Introduction to Agent-based Computational Economics
The course, Introduction to Agent-based Computational Economics, The readings for the classes are available at the links below.
Simulation of social interactions focuses on the complex adaptive behavior that emerges in social systems. To better understand the behavior of such complex adaptive social systems, "artificial worlds" composed of interacting adaptive agents can be created and analyzed. Such models often exhibit properties that are strikingly similar to the actual social world, e.g., cooperation, social norms, and social stratification into different classes, and provide a unique window into understanding such phenomena. Using simulation methods, previously inaccessible, yet fundamental, questions are now becoming amenable to analysis. There is much research to be done in this area---along with creating and understanding these types of complex systems, efforts need to be directed toward developing accessible versions of these models for the classroom.
- Subject Outline (PDF)
- Text: Nigel Gilbert and Klaus G. Troitzsch, Simulation for the Social Scientist, Buckingham: Open University Press, 2nd edition, 2005. See its web site here.
- A glossary of terms encountered in this course is here.
- An informative reading on the Monte Carlo method here.
- NetLogo is a programmable environment for exploring the workings of decentralised systems, specifically designed to be user-friendly for students: here. (NetLogo 3.0 was released on September 15, 2005.)
- Class 1: Introduction,
Simulations: concepts and grand challenges.
G&T: Ch 1 and 2.
Bob's lecture overheads, after March & Lave: OHs || Printable
- Andrea Schertler reviews Simulation for the Social Scientist by Gilbert and Troitzsch, 1999, Here.
- Axelrod: Advancing the art of simulation in the social sciences (revised 2003). Here
The Replication of Eight Simulation Models from Axelrod 2003: Here.
- R. Axtell, R. Axelrod, J.M. Epstein, and M.D. Cohen, Aligning Simulation Models: A Case Study and Results, Computational & Mathematical Organization Theory 1(2): 123-141, 1996.
- James G. March, Charles A. Lave Introduction to Models in the Social Sciences, New York: HarperCollins, 1975. (handout)
OHs . || Printables.
- Kauffman, S., (1995). At Home in the Universe: The Search for Laws of Self-Organization and Complexity. Oxford and New York: Oxford University Press. See especially 252-64. (handout)
- Schelling, T. (1978). Micromotives and macrobehavior. New York: W. W. Norton. (See especially 137-55.) (handout)
- J.P. Marney and Heather F.E. Tarbert (2000), Why do simulation? Towards a working epistemology for practitioners of the dark arts, Journal of Artificial Societies and Social Simulation vol. 3, no. 4, , Here.
- Pietro Terna reviews Dynamics of Organizations: Computational Modeling and Organization Theories, edited by Lomi and Larsen, Cambridge, MA: The M. I. T. Press, 2001 Here.
- Paul E. Johnson reviews Tools and Techniques for Social Science Simulation, edited by Suleiman, Troitzsch & Gilbert, 2000. Here.
- Robert Marks, Simulating economics, Australian Journal of Management, Vol. 27, No. 1, June 2002, pp. i-iii. Here.
- Robert Marks, Models rule, Australian Journal of Management, Vol. 28, No. 1, June 2003, pp. i-iii. Here. Here.
- Scott Moss, Canonical Task Environments for Social Simulation: Here.
- Class 2: Shayne Garry and System Dynamics in the social sciences.
G&T: Ch 3.
I would suggest you read the chapters/articles in the following order. The session is intended to give you a broad overview of: 1) What are the defining characteristics of system dynamics as a modeling approach and 2) How has system dynamics modeling been used to advance and test theory in management research.
- Meadows D. (1980). The Unavoidable A Priori. In Elements of the System Dynamics Method. J. Randers. Cambridge, MA, Productivity Press: 23-57. (handout) Dynamics of Innovation Implementation. Organization Science, 13(2), 109-127. (handout)
- Oliva, R., & Sterman, J. D. (2001). Cutting corners and working overtime: quality erosion in the service industry. Management Science, 47(7), 894-914. (handout)
- Download the model Here.
- Paiche, M., & Sterman, J. D. (1993). Boom, Bust, and failures to learn in experimental markets. Management Science, 39(12), 1439-1458. (handout)
- John H. Miller, Active non-linear tests (ANTs) of complex simulation models, Management Science, 44(6): 820-830, 1998. Here.
- You can access the models used in articles 2 and 3 on the course web sites. You can open both models using the Vensim modeling software which can be downloaded free for educational purposes at the following web site:
Download a free version of Vensim PLE software: Here.
- Class 3: Micro-analysis and Cellular automata.
G&T: Ch 4 and 7.
- Sauerbier, T. (2002), UMDBS - A new tool for dynamic microsimulation, Journal of Artificial Societies and Social Simulation, vol. 5, no. 2: Here.
- Laurie Brown and Ann Harding (2002), Social Modelling and Public Policy: Application of Microsimulation Modelling in Australia, Journal of Artificial Societies and Social Simulation, vol. 5, no. 4 Here.
- Rainer Hegselmann and Andreas Flache (1998), Understanding Complex Social Dynamics: A Plea For Cellular-Automata-Based Modelling, Journal of Artificial Societies and Social Simulation, vol. 1, no. 3: Here.
- Phillip Bonacich, The Evolution of Exchange Networks: A Simulation Study, Journal of Social Structure vol. 2, no. 5, 2001, Here.
- Parker, D. C. 1999. Landscape Outcomes in a Model of Edge-Effect Externalities: A Computational Economics Approach. Santa Fe, NM: Santa Fe Institute Publication 99-07-051 E. Here.
