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Population Learning in a Model with Random Payoff Landscapes and Endogenous Networks

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Author Info

  • Giorgio Fagiolo

    ()

  • Luigi Marengo
  • Marco Valente

Abstract

Population learning in dynamic economies with endogenous network formation has been traditionally studied in basic settings where agents face quite simple and predictable strategic situations (e.g. coordination). In this paper, we start instead to explore economies where the payoff landscape is very complicated (rugged). We propose a model where the payoff to any agent changes in an unpredictable way as soon as any small variation in the strategy configuration within its network occurs. We study population learning where agents: (i) are allowed to periodically adjust both the strategy they play in the game and their interaction network; (ii) employ some simple criteria (e.g. statistics such as MIN, MAX, MEAN, etc.) to myopically form expectations about their payoff under alternative strategy and network configurations. Computer simulations show that: (i) allowing for endogenous networks implies higher average payoff as compared to static networks; (ii) populations learn by employing network updating as a “global learning” device, while strategy updating is used to perform “fine tuning”; (iii) the statistics employed to evaluate payoffs strongly affect the efficiency of the system, i.e. convergence to a unique (multiple) steady-state(s); (iv) for some class of statistics (e.g. MIN or MAX), the likelihood of efficient population learning strongly depends on whether agents are change-averse in discriminating between options associated to the same expected payoff. Copyright Springer Science + Business Media, Inc. 2005

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File URL: http://hdl.handle.net/10.1007/s10614-005-6160-5
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Bibliographic Info

Article provided by Society for Computational Economics in its journal Computational Economics.

Volume (Year): 24 (2005)
Issue (Month): 4 (June)
Pages: 383-408

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Handle: RePEc:kap:compec:v:24:y:2005:i:4:p:383-408

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Web page: http://www.springerlink.com/link.asp?id=100248
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Related research

Keywords: adaptive expectations; dynamic population games; endogenous networks; fitness landscapes; population learning;

References

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  1. Fernando Vega Redondo & Sanjeev Goyal, 2001. "Learning, Network Formation And Coordination," Working Papers. Serie AD 2001-19, Instituto Valenciano de Investigaciones Económicas, S.A. (Ivie).
  2. Jackson, Matthew O. & Watts, Alison, 2002. "On the formation of interaction networks in social coordination games," Games and Economic Behavior, Elsevier, vol. 41(2), pages 265-291, November.
  3. Blume Lawrence E., 1993. "The Statistical Mechanics of Strategic Interaction," Games and Economic Behavior, Elsevier, vol. 5(3), pages 387-424, July.
  4. Fagiolo, Giorgio, 2005. "Endogenous neighborhood formation in a local coordination model with negative network externalities," Journal of Economic Dynamics and Control, Elsevier, vol. 29(1-2), pages 297-319, January.
  5. Alan Kirman, 1997. "The economy as an evolving network," Journal of Evolutionary Economics, Springer, vol. 7(4), pages 339-353.
  6. Brock,W.A. & Durlauf,S.N., 2000. "Discrete choice with social interactions," Working papers 7, Wisconsin Madison - Social Systems.
  7. Edward Droste & Robert P. Gilles & Cathleen Johnson, 2000. "Evolution of Conventions in Endogenous Social Networks," Econometric Society World Congress 2000 Contributed Papers 0594, Econometric Society.
  8. Glen Ellison, 2010. "Learning, Local Interaction, and Coordination," Levine's Working Paper Archive 391, David K. Levine.
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Cited by:
  1. Sylvie Geisendorf, 2010. "Searching NK Fitness Landscapes: On the Trade Off Between Speed and Quality in Complex Problem Solving," Computational Economics, Society for Computational Economics, vol. 35(4), pages 395-406, April.

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