A Genetic Algorithms Approach: Social Aggregation and Learning with Heterogeneous Agents

  • Davide Fiaschi
  • Pier Mario Pacini
Part of the Lecture Notes in Economics and Mathematical Systems book series (LNE, volume 551)

Summary

We analyze an economy in which increasing returns to scale incentivate social aggregation in a population of heterogeneous boundedly rational agents; however these incentives are limited by the presence of imperfect information on others’ actions. We show by simulations that the equilibrium coalitional structure strongly depends on agents’ initial beliefs and on the characteristics of the individual learning process that is modeled by means of genetic algorithms. The most efficient coalition structure is reached starting from a very limited set of initial beliefs. Furthermore we find that (a) the overall efficiency is an increasing function of agents’ computational abilities; (b) an increase in the speed of the learning process can have ambiguous effects; (c) imitation can play a role only when computational abilities are limited.

Key words

Coalition formation Learning Genetic Algorithms Increasing returns to scale Numerical simulations 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Anderlini L, H Sabourian (1995) Cooperation and Effective Computability. Econometrica 63:1337–1369MathSciNetGoogle Scholar
  2. 2.
    Arifovic J (1994) Genetic Algorithm Learning and the Cobweb Model. Journal of Economic Dynamics and Control 18:3–28CrossRefMATHGoogle Scholar
  3. 3.
    Bemheim BD, Peleg B, Whinston MD (1987) Coalition-Proof Nash Equilibria: Concepts. Journal of Economic Theory 42:1–12MathSciNetGoogle Scholar
  4. 4.
    Birchenhall CR (1996) Evolutionary Games and Genetic Algorithms. In: Gill’ M. (ed) Computational Economic Systems. Kluwer Academic PublishersGoogle Scholar
  5. 5.
    Farrell J, Scotchmer S (1988) Partnerships Quarterly. Journal of Economics 103:279–97MathSciNetGoogle Scholar
  6. 6.
    Fiaschi D, Pacini PM (2003) Coalition Formation with Boundedly Rational Agents. Forthcoming in: Kirman A, Marsili M, Gallegati M. (eds) The Complex Dynamics of Economic Interaction. Lectures Notes in Economics and Mathematical System. Springer, BerlinGoogle Scholar
  7. 7.
    Fiaschi D, Pacini PM (1998) Endogenous Coalition Formation with Identical Agents. Working paper n. 308 (Progetto dAteneo: Leconomia italiana e la sua collocazione internazionale: una redifinizione delle politiche di wel-fare edelloccupazione per una pi’u efficiente crescita economica). University of BolognaGoogle Scholar
  8. 8.
    Guesnerie R, Oddou C. (1988) Increasing Returns to Size and Their Limits. Scandinavian Journal of Economics 90:259–73Google Scholar
  9. 9.
    Holland JH, Holyoak KJ, Nisbett RE (1986) Induction: Processes of Inference, Learning and Discovery. MIT Press, Cambridge, USAGoogle Scholar
  10. 10.
    Holland JH, Miller JH (1991) Artificial Adaptive Agents in Economic Theory. American Economic Review Papers and Proceeding 81:365–70Google Scholar
  11. 11.
    Mailath GJ (1998) Do People Play Nash Equilibrium? Lessons From Evolutionary Game Theory. Journal of Economic Literature 36:1347–1374Google Scholar
  12. 12.
    Marimon R (1996) Learning from Learning in Economics. Working Paper N. 96/12, European University Institute, FlorenceGoogle Scholar
  13. 13.
    Young, H. Peyton and Dean Foster (1991) Cooperation in the Short and in the Long Run. Games and Economic Behavior 3:145–56Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Davide Fiaschi
    • 1
  • Pier Mario Pacini
    • 1
  1. 1.Department of EconomicsUniversity of PisaPisa

Personalised recommendations