- Hegselmann reviews Simulating Society - A Mathematica Toolkit for Modeling Socioeconomic Behavior by Richard J. Gaylord and Lou D'Andria (NY: TELOS/Springer Verlag), 1998: AGSM 302.0113/1 Here.
"The models in the book are basically two-dimensional cellular automata. What the book is about therefore is, how to do cellular-automata-based modelling of social dynamics with Mathematica."
- Edmund Chattoe's Cellular Automata Bibliography: Here.
- Play the Game of Life on-line: Here.
- Class 4: Agent-based models.
G&T: Ch 8 and 9.
- Robert Axelrod and Leigh Tesfatsion, On-Line Guide for Newcomers to Agent-Based Modeling in the Social Sciences Here.
- Pietro Terna (2001), Creating Artificial Worlds: A Note on Sugarscape and Two Comments, Journal of Artificial Societies and Social Simulation 4(2). Here.
- Gilbert, N., Terna P., How to build and use agent-based models in social science, Here.
- Sallach D.L., Macal C.M., Introduction: The Simulation of Social Agents, Social Science Computer Review, 19(3): 245-248, August 2001.
- Robert Axtell: Why agents? On the varied motivations for agent computing in the social sciences, Center on Social and Economic Dynamics Working Paper No. 17, November 2000, in Agent Simulation: Applications, Models, and Tools, 1999. Here.
- Marks: On Market Design using Agent-Based Models. (2004) Here
- GulyÁs, L. On the Transition to Agent-Based Modeling: Implementation Strategies From Variables to Agents, Social Science Computer Review 20(4): 389-399, 2002.
- Bankes, S. Lempert, R. Popper, S., Making Computational Social Science Effective: Epistemology, Methodology, and Technology, Social Science Computer Review 20(4): 377-388, 2002.
- Adaptive Agents, Intelligence, and Emergent Human Organization: Capturing Complexity through Agent-Based Modeling: a supplement of the PNAS 99(3): May, 2002: Contents listed here.
- The Icosystem Game, a simple agent-based simulation to demonstrate emergent behaviour.
- SimWorld Ltd, a UK consulting company that includes "Innovative Business Solutions in Forecasting, Optimisation and Planning. Neural Nets, Genetic Algorithms, Fuzzy Logic and Complex Adaptive Systems Simulations."
- Class 5: Learning and evolutionary models: Genetic Algorithms and Neural Nets.
G&T: Ch 10.
- Marks, R.E. (1988) Niche strategies: the Prisoner's Dilemma computer tournaments revisited, Here.
- Marks, R.E. (1992) Breeding optimal strategies: optimal behaviour for oligopolists, Journal of Evolutionary Economics, 2: 17-38. Here.
- Midgley, D.F., Marks, R.E., and Cooper, L.G. (1997) Breeding competitive strategies, Management Science, 43(3): 257-275. [The lead article.]
- Marks, R.E. (1998), Evolved perception and behaviour in oligopolies, Journal of Economic Dynamics and Control. 22(8-9): 1209-1233. Here.
- Robert Hoffmann (2000), Twenty Years on: The Evolution of Cooperation Revisited, Journal of Artificial Societies and Social Simulation vol. 3, no. 2, Here.
- Marks reviews Neural Networks for Economic and Financial Modelling, by A. Beltratti, S. Margarita and P. Terna, London: International Thomson Computer Press, 1996. Here.
- Daniel John Zizzo reviews Neural Networks: An Introductory Guide for Social Scientists by G. David Garson, London: Sage Publications, 1998: Here.
- The Uses of Genetic Programming in Social Simulation: A Review of Five Books by Bruce Edmonds, Here.
- Carl Henning Reschke (2001), Evolutionary Perspectives on Simulations of Social Systems, Journal of Artificial Societies and Social Simulation vol. 4, no. 4 Here.
- Nicolaas J. Vriend, An illustration of the essential difference between individual and social learning, and its consequences for computational analyses, Journal of Economic Dynamics & Control, vol, 24: 1-19, 2000 Here.
- N.N. Schraudolf & J.J. Grefenstette, A User's Guide to GAucsd 1.4, Technical Report CS92-249, CSE Department, UC San Diego, 1992 Here.
- Robert Tobias and Carole Hofmann (2004), Evaluation of free Java-libraries for social-scientific agent based simulation, Journal of Artificial Societies and Social Simulation vol. 7, no. 1 Here.
- Dugdale's An Evaluation of Seven Software Simulation Tools for Use in the Social Sciences: Here.
- Complete text of Dynamics of Complex Systems, by Yaneer Bar-Yam, (Westview, 2003) in PDF Here.
- J. Padgett's Santa Fe Institute projects Here.
- Sample NSF Grant Application on "Human and Social Dynamics of Renaissance Florence": Here.
- Some Guidelines from the NSF Here.
- Leigh Tesfatsion's Computational Laboratories and links: Here.
- Leigh Tesfatsion's links to other simulation courses: Here.
- Modelling and Simulation -- Koblenz: Here.
- 2005 Koblenz seminar: Here.
- Simulating ancient settlement systems: Here.
- Jeff Brantingham's use of agent-based modelling in archæology. Here.
- The Journal of Artificial Societies and Social Simulation: Here
- Social Science Computer Review: Here and here.
- Computational & Mathematical Organization Theory Here.
- The University of Michigan's Center for the Study of Complex Systems Computer Lab documentation: Here.
- Applications of AI to the Social Sciences. Here.
- Centre for Research on Simulation in the Social Sciences Here.
- Edmund Chattoe's Simulation Bibliography: Here.