them from the rest of the ?nalists. Others were noteworthy for either the thoroughness oftheir mod- eling or the power of their communicated results.Huazhong University of Science and TechnologyThe ICM team from Huazhong University of Science and Technology, Wuhan, China performed a thorough network analysis of the information ?ow and relationshipsofemployeesin theorganization. In their paper,“Ex- tended Criminal Network Analysis Model Allows Conspirators Nowhere to Hide,” they provided an in-depth analysis of the relationships between people and the way the criminal network operated and expanded. This report presented their framework, models, analysis, and results in power- ful visual formats that enabled readers to understand their work and feel con?dent in their results. In many ways, this paper is an excellent exam- ple of the potential of network modeling and the power of social network analysis to sort out nodal,edge,and data attributes through use ofnetwork measures and data analysis.Mathematical Modeling Innovative Practice BaseThe report entitled “iRank Model: A New Approach to Criminal Net- work Detection” wassubmitted by a team from theMathematicalModeling300TheUMAP Journal 33.3 (2012) Innovative Practice Base, China. The Mathematical Modeling Innovative Practice Base,China,established in 2008,is an institute that promotes inter- disciplinary research and educational activities, integrating mathematical modeling and computational approaches to address problems arising in various areas of science and engineering. Their report contained creative analysis of the available data from several perspectives, starting with basic analysis as shown by: Carefully examining into the patterns of information exchanges and social connections in the network, we can see that only 24% messages carry conspiratorial information, which seems not systematically sig- ni?cant given that 20% of all the topics are conspiratorial. Therefore, two patterns can be inferred from the statistical results:?Although conspirators are generally more active than the known innocent people, they exchange irrelevant information with each other. Conspiratorial messages only take a small portion in their message traf?c.?Since the existing 7 conspirators have already involved in spread- ing about 40% of the total conspiratorial messages, it is very likely that the total number of conspirators is less than 20. They also performed a very thorough social network analysis of the message network. This report contained excellent visualizations to explain their algorithm, analysis and results.Nanjing University of Information Science and TechnologyTheICM team from NanjingUniversityofInformation Scienceand Tech- nology, Nanjing, China, built three different models for?nding and sepa- rating conspirators and then merged these for their best-case solution. A fourth model was used to identify the conspiracy leaders. Their paper, “Message Network Modeling for Crime Busting,” was an excellent synop- sis of the diverse methods one could use to approach this problem. Their emphasis was in classical network analysis and data mining algorithms. Once again, this team did a thorough job analyzing semantic analysis and its utility for information and network modeling.Northwestern Polytechnical UniversityFinding the hidden features of a network was the theme of the paper entitled “Social Network Analysis in Crime Busting,” by the ICM team from Northwestern Polytechnical University, Shaanxi, China. This paper started with the foundations of graphs and networks and built the concept of cooperation within the network. This concept was a fundamentally sound and deeper approach than those of many of the other models. The resulting model was a powerful one for understanding a conspiracy andJudges’Commentary301 the team did an excellent job in their creative modeling and analysis. Their discussion on semantics and text analysis was thorough and insightful in ?nding ways for possible inclusion of these more powerful methodologies in their models.Shanghai Jiaotong University“Crime Busting by an Iterative 2-phase Propagation Method,” was sub- mitted by a team from Shanghai Jiaotong University, Shanghai, China. Their classic propagation model of performing iterative and alternating computation of person suspiciousness and topic suspiciousness from each other was creative and powerful. Upon convergence of their model, they produced a priority list ofconspirators and performed a thorough analysis. This team’s model was both mathematically and scienti?cally simple yet elegant.University of Electronic Science and Technology of ChinaThe report and work entitled “Finding Conspirators in the Network: Machine learning with Resource-allocation Dynamics” from the University ofElectronicScienceand Technology ofChina,Chengdu,China,wasstrong from start to?nish. This team made careful and thorough assumptions: (i)Two classes,conspiratorsand non-conspirators,are linearly separa- ble in the space spanned by localfeatures ofa node,which is necessary to machine learning. (ii) A conspirator is reluctant to mention topics related to crime when talking with an outsider. (iii)Conspirators tend not to talk about irrelevant topics frequently with each other. (iv) The