Rationality vs. Learning in the Evolution of Solidarity Networks: A Theoretical Comparison

  • Andreas Flache
  • Rainer Hegselmann
Article

Abstract

In this paper we analyze the evolution of solidarity relations between dissimilar actors by means of a cellular automaton framework. We assume that actors face two types of decisions in the course of an iterated game. First, actors&2018; solidarity decisions constitute mutual support relations between neighbors. Second, by migrating in a two dimensional world, actors select between potential solidarity partners. Moreover, actors are dissimilar with respect to their neediness class, i.e., their need for help. Hegselmann (1996) demonstrated by computer simulation that under these assumptions the behavior of (boundedly) rational egoists may lead to the emergence of a solidarity network that is characterized by class segregation. In the present paper, we explore whether the macro phenomenon of segregation depends on the micro assumption of rationality. We replace Hegselmann&2018;s rational egoist by an adaptive egoist, who takes solidarity and migration decisions on basis of the &2018;law of effect&2019;. A stochastic learning model (e.g., Flache and Macy, 1996) is used to simulate adaptive decision making. Our model of learning behavior, we show, entails the emergence of class segregated solidarity networks. At the same time, however, the evolving networks are considerably more fragile and less extended than those arising amongst rational egoists. While critics of the rational choice approach often argue that rational egoist models tend to underestimate the level of social solidarity, we showed that in this particular analysis relaxing the assumption of rationality may entail the prediction of less rather than more solidarity.

social dilemmas solidarity computer simulation cellular modelling micro foundations 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Bush, R.R. and F. Mosteller (1955), Stochastic Models for Learning. Wiley: New York.Google Scholar
  2. Dawes, R.M. and R.H. Thaler (1988), "Anomalies: Cooperation," Journal of Economic Perspectives, 2, 187–197.Google Scholar
  3. Duncan, O.D. and B. Duncan (1955), "A Methodological Analysis of Segregation Indices," American Sociological Review, 2, 210–217.Google Scholar
  4. Flache, A. (1996), The Double Edge of Networks: An Analysis of the Effect of Informal Networks on Cooperation in Social Dilemmas. Amsterdam: Thesis Publishers.Google Scholar
  5. Flache, A. and M.W. Macy (1996), "The Weakness of Strong Ties: Collective Action Failure in a Highly Cohesive Group," Journal of Mathematical Sociology, 21, 3–28.Google Scholar
  6. Friedman, M. (1953), Essays in Positive Economics. Chicago: University of Chicago Press.Google Scholar
  7. Friedman, J.W. (1986), Game Theory with Applications to Economics (2nd edition 1991). Oxford: Oxford University Press.Google Scholar
  8. Hechter, M. (1992), "The Insufficiency of Game Theory for the Solution of Real-World Collective Action Problems," Rationality and Society, 4, 33–40.Google Scholar
  9. Hegselmann, R. (1994), "Zur Selbstorganisation von Solidarnetzwerken unter Ungleichen—Ein Simulationsmodell," in K. Homann (Eds.) Wirtschaftsethische Perspektiven I—Theorie, Ordnungsfragen, Internationale Institutionen, Berlin: Duncker & Humblot, pp. 105–129.Google Scholar
  10. Hegselmann, R. (1996), "Cellular Automata in the Social Sciences—;Perspectives, Restrictions, and Artefacts," in R. Hegselmann, K.G. Troitzsch and U. Mueller (Eds.) Modeling and Simulation in the Social Sciences from the Philosophy of Science Point of View (Theory and Decision Library), Dordrecht: Kluwer, pp. 209–234.Google Scholar
  11. Hegselmann, R. (1996a), "Social Dilemmas in Lineland and Flatland," in W.B.G. Liebrand and D. Messick (Eds.) Frontiers in Social Dilemma Research, Berlin: Springer, pp. 337–362.Google Scholar
  12. Hegselmann, R. and A. Flache (1998), "Understanding Complex Social Dynamics: A Plea for Cellular Automta Based Modelling," Journal of Artificial Societies and Social Simulation, 1: http://www.soc.surrey.ac.uk/JASSS/1/3/1.htmlGoogle Scholar
  13. Laumann, E.O. (1973), Bonds of Pluralism. New York.Google Scholar
  14. Macy, M.W. (1990), "Learning Theory and the Logic of Critical Mass," American Sociological Review, 55, 809–826.Google Scholar
  15. Macy, M.W. (1991), "Learning to Cooperate: Stochastic and Tacit Collusion in Social Exchange," American Journal of Sociology, 97, 808–843.Google Scholar
  16. Macy, M.W. (1993), "Backward Looking Social Control," American Sociological Review, 58, 819–836.Google Scholar
  17. Macy, M.W. and A. Flache (1995), "Beyond Rationality in Models of Choice," Annual Review of Sociology, 21, 73–91.Google Scholar
  18. Roth, A. (1992), "Game Theory as Part of Empirical Economics," in J.D. Hey (Ed.) The Future of Economics, Cambridge: Blackwell, pp. 107–114.Google Scholar
  19. Schelling, T. (1971), "Dynamic Models of Segregation," Journal of Mathematical Sociology, 1, 143–186.Google Scholar
  20. Taylor, M. (1987), The Possibility of Cooperation (revised edition of: Anarchy and cooperation, 1976). London: Wiley & Sons.Google Scholar

Copyright information

© Kluwer Academic Publishers 1999

Authors and Affiliations

  • Andreas Flache
    • 1
  • Rainer Hegselmann
    • 1
  1. 1.Department of PhilosophyUniversity of BayreuthBayreuthGermany

Personalised recommendations