Complexity Theory in the Social Sciences




PS 793, Fall 2003                                                      

Professor Robert Axelrod

Office Hours: Tuesdays 2-4 at 409 Lorch



            Complexity theory is a new interdisciplinary approach to understanding dynamic processes involving the interaction of many actors.  A primary methodology of complexity theory is agent-based modeling.  Agent-based modeling involves specifying how individual agents (such as people, nations, or organizations) interact with each other and with their environment.  Computer simulation is then used to discover the emergent properties of the model, and thereby gain insights into dynamic processes that would be too difficult to model with standard mathematical techniques.


            Agent-based modeling provides a third way of doing science in addition to the traditional methods of deduction and induction.  Like deductive models, an agent-based model starts with a well-defined set of assumptions.  But unlike deductive models, an agent-based framework is capable of revealing consequences through simulation that cannot be deduced with standard mathematical techniques.  And like induction, the main method of finding these consequences (and perhaps new insights) is through analysis of a set of data - in this case data generated by running the computer simulation. The goal is to discover new principles about the dynamics of complex systems, especially complex adaptive systems that are typical of social processes.  There is no need to assume rationality.


            The course will consider a wide variety of applications of agent-based models to the social sciences, including residential segregation, revolution, social influence, urban growth, war, alliances, organizational change, elections, and stock markets.  Among the issues to be examined across models are: path dependence, sensitivity to initial conditions, emergence of self-organized structure, adaptation to a changing environment, co-evolution, information cascades, and criteria for judging the value of an agent-based model.


            The grade will be based on four exercises (6% each; due September 18 to October 9), the research design for the project (10%; due October 30), and the project report (64%; due noon December 15). 


            This syllabus is on the web with live links. See “Recent Courses” in Axelrod’s personal home page.


            Information and resources for the exercises are at:


            Knowledge of a programming language (such as BASIC, Pascal, C, C++ or Java) is required.  Some basic statistics will be helpful, but is not required.


            The course is intended for graduate students in a wide variety of fields, not just political science.


            Office hours are Tuesdays 2-4 at 409 Lorch Hall. The phone number is 763-0099. I can also be reached on e-mail at  My personal web page is


           A course pack has been prepared by Dollar Bill Copy but is available for sale only at Ulrich’s Bookstore, 549 E. University, 662-3201.




Required Reading




1. Introduction to Complexity Theory. September 4.

           Krugman, Paul, 1996. The Self-Organizing Economy, Malden, MA: Blackwell, pp. 16-21. On Schelling’s residential segregation model.

           Rauch, Jonathan, “Seeing Around Corners,” Atlantic, April 2001.  (Skip section on “Mr. Schelling’s Neighborhood”).

           Center for the Study of Complex Systems, “What is the Study of Complex Systems,” and “Examples” from

            Vicsek, Tamas, “Complexity: The Bigger Picture,” Nature 418, 131 (2002).  




2. Agent-Based Modeling. September 11.

           Resnick, Mitchel 1994. Turtles, Termites, and Traffic Jams: Explorations in Massively Parallel Microworlds, Cambridge, MA: MIT Press, p 3-19 on decentralization.

           Axelrod, Robert and Ross A. Hammond, 2003.  “The Evolution of Ethnocentric Behavior,” Revised version of a paper prepared for delivery at the Midwest Political Science Association, April 3-6, 2003. Chicago, IL. Available at

Animation at

           Axelrod, Robert, 1997. "Advancing the Art of Simulation in the Social Sciences" in Rosario Conte, Rainer Hegselmann and Pietro Terna (eds.), Simulating Social Phenomena. Berlin: Springer.



3. Organizations.  September 18.

           Exercise 1 due.

           March, James G. 1991. "Exploration and Exploitation in Organizational Learning," Organization Science,  2, pp. 71-87.

           Simon, Herbert, 1982.  The Sciences of the Artificial second edition, Cambridge, MA: MIT Press, pp. 193-230.

           Epstein, Joshua, 2003. “Growing Adaptive Organizations: An Agent-Based Computational Approach,” Santa Fe Institute Working Paper.

(Note: the figures are best viewed in color.)



4. Social Influence.  September 25.

           Exercise 2 due.

           Kennedy, James, 1998. “Thinking Is Social: Experiments with the Adaptive Culture Model,” Journal of Conflict Resolution, 42, pp. 56-76. Available through Proquest at

           Axtell, Robert and Joshua Epstein,1999.  “Coordination in Transient Social Networks: An Agent-Based Computational Model of the Timing of Retirement,” Brookings CSED Working Paper No. 1, May 1999. Online at

           The Java model is available for running at



5. Genetic Algorithm and the Prisoner's Dilemma. October 2.

           Exercise 3 due.

