Workshop: Introduction to Modelling and Simulation
The Eighth Asia Pacific Complex Systems Conference
Simulation of social interactions focuses on the patterns that emerge in social systems of interacting agents. "Emergent" means not simply the agregation of individual agent's actions. To understand such "complex adaptive" systems, artificial societies composed of interacting adaptive agents can be created and analyzed. Such models can exhibit properties such as cooperation, social norms, and social stratification, and help understanding such phenomena. Using simulation, previously inaccessible questions are amenable to analysis. Such bottom-up simulations can also be used to design the rules of engagement, for instance in a new market. The workshop will cover theory and practice, with hands-on simulations.
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.
- Nigel Gilbert and Klaus G. Troitzsch, Simulation for the Social Scientist, Buckingham: Open University Press, 2nd edition, 2005. See its web site here.
- and also:
John H. Miller & Scott E. Page, Complex Adaptive Systems: An Introduction to Computational Models of Social Life, Princeton Uni. Press, 2007.
- A glossary of terms encountered in the lectures 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 3.1.3 was released in September, 2006.)
- The latest NetLogo User Manual and tutorials here.
- The NetLogo programs in Chapters 7-10 of Gilbert & Troitzsch here. (Run them from your browser, or download them.)
- NetLogo models contributed by the user community here.
- A NetLogo model of the Sons & Daughters (or Boys & Girls) model on page 69 of March & Lave, 1975. Here.
- Lecture 1: Introduction,
Simulations: concepts and grand challenges.
G&T: Ch 1 and 2.
Bob's lecture overheads, after March & Lave: OHs || Printable
- James G. March, Charles A. Lave Introduction to Models in the Social Sciences, New York: HarperCollins, 1975. Extract Here.
- Execute the NetLogo simulation of March & Lave's Boys & Girls model Here.
- Axelrod: Advancing the art of simulation in the social sciences (revised 2005). Here
The Replication of Eight Simulation Models from Axelrod 2003: Here.
- Marks, R.E. (2007) Validating Simulation Models: A General Framework and Four Applied Examples, forthcoming Computational Economics. Here.
- Midgley D.F., Marks R.E., and Kunchamwar D. (2007) The Building and Assurance of Agent-Based Models: An Example and Challenge to the Field, Journal of Business Research, 60: 884-893. Here.
- A. Hailu & S. Schilizzi, Are Auctions More Efficient Than Fixed Price Schemes When Bidders Learn? Australian Journal of Management, 29(2): 147-168, December 2004. Here
- Andrea Schertler reviews Simulation for the Social Scientist by Gilbert and Troitzsch, 1999, Here.
- R. M. Burton, Computational laboratories for Organizational Science: questions, validity and docking, Computational & Mathematical Organizational Theory 9: 91-108, 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. Here.
- 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.
- Schelling, T. (1978). Micromotives and macrobehavior. New York: W. W. Norton. (See especially 137-55.)
- 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.
- Scott Moss, Canonical Task Environments for Social Simulation: Here.
- Magda Fontana, Simulation in Economics: Evidence on Diffusion and Communication, Here.
- Nadav M. Shnerb, Yoram Louzoun, Eldad Bettelheim, and Sorin Solomon, The importance of being discrete: Life always wins on the surface, PNAS September 12, 2000, vol. 97, no. 19. Here.
- Lecture 2: Agent-based models.
G&T: Ch 8 and 9.
Bob's lecture overheads: OHs || Printable
- Agent-based simulation comes of age, OR/MS Today, August 2006, pp 34-38. Here.
- Robert Axelrod and Leigh Tesfatsion, On-Line Guide for Newcomers to Agent-Based Modeling in the Social Sciences, in Leigh Tesfatsion and Kenneth L. Judd (Eds.), Handbook of Computational Economics, Vol. 2: Agent-Based Computational Economics, Handbooks in Economics Series, North-Holland, Amsterdam, 2006. Here.
Web support materials (readings and demonstration software). Here.
Leigh's Trento materials Here.