leader of conspiracy tries to minimize risk by restricting direct con- tacts. (v) A non-conspirator has no idea of who are conspirators, thus treating conspirator and non-conspirators equally. Then they used machine learning and logistic regression to build their model. They were careful to show their analysis of leader selection and other problem requirements. They followed up their modeling and anal- ysis with sensitivity analysis and a careful discussion of the strengths and weaknesses of their model and its approach. Most impressive was their ability to discuss the incorporation of semantic analysis into their model and the discussion of the power of information modeling to the future.Cornell UniversityThe team from Cornell University, Ithaca, NY, took a very different ap- proach than the other Outstanding papers. Their paper “Crime Ring Anal- ysis with Electric Networks” presented a model using an electrical circuit analogy for the conspiracy where the interactions between people, repre- sented as circuit nodes, were considered a conductance term. This model302TheUMAP Journal 33.3 (2012) was creative in its structure and enabled the team to perform an interesting analysis of the conspiracy factors. This team was selected as the INFORMS winner.ConclusionAmong the 1,329 papers, there were many strong submissions, which made judging dif?cult. However, it was gratifying to see so many students with the ability to combine modeling,science and effective communication skills in order to understand such a complex problem and recommend solutions. We look forward to next year’s competition, which will involve another problem in network science and hopefully, the participation of many teams of competent and passionate interdisciplinary modelers.Recommendations forFuture Participants?Answerthe problem.Weak papers sometimes do not address a signif- icant part of the problem. Outstanding teams often cover all the bases and then go beyond.?Time management is critical.Every year there are submissions that do an outstanding job on one aspect of the problem, then “run out of gas” and are unable to complete their solution. Outstanding teams have a plan and adjust as needed to submit a complete solution.?Coordinate yourplan.It is obvious in many weak papers how the work and writing was split between group members,then pieced together into the?nal report. For example, the output from one model doesn’t match the input for the next model or a section appears in the paper that does not?t with the rest of the report. The more your team can coordinate the efforts of its members, the stronger your?nal submission will be.?The model is not the solution.Some weak papers present a strong model,and then stop. Outstandingteamsusetheir modelstounderstand the problem and recommend or produce a solution.?Explain what you are doing and why.Weak teams tend to use too many equations and too few words. Problem approaches appear out of nowhere. Outstanding teams explain what they are doing and why.Judges’Commentary303About the AuthorsChrisArneygraduated from WestPointand served as an intelligence of?cer in the U.S. Army. His aca- demic studies resumed at Rensselaer Polytechnic In- stitute with an M.S. (computer science) and a Ph.D. (mathematics). He spent most of his 30-year military career as a mathematics professor at West Point, be- fore becoming Dean ofthe SchoolofMathematicsand Sciences and Interim Vice President for Academic Affairs at the College of SaintRosein Albany,NY.Christhen moved toRTP(Research TrianglePark), NC,where he served for various durations as chair ofthe MathematicalSci- ences Division, of the Network Sciences Division, and of the Information Sciences Directorate of the Army Research Of?ce. Chris has authored 22 books,written morethan 120technicalarticles,and given morethan 250pre- sentations and 40 workshops. His technical interests include mathematical modeling, cooperative systems, pursuit-evasion modeling, robotics, arti- ?cial intelligence, military operations modeling, and network science; his teaching interests include using technology and interdisciplinary problems toimproveundergraduateteachingand curricula. Heisthefoundingdirec- tor of COMAP’s Interdisciplinary Contest in Modeling (ICM)R?. In August 2009, he rejoined the faculty at West Point as the Network Science Chair and Professor of Mathematics. Kate Coronges is an Assistant Professor in the De- partment of Behavioral Sciences and Leadership and a research fellow in the Network Science Center at the U.S. Military Academy. She has a Master’s in Public Health and a Ph.D. in Health Behavior Research from the Uni- versity of Southern California. Kate teaches courses in social network analysis and public policy, working with cadets to apply analytic tools to understand and model complex systems, particularly as they relate to public policy issues such as energy, education, information security, and health care. Her primary research effort involves a social network study of leadership and organi- zational performance. She also is working on an analysis of social accept- ability of automatic biometric authentication tools, social determinants of phishing security vigilance, and modeling social media data to understand how protests turn to riots. Her publications in network science include the study of education, drug addiction, DADT (“Don’t ask, don’t tell”) policy, coalition building, and security.304TheUMAP Journal 33.3 (2012)Reviews305ReviewsMaasz, Juergen, and John O’Donoghue (eds.). 