           Holland, John H. 1992 (July). "Genetic Algorithms," Scientific American, 267, pp. 66-72.

            (If you are not familiar with the Prisoner's Dilemma, read Robert Axelrod, 1984. The Evolution of Cooperation, New York, Basic Books, pp. 3-69 and 158-68.)

           Riolo, Rick L., 1992 (July). “Survival of the Fittest Bits,” Scientific American, 267, pp. 89-91.  

            Axelrod, Robert, "The Evolution of Strategies in the Iterated Prisoner's Dilemma," 1987.  In Lawrence Davis (ed.), Genetic Algorithms and Simulated Annealing, London: Pitman, and Los Altos, CA: Morgan Kaufman, pp. 32-41.  Reprinted in Robert Axelrod, The Complexity of Cooperation: Agent-Based Models of Competition and Collaboration, 1997. Princeton, NJ: Princeton University Press, pp. 11-29.



6. Competition.  October 9. 

           Exercise 4 due.

           Cederman, Lars-Erik, 2001. "Nationalist Systems Change and its Geographic Consequences,”  Paper prepared for the Annual Meeting of the American Political Science Association, San Francisco., August 30-September 2, 2001. Note: You may skim the appendix. 

           Kollman, Ken, John H. Miller and Scott E. Page, 1998. “Political Parties and Electoral Landscapes,”  British Journal of Political Science, 28, pp. 139-58. Available at


7. Evolution and Reputation, October 16.

           Lindgren, Kristian, 1991. "Evolutionary Phenomena in Simple Dynamics," in C. G. Langton et al. (eds.), Artificial Life II, Reading, MA: Addison-Wesley.

Henrich, Joseph, et al., May 2001. “In search of homo economicus: Behavioral experiments in 15 small-scale societies,” The American Economic Review, 91 ( 2001), pp 73-78.

           Nowak, Martin A., Karen M. Page, and Karl Sigmund, 8 September 2000. “Fairness Versus Reason in the Ultimatum Game,” Science, 289,  pp. 1773-75. Available from Good example of evolutionary dynamics to explain human behavior in a simple game, with and without reputational effects.



8. Cascades.  October 23.

            Farmer, J. Doyne, and Andrew W. Lo, 1999. “Frontiers of Finance: Evolution and Efficient Markets,” Proc. Nat. Acad. Sci. USA, 96. pp. 1991-2.

            Arthur, Brian W., John H. Holland, Blake LeBaron, Richard Palmer and Paul Thayer, 1997. “Asset Pricing Under Endogenous Expectations in an Artificial Stock Market,” in B. Arthur, S. Durlauf and D. Lane (eds.), The Economy as an Evolving Complex System, II, Reading, MA: Addison-Wesley, pp. 15-44.  The source code is available at

            Arthur, W. Brian, 1988. "Urban Systems and Historical Path Dependence," in Jese H. Ausubel and Robert Herman (eds.), Cities and Their Vital Systems. Washington DC: National Academy Press, pp. 85-97.


9. Complexity.  October 30.

           Research design is due.

           Mitchell, Melanie, 4 Oct 2002. “Is the Universe a Universal Computer? Review of A New Kind of Science, by Stephen Wolfram” Science, Volume 298, Number 5591, pp. 65-68. Available from

           Resnick, Mitchel  1994. Turtles, Termites, and Traffic Jams: Explorations in Massively Parallel Microworlds, Cambridge, MA: MIT Press,  pp. 129-144 on decentralized mind set.

           Gell-Mann, Murray, 1995. "What is Complexity?" Complexity, 1, pp. 16-19 on measuring the degree of complexity in a system.

           Arthur, W. Brian. 1993. "Why Do Things Become More Complex?", Scientific American, May, p 144.

           Holland, John, 1995. Hidden Order. Reading, MA: Addison-Wesley, pp. 1-40 on elements of a complex adaptive system.

           Kelly, Kevin, 1994. Out of Control: The New Biology of Machines, Social Systems, and the Economic World.  Reading, MA: Addison-Wesley, pp. 22-25 and 468-72.



10-12. Class reports. November 6, 13, and 20.


13. Overview. December 4. 


Project report is due noon December 15.





Optional Readings



Introduction to Complexity Theory. Week 1.

