An Introduction to Agent Based Modeling and Agent Based Computational Economics; A Simple Model for Markets Where Consumers are Imperfectly Informed

Document Type : Research Paper

Authors

Associate Professor of Economics, Allameh Tabataba’i University

Abstract

This paper introduces Agent Based Modeling (ABM) approach in social sciences especially in economics. By comparing the restrictions of mainstream economic models to those of agent based models we explain the benefits and capacities of using agent based modeling in economics named Agent based Computational Economics (ACE). Due to this restrictions and over simplifying assumptions in traditional models, sometimes economic policy makers cannot trust the results of these models because those assumptions caused them to be far from reality especially in case of abnormality and non-equilibrium. ACE is one of the so many efforts of economist to reduce this gap between economic models and economic realities. We enumerate some of the economic issues such as non-equilibrium, heterogeneity, learning and bounded rationality, nonlinearity, local interaction, network structure, complexity, aggregation failure and holism, and we explain the capacities of ACE to face with each of these issues. Finally we present a simple agent based model to deal with Markets Where Consumers are imperfectly informed about products quality

Keywords


  1. Arthur, W. B. (2006). Chapter 32 Out-of-Equilibrium Economics and Agent-Based Modeling. Handbook of Computational Economics. 2:1551-1564.
  2. Axtell, R. (2003). Economics as Distributed Computation. Meeting the Challenge of Social Problems via Agent-Based Simulation: Post-Proceedings of the Second International Workshop on Agent-Based Approaches in Economic and Social Complex Systems. Springer Japan3-23.
  3. Bedau, M. A. (2002). weak emergence. Principia, 6(1): 5
  4. Blume, L. E. and S. N. Durlauf (2005). The Economy As an Evolving Complex System, III: Current Perspectives and Future Directions, Oxford University Press.
  5. Epstein, J. M. (2006). Remarks on the foundations of agent-based generative social science. Handbook of computational economics, 2: 1585-1604.
  6. Gode, D. K. and S. Sunder (1993). Allocative Efficiency of Markets with Zero-Intelligence Traders: Market as a Partial Substitute for Individual Rationality. Journal of Political Economy 101(1): 119-137.
  7. Gourieroux, C. and A. Monfort (1997). Simulation-based Econometric Methods, Oxford University Press.
  8. Hofstadter, D. R. (1979). Godel, Escher, Bach: An Eternal Golden Braid, Basic Books, Inc.
  9. Holland, J. H. (1995). Hidden order: how adaptation builds complexity, Addison Wesley Longman Publishing Co., Inc.
  10. Kirman, A. P. (1992). Whom or What Does the Representative Individual Represent? The Journal of Economic Perspectives, 6(2): 117-136.
  11. Page, S. E. (2012). Aggregation in agent-based models of economies. The Knowledge Engineering Review, 27(2): 151-162.
  12. Richiardi, M., R. Leombruni, N. J. Saam and M. Sonnessa (2006). A Common Protocol for Agent-Based Social Simulation. Journal of Artificial Societies and Social Simulation, 9(1)..
  13. Richiardi, M. G. (2012). Agent-based computational economics: a short introduction. The Knowledge Engineering, 27(2): 137-149.
  14. Richiardi, M. G. (2017). The Future of Agent-Based Modeling. Eastern Economic Journal, 43(2): 271-287.
  15. Smallwood, D. E. and J. Conlisk (1979). Product Quality in Markets Where Consumers are Imperfectly Informed. The Quarterly Journal of Economics, 93(1): 1-23.
  16. Tesfatsion, L. J., K. L. (eds) (2006). Handbook of Computational Economics, Elsevier.