2011.Real-World Problems for Secondary School Mathematics Students: CaseStudies. Rotterdam, The Netherlands: Sense Publishers; ix + 281 pp, $49.99 (P). ISBN 978–94– 6091–541–3. Secondary school mathematics teachers seek resources for bringing rel- evant applications of mathematics to their students, and this book is de- scribed as being “full of ideas for introducing real world problems into mathematics classrooms.” The collection of 16 papers promises to provide teachers with a wealth of applications from a wide variety of school con- tent areas (e.g., statistics, geometry, and calculus), and to focus on topics that should appeal to a student audience with diverse interests (e.g., en- ergy issues,traveling to Mars,rugby and snooker, lotteries,logisticgrowth, worldwide oil reserves, and even Dirk Nowitzki). That is, in fact, what this collection does provide. Many of the authors offer suggestions on how to format the material into classroom lessons, yet they also encourage teachers to individualize the lessons for their own students and their own circumstances. There is also an international?avor to thecollection,a fact that willappealto many teachersand many students. There are several cautions, however:?Although the content is timely, class time will be needed for students to pro?t from these lessons; most lessons are not one- or two-period explorations. Teachers who are already short on time will have to weigh whether the advantageofproviding studentswith interesting,nontrivial real world applications is enough to warrant requisite class days.?Unless a teacher has a multiple-courseassignment,he or she willnot?nd a variety of lessons from which to select if one of the requirements is to illustrate applications of the mathematical topics covered in a particular course.?Since the mathematics is accessible, but de?nitely nontrivial, much of the content may be daunting for students who are not already mathe- matically pro?cient. For these reasons, this collection may best be seen as an excellent resource for a mathematics department rather than for one particular teacher. ItTheUMAPJournal33(3)(2012)305–308. c ?Copyright2012by COMAP,Inc. Allrightsreserved. Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for pro?t or commercial advantage and that copies bear this notice. Abstracting with credit is permitted, but copyrights for components of this work owned by others than COMAP must be honored. To copy otherwise, to republish, to post on servers, or to redistribute to lists requires prior permission from COMAP.306TheUMAP Journal 33.3 (2012) would also be a?ne resource for an independent study course or for an upper-level course in mathematical modeling. J.T.Sutcliffe,MathematicsDepartment,St.Mark’sSchoolofTexas,10600Preston Road, Dallas, TX 75230;sutcliffe@smtexas.org. Thomson, Brian S. 2010.The Calculus Integral.North Charleston, SC: Cre- ateSpace. x + 291pp,$14.95(P).ISBN 978–1–442180956. Free download athttp://classicalrealanalysis.info/documents/ T-CalculusIntegral-AllChapters-Portrait.pdf. The study of integration within a?rst course of calculus is always prob- lematic. Thestandard approach isto begin with theproblem ofdetermining areas under curves,createapproximating sums,moveon to thegeneralRie- mann sums, de?ne the de?nite integral as a limit of these sums, and then prove the Fundamental Theorem of Calculus that links these limits of Rie- mann sumsto what Ishallrefer to asantiderivatives,also known asprimitives orinde?niteintegrals. Thomson is one in a long line of mathematicians dissatis?ed with this approach. It hasmany?aws. Thede?nition ofthede?niteintegralasa limit ofRiemann sums is incredibly sophisticated. For most students,the formal de?nition is quickly forgotten and the working de?nition of integration becomes antidifferentiation. The problem with this is that many students lose the link between antidifferentiation and Riemann sums. The reason that the standard textbook approach is problematic is that it is seriously ahistorical. Riemann created his de?nition in the 1850s for the speci?c purpose of determining how discontinuous a function might be yet still be integrable. His formulation is ideally suited for this purpose, a purpose that bears no relevance for the?rst year of university calculus. The fact is that from the time that Newton?rst recognized the power of reversing differentiation as a tool for computing areas until Cauchy sought a characterization of integration that would enable him to assert that every continuous function is integrable, integration was de?ned as antidiffer- entiation. Thomson embraces this natural and historical de?nition of the integral, what he calls “The Calculus Integral,” and uses it as the starting point for an exploration into our modern understanding of integration. This book is described as appropriate for a course of honors calculus or a?rst course in real analysis. In either context, it would be challeng- ing but do-able with the right students. The development