           Waldrop,  M. Mitchell, Complexity: The Emerging Science at the  Edge of Order and Chaos. Touchstone Books. 1993.

           Kelly, Kevin, Out of Control. Addison Wesley, 1996.

           Axelrod, Robert and Michael Cohen, Harnessing Complexity. Free Press, 2000.

            Johnson, Steven, Emergence: The Connected Lives of Ants, Brains, Cities and Software. Scribner, 2001.

           Watts, Duncan J. Six Degrees: The Science of a Connected Age, W.W. Norton & Company, 2003.


Agent-Based Modeling. Week 2.

           Tesfatsion, Leigh (1997) ``How Economists Can Get A-Life'' in The Economy as a Complex Evolving System II W. Brian Arthur, Steven Durlauf, and David Lane eds. pp 533-565. Addison Wesley, Reading, MA. 

           Epstein, Joshua M. and Robert A. Axtell, 1976, Growing Artificial Societies: Social Science from the Bottom Up.  Washington, DC: Brookings and Cambridge, MA: MIT Press,   especially pp. 1-53 and  94-137.

           Gilbert, Nigel, 1999. “Simulation, A New Way of Doing Science,” American Behavioral Scientist, 42, August, pp. 1485-87. An introduction to a special issue of useful articles.

           Adaptive Agents, Intelligence, and Emergent Human Organization: Capturing Complexity through Agent-Based Modeling. A special issue of Proc. Natl. Acad. Sci. USA, Vol. 99 (Supp. 3). May 14 2002,



Organization Theory. Week 3.

            March, James G. 1978. "Bounded Rationality, Ambiguity, and the Engineering of Choice," Bell Journal of Economics, 9, pp. 587-607.

            Cohen, Michael D., James G. March and Johan P. Olsen, 1972. "A Garbage Can Model of Organizational Choice," Administrative Science Quarterly, 17, pp. 1-25.

            Carley, Kathleen, 1991. "A Theory of Group Stability," American Sociological Review, 56, pp. 331-54.

            Axelrod, Robert and Michael D. Cohen, 2000. Harnessing Complexity: Organizational Implications of a Scientific Frontier (NY: Free Press).

            Carpenter, Daniel, David Lazer, and Kevin Esterling, “The Strength of Strong Ties: A Model of Contract Making in Policy Networks with Evidence from U.S. Health Policies,”  Rationality and Society, forthcoming.


Social Influence. Week 4.

           Axelrod, Robert, 1997. "The Dissemination of Culture: A Model with Local Convergence and Global Polarization," Journal of Conflict Resolution, 41, pp. 203-26.  Reprinted in Robert Axelrod, The Complexity of Cooperation: Agent-Based Models of Competition and Collaboration, 1997. Princeton, NJ: Princeton University Press, pp. 145-77.  For source code in Pascal see

           Axtell, Robert, Joshua Epstein, and H. Peyton Young, “The Emergence of Classes in a Multi-Agent Bargaining Model,”  Center for Social and Economic Dynamics, Washington, D. C. Working Paper 9.  The paper and the model itself are available at

           Gaylord, Richard J. and Louis J. D’Andria, 1998. Simulating Society: A Mathematica Toolkit for Modeling Socioeconomic Behavior (NY: Springer). (The Mathematica source codes are available at for programs related to Axelrod’s culture model.)

           Latane, Bibb, Andrzej Nowak and James H. Liu. 1994. "Measuring Emergent Social Phenomena: Dynamism, Polarization and Clustering as Order Parameters of Social Systems," Behavioral Science, 39, p. 1-24.

           Granovetter, Mark, 1978. "Threshold Models of Collective Behavior," American Sociological Review, 83, pp. 1420-42.  (This is an example of a formal model that does not require agent-based techniques to solve.)

            Lustig, Ian, “Agent-based modeling of collective identity: testing constructivist theory,“ Journal of Artificial Societies and Social Simulation vol. 3, no. 1. Available at

           Rilling, J. K., Gutman, D. A., Zeh, T. R., Pagnoni, G., Berns, G. S., & Kilts, C. D. (2002). A Neural Basis for Social Cooperation. Neuron, 35, 395-405.  See also NY Times story by N. Angier on this research, July 23, 2002.


Genetic Algorithm and Prisoner's Dilemma. Week 5.

            Riolo, Rick, 1992. "Survival of the Fittest," Scientific American, July, pp. 114-6 on how to make your own genetic algorithm.

