Modelling Bounded Rationality in Agent-Based Simulations Using the Evolution of Mental Models

  • Bruce Edmonds
Part of the Advances in Computational Economics book series (AICE, volume 11)


There are many possible ways of modelling economic agents. These traditionally fall into one of two camps, dating from Simon’s distinction between substantive and procedural rationality: this is often characterised as those with bounded rationality and those with no such bounds (although this is not strictly correct, Moss & Sent forthcoming). Although the latter type is more analytically tractable we are interested in the former type.


Utility Function Internal Model Economic Agent Modelling Agent Bounded Rationality 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Akiyama, E. and K. Kaneko: 1996, ‘Evolution of Cooperation, Differentiation, Complexity, and Diversity in an Iterated Three-person Game’. Artificial Life 2, 293–304.CrossRefGoogle Scholar
  2. Angeline, P. and K. E. Kinnear: 1996, Advances in Genetic Programming, Vol.2. Cambridge, MA: MIT Press.Google Scholar
  3. Arifovic, J.: 1994, ‘Genetic Algorithm Learning and the Cobweb Model’. Journal of Economic Dynamics and Control 18, 3–28.CrossRefGoogle Scholar
  4. Arthur, B.: 1994, ‘Inductive Reasoning and Bounded Rationality’. American Economic Association Papers 84, 406–411.Google Scholar
  5. Axelrod, R.: 1984, The Evolution of Cooperation. New York: Basic Books.Google Scholar
  6. Dosi, G., L. Marengo, A. Bassanini & M. Valente: forthcoming, ‘Norms as Emergent Properties of Adaptive Learning’. Journal of Evolutionary Economics,.Google Scholar
  7. Edmonds, B.: 1998a, ‘Meta-Genetic Programming: co-evolving the genetic operators’. CPM Report 98–32, MMU, Manchester, UK. ( Scholar
  8. Edmonds, B.: 1998b, ‘Modelling Socially Intelligent Agents’. Applied Artificial Intelligence 12, 677–699.CrossRefGoogle Scholar
  9. Elman, J.L.: 1993, ‘Learning and Development in Neural Networks - The Importance of Starting Small’. Cognition 48, 71–99.CrossRefGoogle Scholar
  10. Gaylard, H.: 1996, ‘A Cognitive Approach to Modelling Structural Change’. CPM Report 96–20, MMU, Manchester, UK.Google Scholar
  11. Holland, J. H.: 1992, Adaptation in Natural and Artificial Systems, 2nd Ed.. Cambridge, MA: MIT Press.Google Scholar
  12. Kaneko, K.: 1990, ‘Globally Coupled Chaos Violates the Law of Large Numbers but not the Central Limit Theorem’. Physics Review Letters 65, 1391–1394.CrossRefGoogle Scholar
  13. Kinnear, K. E. (ed.): 1994, Advances in Genetic Programming. Cambridge, MA: MIT Press.Google Scholar
  14. Koza, J. R.: 1992, Genetic Programming: On the Programming of Computers by Means of Natural Selection. Cambridge, MA: MIT Press.Google Scholar
  15. Montana, D. J.: 1995, ‘Strongly Typed Genetic Programming’. Evolutionary Computation 3, 199–230.CrossRefGoogle Scholar
  16. Moss, S. J. and B. Edmonds: 1998, ‘Modelling Economic Learning as Modelling’. Cybernetics and Systems 29, 5–37.CrossRefGoogle Scholar
  17. Moss, S. J., H. Gaylard, S. Wallis, and B. Edmonds: 1998, ‘SDML: A Multi-Agent Language for Organizational Modelling’. Computational and Mathematical Organization Theory 4, 43–69.CrossRefGoogle Scholar
  18. Moss, S. and Sent, E-M.: 1998, ‘Boundedly versus Procedurally Rational Expectations’. In: Hallet, H and McAdam, P. (eds.), New Directions in Macro Economic Modelling,: Kluwer, pp..Google Scholar
  19. Palmer, R.G. et. al.: 1994, ‘Artificial Economic Life - A simple model of a stockmarket’. Physica D 75,264–274.CrossRefGoogle Scholar
  20. Penrose, E.: 1972, The Growth of the Firm. Oxford: Blackwell.Google Scholar
  21. Prügel-Bennett, A. and J. L. Shapiro: 1994, ‘An Analysis of Genetic Algorithms Using Statistical Mechanics’. Physical Review Letters 72, 1305–1309.CrossRefGoogle Scholar
  22. Vriend, N.J.: 1995, ‘Self-organization of markets: an example of a computational approach’. Computational Economics 8, 205–232.CrossRefGoogle Scholar
  23. Zambrano, E.: 1997, ‘The Revelation Principle of Bounded Rationality’. Sante Fe working paper 97–06–060, New Mexico, USA.Google Scholar

Copyright information

© Springer Science+Business Media New York 1999

Authors and Affiliations

  • Bruce Edmonds

There are no affiliations available

Personalised recommendations