is elegant and extremely original. After a dense?rst chapter that introduces the basic theorems needed to work with limits, sequences and series, continuity, and differentiability, Thomson begins by de?ning the inde?nite integral offon an open interval as a continuous function whose derivative coincides withfexcept possibly at?nitely many points. De?nite integrals are de?nedReviews307 in terms of inde?nite integrals. The Fundamental Theorem is introduced in two steps: First is the use of the Mean Value Theorem to establish the existence of a sequence of tagsζisuch that?b af(x)dx=n?i=1f(ζi)(xi?xi?1).Second comes the theorem that the de?nite integral can be uniformly approximated by Riemann sums with arbitrary tags. The emphasis has switched in a pedagogically signi?cant way from de?ning the de?nite in- tegral as a limit of Riemann sums to demonstrating that it can be approxi- mated arbitrarilycloselybyRiemann sums,simplybycontrollingthelength of the subintervals in the partition. This approach opens the door to the result that de?nite integrals are also uniformly approximated by Robbins sums, an interesting variation on the Riemann sum that was described by Herbert E. Robbins . The text continues through the study of sequences and series of inte- grals and the monotone convergence theorem, then into Cantor sets, sets of measure zero, functions with zero variation, and absolute continuity. The most original aspect of this text is the de?nition of the Lebesgue integral. Parallel to the Calculus Integral, the inde?nite Lebesgue integral offon an open interval is de?ned as an absolutely continuous function in the Vi- tali sense whose derivative coincides withfexcept possibly on a set of measure zero. The Lebesgue integral offover the interval is thende?ned asF(b)?F(a), whereFis an inde?nite Lebesgue integral offonthis interval. Connecting this de?nition to Riemann sums leads naturally into a discussion of the Henstock-Kurzweil integral, where the text ends. Traditional measure theory is nowhere to be found. One of the most distinctive features of this book is that none of the theorems or corollaries is proven in the text. Instead, Thomson leads the reader through a series of exercises that build to each proof. The actual text is quite short, only 150 pages. It is followed by an almost equally long presentation of the solutions to the exercises. Just the text, leaving the scaffolded proofs to the students without the option of looking them up, would provide an excellent inquiry-based introduction to real analysis or a challenging senior seminar. Thomson has given us a rich introduction to the complexities ofintegra- tion with many historical references and intriguing asides. I agree with his use of the Calculus Integral and his approach to Riemann sums. De?ning integration as a limit of Riemann sums makes no sense for?rst-year calcu- lus. I am not convinced that his approach to Lebesgue integration makes better pedagogical sense than a more traditional route, but it does form part of a coherent and consistent approach to integration. The student who completes this book willbe very wellversed in realanalysis and fully ready to tackle measure theory.308TheUMAP Journal 33.3 (2012)ReferenceRobbins, Herbert E. 1943. Note on the Riemann integral.American Mathe- matical Monthly50 (10) (December 1943): 617–618. DavidBressoud,MathematicsandComputerScience,MacalesterCollege,St.Paul, MN 55105;bressoud@macalester.edu.
北美论文到底难不难呢？Locating the conspirators in the company The mathematical model built in this paper can analyze the complex crime reasonably. According to a part of known arrested suspects and their social relationship, the model can discover the conspirator with the maximum possibility, which can accelerate the process of investigating cases With the the suspicious information, known conspirators and innocents, a model of finding internal crime suspects in the company is established. For a person in the company, if he has closer contact with other members he will has more possibility to commit a crime, which is called core degree. Making use of Matrix replacement method and Hierarchical clustering method, we can find the group of higher core degree and sort them with descending. Next, we deem that a member who has closer contact with a conspirator will be more possibilily a conspirator . Because of the known conspirators in the company, to find the other suspects in the network sturcture, A list of association degree is ordered with ascending. At last, on the basis of the computational model, taking transmission information among members into acount, we stratified these message topics and given the different layers of different topics of suspicious degrees. According to the different topics among these members, we can calculate their suspicious degrees accurately and ordered them descending. Analysis by synthesis the result of three sort, using a computermerge the result of three sort, thereby all of the suspects is obtained accurately. Computers is the efficient and accurate tools to handle and analyze large-scale data. Using computers to deal with original data can reduce the error of the value of topics. We can make full use of semantic network analysis, artificial intelligence and