            Morikawa, Tomonori, John Orbell and Audun S. Runde, 1995. "The Advantage of Being Moderately Complex,"  American Political Science Review, 89, pp. 601-11.  (On achieving cooperation even in one move Prisoner's Dilemmas through local interaction.)

            Axelrod, Robert, 1997. Complexity of Cooperation, Chapter 3 on coping with misperception and misunderstandings in the iterated Prisoner’s Dilemma.. For source code in Fortran, including all of the rules submitted to the second round of the computer tournament, see

            Lindgren, Kristian and Mats G. Nordahl, “Cooperation and Community Structure in Artificial Ecosystems,” in Chris Langton (ed.), Artificial Life: An Overview (Cambridge, MA: MIT Press, 1995), pp. 15-37. (Includes spatial PD, explicit resource flows, predatory interactions, and food webs.)

Holland, John H,. Hidden Order: How Adaptation Builds Complexity. The MIT Press, 1995.

            Cohen, Michael D., Rick L. Riolo, and Robert Axelrod, 2001. “The Role of Social Structure in the Maintenance of Cooperative Regimes,” Rationality and Society, 13, pp. 5-32.


Competition. Week 6.

           Johnson, Paul E., 1999. “Simulation in Political Science,” American Behavioral Scientist, 42, pp. 1509-30.

           Epstein, Joshua,  John D. Steinbruner, and Miles T. Parker, 2001. “Modeling Civil Violence: An Agent-Based Computational Approach,” Center for Social and Economic Dynamics, Washington, D. C. Working Paper 20.

           Kollman, Ken, John H. Miller, and Scott E. Page, 1992. "Adaptive Parties in Spatial Elections," American Political Science Review, 86, pp. 929-37

           Cederman, Lars-Erik, 1997. Emergent Actors in World Politics: How States and Nations Develop and Dissolve. (Princeton, NJ: Princeton University Press), especially pp. 184-212 and 219-22.

           Cederman, Lars-Erik, “Back to Kant: Reinterpreting the Democratic Peace as a Macrohistorical Learning Process. American Political Science Review, 1995, vol. 95, pp 5-31.

           Bremer, Stuart A. and Michael Mihalka. 1977. "Machiavelli in Machina: Or Politics among Hexagons," in Karl W. Deutsch et al. (eds.)., Problems of World Modeling. Cambridge, MA: Ballinger, pp. 303-337.

           Cusack, Thomas R. and Richard Stoll. 1990. Exploring Realpolitik: Probing International Relations Theory with Computer Simulation. Boulder, CO: Lynne Rienner.

           Schrodt, Philip, "Conflict as a Determinant of Territory," Behavioral Science, 26, 1981, pp. 37-50.

           Cederman, Lars-Erik, 1994. "Emergent Polarity: Analyzing State-Formation and Power Politics," International Studies Quarterly, 38, especially pp. 501-33.

           Axelrod, Robert, 1995. "A Model of the Emergence of New Political Actors" in Nigel Gilbert and Rosaria Conte (eds.), Artificial Societies: The Computer Simulation of Social Life. London: University College Press, 1995, pp. 19-39. Reprinted in Robert Axelrod, The Complexity of Cooperation: Agent-Based Models of Competition and Collaboration, 1997. Princeton, NJ: Princeton University Press, pp. 121-144.  For source code in Pascal see

          Kollman, et al., 1997. “Political Institutions and Sorting in a Tiebout Model,” The American Economic Review, 87, pp. 977ff.


Evolution and Reputation. Week 7.

           Riolo, Rick L. Michael D. Cohen, and Robert Axelrod, 22 November 2001. “Evolution of Cooperation without Reciprocity,”, Nature, 414.

           Ferriere, Regis, 11 June1998. “Help and You Shall Be Helped,” Nature, 393, pp. 517-9.

            Nowak, Martin A. and Karl Sigmund, 11 June 1998. “Evolution of Indirect Reciprocity by Image Scoring,” Nature, 393, pp. 573-7.



Cascades. Week 8.

LeBaron, Blake, 2000. “Agent-Based Computational Finance: Suggested Readings and Early Research,” Journal of Economic Dynamics & Control, 24, pp. 679-702.

            Ray J. Paul, George M. Giaglis, and Vlatka Hlupic, 1999. “Simulation of Business Processes,” American Behavioral Scientist, 42, pp. 1551-76.

            Sargent, Thomas, 1993. Bounded Rationality in Macroeconomics. Oxford: Clarendon Press. 