- J.M. Epstein, Remarks on the Foundations of Agent-Based Generative Social Science, Handbook of Computational Economics, Volume 2: Agent-Based Modeling, edited by Leigh Tesfatsion and Kenneth L. Judd, Amsterdam: Elsevier Science, 2006. Here.
- John Miller and Scott Page (2004), The standing ovation problem, Complexity, vol 9 no 5: 8-16. Here.
- 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, including:
- E. Bonabeau, Agent-based modeling: Methods and techniques for simulating human systems, Proc. National Academy of Sciences, 99(suppl. 3): 7280-7287, 2002. Here.
- Epstein, Joshua M., John D. Steinbruner, and Miles T. Parker (2002), Modeling Civil Violence: An Agent-Based Computational Approach, Proceedings of the National Academy of Sciences, U.S.A., 99: 7243-7250. Here.
- Pietro Terna (2001), Creating Artificial Worlds: A Note on Sugarscape and Two Comments, Journal of Artificial Societies and Social Simulation 4(2). Here.
- Sallach D.L., Macal C.M., Introduction: The Simulation of Social Agents, Social Science Computer Review, 19(3): 245-248, August 2001. Here.
- 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: Market Design using Agent-Based Models, in Leigh Tesfatsion and Kenneth L. Judd (Eds.), Handbook of Computational Economics, Vol. 2: Agent-Based Computational Economics, Handbooks in Economics Series, North-Holland, Amsterdam, 2006. Here
- Duncan A. Robertson: Agent-Based Models of a Banking Network as an Example of a Turbulent Environment: The Deliberate vs. Emergent Strategy Debate Revisited, EMERGENCE, 5(2), 56-71, 2003. 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.
- Leigh Tesfatsion's information on application areas for agent-based model, from her course at Iowa State. Here.
- AnyLogic software Java demos on Social Dynamics. Here.
- Roberto Leombruni, Matteo Richiardi, Why are economists sceptical about agent-based simulations? Physica A, 355 (2005) 103-109.
- Gintis reviews Handbook of Computational Economics, Volume II: Agent-Based Computational Economics, Leigh Tesfatsion and Kenneth L. Judd (eds.) Elsevier/North-Holland: Amsterdam, 2006, Here.
- Lecture 3: Learning and evolutionary models: Genetic Algorithms and Neural Nets.
G&T: Ch 10.
Bob's lecture overheads: OHs || Printable
- Duffy J., Agent-Based Models and Human Subject Experiments, in Leigh Tesfatsion and Kenneth L. Judd (Eds.), Handbook of Computational Economics, Vol. 2: Agent-Based Computational Economics, Handbooks in Economics Series, North-Holland, Amsterdam, 2006. Here.
- Szpiro G., The emergence of risk aversion, Complexity, 2(4), 31-39, 1997. Here.
- 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.
- Marks R.E., Midgley D.F., Cooper L.G., 2006, Co-evolving better strategies in oligopolistic price wars, Here.
- Robert E. Marks, Hermann Schnabl, 1999, Genetic Algorithms and Neural Networks: A Comparison Based on the Repeated Prisoner's Dilemma, 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.
- Blake LeBaron, Building the Santa Fe Artificial Stock Market, 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.
- Some student projects from the First World Congress on Social Simulation. Here.
- Complete text of Dynamics of Complex Systems, by Yaneer Bar-Yam, (Westview, 2003) in PDF Here.
- Leigh Tesfatsion's web resources:
- Doctoral courses:
- J. Padgett's Santa Fe Institute projects Here.
- The Journal of Artificial Societies and Social Simulation: Here
- Social Science Computer Review: Here and here.
- Computational & Mathematical Organization Theory Here.
- The Journal of Economic Interaction and Coordination Here.
- The Electronic Journal of Evolutionary Modeling and Economic Dynamics (e-JEMED) Here.
- Emergence: Complexity & Organization 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.
- Philip Mirowski, Markets Come to Bits: Evolution, Computation and Markomata in Economic Science, Draft 1.3, November 2004. Here.
Last Updated 4 February, 2007 Robert Marks, firstname.lastname@example.org