text analysis, and calculate the frequency of words in original data . There are some less important but greater frequent words among those data, such as copula and personal pronouns. Deleting this type of words and merge them with similar semantic words, a language bank is then built with these similar words, and the frequency of every words is computed. Sometimes words collocation in the bank are also suspicious. therefore when these suspicious words appear and the frequency of them is high, thus the person who transmit the type of information must be suspicious characters. It suffice to find all of suspects accurately by this method. The crime busting model we built can also apply to many other practical cases. For example, it can be used on detecting difficult network-crime cases and applied to the problem of the spread of the virus between cells in the biological network. The method can also deal with these difficulties with high accuracy. Locating the conspirators in the company Abstract The mathematical model built in this paper can analyze the complex crime reasonably. According to a part of known arrested suspects and their social relationship, the model can discover the conspirator with the maximum possibility, which can accelerate the process of investigating cases With the the suspicious information, known conspirators and innocents, a model of finding internal crime suspects in the company is established. For a person in the company, if he has closer contact with other members he will has more possibility to commit a crime, which is called core degree. Making use of Matrix replacement method and Hierarchical clustering method, we can find the group of higher core degree and sort them with descending. Next, we deem that a member who has closer contact with a conspirator will be more possibilily a conspirator . Because of the known conspirators in the company, to find the other suspects in the network sturcture, A list of association degree is ordered with ascending. At last, on the basis of the computational model, taking transmission information among members into acount, we stratified these message topics and given the different layers of different topics of suspicious degrees. According to the different topics among these members, we can calculate their suspicious degrees accurately and ordered them descending. Analysis by synthesis the result of three sort, using a computer merge the result of three sort, thereby all of the suspects is obtained accurately. Computers is the efficient and accurate tools to handle and analyze large-scale data. Using computers to deal with original data can reduce the error of the value of topics. We can make full use of semantic network analysis, artificial intelligence and text analysis, and calculate the frequency of words in original data . There are some less important but greater frequent words among those data, such as copula and personal pronouns. Deleting this type of words and merge them with similar semantic words, a language bank is then built with these similar words, and the frequency of every words is computed. Sometimes words collocation in the bank are also suspicious. therefore when these suspicious words appear and the frequency of them is high, thus the person who transmit the type of information must be suspicious characters. It suffice to find all of suspects accurately by this method. The crime busting model we built can also apply to many other practical cases. For example, it can be used on detecting difficult network-crime cases and applied to the problem of the spread of the virus between cells in the biological network. The method can also deal with these difficulties with high accuracy. Keywords: core degrees, suspicious degrees, association degrees, text analysis, crime busting Team#15783 page 2 of 15 Contents 1 Introduction ............................................................................................................... 3 2 Analysis of the Problem ............................................................................................ 3 3 Crime busting ............................................................................................................ 4 3.1 Analysis ............................................................................................................ 4 3.2 Symbols ............................................................................................................ 4 3.3 Assumption ...................................................................................................... 5 3.4 Modeling .......................................................................................................... 5 3.4.1 Solving of the core degree ..................................................................... 5 3.4.2 Solving of absolutely close degree ........................................................ 6 3.4.3 Using suspected information to find criminal suspects ..................... 7 3.5 Consider the additional datas ........................................................................ 9 4 Computer processing .............................................................................................. 