Palmer, R.G. , W. B. Arthur, H. Holland et al,. ”Artificial Economic Life- A Simple Model of the Stock Market." Physica D. 75 (1-3): 264-274 Aug. 1, 1994. See also articles that cite this article for additional stock market models.

Lux T., and M. Marchesi, “Scaling and Criticality in a Stochastic Multi-Agent Model of a Financial Model,” Nature, 397 (6719): 498-500, Feb. 11, 1999. See also articles that cite this article for additional stock market models.

            Albin, Peter and Duncan K. Foley, 1992. "Decentralized, Dispersed Exchange Without and Auctioneer: A Simulation Study,"  Journal of Economic Behavior and Organization, 18, pp. 27-51.

            Krugman, Paul, 1993. "On the Number and Location of Cities," European Economic Review, 37, pp. 293-8.

            Arthur, W. Brian, 1995. "Complexity in Economic and Financial Markets," Complexity, 1, pp. 20-25.

            Lesourne, Jacques, 1992. The Economics of Order and Disorder: The Market as Organizer and Creator. Oxford: Clarendon Press.

.          Lohmann, Susanne, 1994. “The Dynamics of Information Cascades: The Monday Demonstrations in Leipzig, East Germany, 1989-91,” World Politics, 47, p. 42-101.

           Foutain, Henry, “The Physics of the Wave, in Stadiums, Not Oceans,” NY Times, September 17, 2002.


Complexity. Week 9.


            A. Meaning of Complexity

            Waldrop, Mitchell, 1992. Complexity: the Emerging Science at the Edge of Order and Chaos. New York: Simon and Schuster on the  evolution of chaos theory into complexity theory, some applications to social sciences, and the history of the Santa Fe Institute.

            Holland, John H., 1995. Hidden Order, rest of book.

            Holland, John H., 1998. Emergence: From Chaos to Order (Reading, MA: Addison-Wesley).

            Gell-Mann, Murray, 1994. The Quark and the Jaguar: Adventures in the Simple and the Complex. New York: Freeman.

            Lansing, Stephen, and James Kremer, 1993. "Emergent Properties of Balinese Water Temple Networks: Coadaptation on a Rugged Fitness Landscape," American Anthropologist, 95, pp.. 97-114.



           B. Fitness Landscapes

           Kauffman, Stuart, 1995. At Home in the Universe: The Search for Laws of Self-Organization and Complexity, New York and Oxford: Oxford University Press, pp. 169-75 on background on his N-K landscapes.   

           Axelrod, Robert and D. Scott Bennett, 1993. "A Landscape Theory of Aggregation", British Journal of Political Science, 23,  pp. 211-33. .  Reprinted in Robert Axelrod, The Complexity of Cooperation: Agent-Based Models of Competition and Collaboration, 1997. Princeton, NJ: Princeton University Press, pp. 69-94.  For source code in Pascal and raw data see

            Axelrod, Robert, Will Mitchell, Robert E. Thomas, D. Scott Bennett, and Erhard Bruderer, 1995. "Coalition Formation in Standard-Setting Alliances,", Management Science, 41, pp. 1493-1508. Reprinted in Robert Axelrod, The Complexity of Cooperation: Agent-Based Models of Competition and Collaboration, 1997. (Princeton, NJ: Princeton University Press). This is another test of Axelrod and Bennett’s landscape model. For source code in Pascal and raw data see



“Cousins” of Complexity.


           A.  Chaos

           Gleick, James, Chaos: Making a New Science, 1987.  New York and London: Penguin Books, pp. 1-31, and 98-9.

            Crutchfield, James P. et al., 1986. "Chaos," Scientific American, 255, December, pp. 46-57.

           Williams, Garnett P. 1997. Chaos Theory Tamed, Washington, DC: Joseph Henry Press, 1997, pp. 161-73. On the logistic equation and control parameters.


           B. Self-Organized Criticality

           Bak, Per and Kan Chen, January 1991. "Self-Organized Criticality," Scientific American, pp. 46-53.

           Bak, Per. 1996.  How Nature Works: The Science of Self-Organized Criticality. New York: Springer-Verlag, pp. 1-32, 135-43, and 183-92.


           C. Cellular Automata

           Callahan, Paul, “What is the Game of Life?”  (Read the text, and run the simulations, and study the R-pentomino.)  An example of a cellular automata, in contrast to an agent-based model.

           Wolfram, Stephen, A New Kind of Science, 2002. Campaign, IL: Wolfram Media.


           D. Neural Nets

           Coveney, Peter and Roger Highfield, 1995. Frontiers of Complexity: the Search for Order in a Chaotic World, New York: Fawcett Columbine, pp. 130-149 on neural nets.

           Bailey, James, 1996. After Thought: The Computer Challenge to Human Intelligence.  New York: Basic Books, pp. 127-35.

            Chellapilla, Kumar and David B. Fogelm September 1999, “Evolution, neural networks, games, and intelligence.” Proceedings of the IEEE, 87(9):1471--1496.




Books on Agent-Based Modeling

           (For books on this and related sub see )


           Axelrod, Robert, 1997. Complexity of Cooperation, Princeton, NJ: Princeton University Press. Chapters on seven models, each with an introduction about their origin and reception.

           Bonabeau, Eric, Marco Dorigo, and Guy Theraulaz, 1999. Swarm Intelligence: From Natural to Artificial Systems, Oxford: Oxford University Press.

           Casteelfranchi, Cristiano and Eric Werner (eds.), 1994. Artificial Social Systems, Berlin and New York: Springer-Verlag.

           Conte, Rosario Rainer Hegselmann and Pietro Terna (eds.), 1997. Simulating Social Phenomena. Berlin: Springer.

           E. Hillebrand and J. Stender (eds.), 1994. Many-Agent Simulation and Artificial Life. Amsterdam, Oxford and Washington: IOS Press.

           Gilbert, Nigel  and Rosaria Conte (eds.), 1995. Artificial Societies: the Computer Simulation of Social Life. London: UCL Press.

           Gilbert, Nigel and Jim Doran (eds.), 1994. Simulating Societies: The Computer Simulation of Social Phenomena. London: UCL Press.

           Gumerman, George  (eds.), 1994. Understanding Complexity in the Prehistoric Southwest.  Reading, MA: Addison-Wesley.

           Schelling, Thomas, 1978. Micromotives and Macro Behavior, NY: Norton.

           Sigmund, Karl. 1993. Games of Life: Explorations in Ecology, Evolution, and Behavior. Oxford: Oxford University Press.


                                                           Web Sites


A. Complexity News


            Complexity Digest is a weekly report covering all aspects of complexity. It typically has abstracts of about 20 articles, as well as news about conferences and opportunities. You can read it on-line at   or you can have it e-mailed to you each week.



B. Reference material


1.Leigh Tesfatsion maintains a very comprehensive site for agent-based modeling in the social sciences. It includes a list of interested faculty, on-line journals, concepts


           2. Material on evolutionary theories in the social sciences:


           3. Special issues of The Computational Economics and The Journal of

Economic Dynamics and Control are devoted to agent-based models. Their introductions are available at


4. Annotated bibliography of Prisoner's Dilemma research:


5. A web-based journal is Journal of Artificial Societies and Social Simulation at



C. Opportunities


            1. UM’s Center for the Study of Complexity has many local activities.


           2. Many  national and international workshops and meetings on complexity are available. See   or the Complexity Digest  


           3. The Santa Fe Institute is the world center for the study of complexity. See  For example, each summer SFI offers intensive programs for advanced graduate students.  One is for all fields (application deadline has been in January of each year), and one is specifically for economic and closely related fields, the Santa Fe Graduate Workshop in Economics (application deadline not yet set, but Professor Scott Page of UM should know the details).


           4. The Society of Computational Economics sponsors an annual contest for outstanding research manuscripts in computational economics written by graduate students. The contest is open to graduate students worldwide working on any aspect of computational economics. See


D. Specific Models


1. Documentation and source code of material in Axelrod's Complexity of Cooperation:


           2. Mathematica source code from Gaylord, Richard J. and Louis J. D’Andria, 1998. Simulating Society: A Mathematica Toolkit for Modeling Socioeconomic Behavior (NY):


            3. Runnable models (with source code in Java):


                       a. from Brookings’ Center on Social and Economic Dynamics using Ascape. This includes many models. Some of these will be described in the Dec. 2001 issue of Atlantic Monthly.  See


                       b. from University of Chicago group, using RePast. This includes Epstein and Axtell’s sugarscape model:


                       c. from Marshall Van Alstyne, School of Information, University of Michigan, an information diffusion simulator for agent societies. It includes tutorials on several works by March and by Watts.  It just requires Java running under IE.


                        d.   Axtell, “The Emergence of Firms in a Population of Agents: Local Increasing Returns, Unstable Nash Equilibria, and Power Law Size Distributions,” Brooking Institutions, CSED Working Paper No. 3, June 1999. A commentary in Nature is at  The paper itself with a Java simulation is at