10 4.1 Analysis .......................................................................................................... 10 4.2 Definition ....................................................................................................... 11 4.3 Computer processing Method ...................................................................... 11 5 Model promotion ..................................................................................................... 13 5.1 Analysis of model .......................................................................................... 13 5.2 Model application.......................................................................................... 13 6 Weaknesses and Strengths of the Model ............................................................... 14 6.1 Strengths ........................................................................................................ 14 6.2 Weaknesses .................................................................................................... 14 7 Conclusion ............................................................................................................... 15 8 References ................................................................................................................ 15 Team#15783 page 3 of 15 1 Introduction In recent years, the group frauds and economic crime problems have been very common in our daily life. Since the number of suspects is great, then it is difficult to detect the cases for the public security organs. Once the criminals escape, they will be a threat to more people and property, and even to personal security . The links between criminals posed by criminal networks is complex , and the mistake of judgment will falsely accuse innocent people and let the criminals get off . Here the problem arises: how to locate all the suspicion without a mistake? We learned many of the existing methods find that none give us a definite answer. Then we found the model of crime busting is similar to the model of social networks in the BBS in a way. What we need to do is identify the core characters and important figures in this matter. If we do the above, then we can basically determine the degree of suspicion for each person in this group combined with all kinds of the message topics, and arrange the guesswork out of suspects. It narrow the scope of criminal elements and reduce the workload of the public security organs so that they can break cases faster. 2 Analysis of the Problem First, we should gain a clear idea of the characteristics of internal crime. There is a wide range of the criminal subjects: the senior managers of the company can be the conspirator, and each of the staffs can be also the conspirator. So it is difficult to capture criminals. But if we use the key features of economic crime, then we can easily find the mastermind of their accomplices. According to the characteristics of crime and the offender's psychological, we believe that the possibility that those persons who keep close contact with criminals are the conspirators is very large. Then, by known criminals and suspicious messages, we can accurately determine criminal conspirators. Based on the known contacts between persons inside the company, we use the matrix displacement method and the hierarchical clustering method to identify the company's central figures who keep close contact with other employees. Then combining with these figures with the types of message topics, we can make a further judgment to identify the other suspects. In addition, using given already certain schemers and certain not schemers, we can further discuss the group which have close relationship with those persons, and so we can increase the veracity of the result. With the help of our model, we can identify the maximum possible accomplices and confidently point out the conspirators and accomplices within the company. Team#15783 page 4 of 15 3 Crime busting 3.1 Analysis Although criminals choose randomly the modality of crime information and the route transmission of crime information, but their crime form still has a certain rule. Their modus operandi in this company transmits mainly by the information transfer form, so criminal gangs should form a network connection model. Questions have given several already certain criminals and crime information types, then according to these criminals' close relationship and the suspected degrees of total numbers of message receive and message issued, we can judge the scope of the criminals. 3.2 Symbols Table1 symbols k A The number of the company group members ai, k if member i and member k have directly connected D C k Connection degree of each member k A ij g k if means of member i A and member j A pass by k A . C k B k A ’s absolute agent degree li, k the most short-circuit path length of member i A and member j A C C k the absolutely tight density D C the average value of the absolute agent degree B C the average value of the absolute agent degree C C the average value of the closely degree D Dev C the smooth coefficient of connection degree B Dev C the smooth coefficient of absolute agent C Dev C the smooth coefficient of absolutely tight density Team#
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