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Endogenous Networks in Random Population Games

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  • Marco Valente
  • Giorgio Fagiolo
  • Luigi Marengo

Abstract

Population learning in dynamic economies has been traditionally studied in over-simplified settings where payoff landscapes are very smooth. Indeed, in these models, all agents play the same bilateral stage-game against any opponent and stage-game payoffs reflect very simple strategic situations (e.g. coordination). In this paper, we address a preliminary investigation of dynamic population games over `rugged' landscapes, where agents face a strong uncertainty about expected payoffs from bilateral interactions. We propose a simple model where individual payoffs from playing a binary action against everyone else are distributed as a i.i.d. U[0,1] r.v.. We call this setting a `random population game' and we study population adaptation over time when agents can update both actions and partners using deterministic, myopic, best reply rules. We assume that agents evaluate payoffs associated to networks where an agent is not linked with everyone else by using simple rules (i.e. statistics) computed on the distributions of payoffs associated to all possible action combinations performed by agents outside the interaction set. We investigate the long-run properties of the system by means of computer simulations. We show that: (i) allowing for endogenous networks implies higher average payoff as compared to "frozen" networks; (ii) the statistics employed to evaluate payoffs strongly affect the efficiency of the system, i.e. convergence to a unique (multiple) steady-state(s) or not; (iii) for some class of statistics (e.g. MIN or MAX), the likelihood of efficient population learning strongly depends on whether agents are change-averse or not in discriminating between options delivering the same expected payoff.

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Bibliographic Info

Paper provided by Society for Computational Economics in its series Computing in Economics and Finance 2003 with number 68.

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Date of creation: 01 Aug 2003
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Handle: RePEc:sce:scecf3:68

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Related research

Keywords: Dynamic Population Games; Bounded Rationality; Endogenous Networks; Fitness Landscapes; Evolutionary Environments; Adaptive Expectations.;

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References

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Cited by:
  1. Giorgio Fagiolo & Alessio Moneta & Paul Windrum, 2007. "A Critical Guide to Empirical Validation of Agent-Based Models in Economics: Methodologies, Procedures, and Open Problems," Computational Economics, Society for Computational Economics, Society for Computational Economics, vol. 30(3), pages 195-226, October.
  2. Paul Windrum & Giorgio Fagiolo & Alessio Moneta, 2007. "Empirical Validation of Agent-Based Models: Alternatives and Prospects," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 10(2), pages 8.
  3. Valente M. & Fagiolo G., 2004. "Minority Games, Local Interactions, and Endogenous Networks," Computing in Economics and Finance 2004, Society for Computational Economics 110, Society for Computational Economics.
  4. Andreas Pyka & Giorgio Fagiolo, 2005. "Agent-Based Modelling: A Methodology for Neo-Schumpeterian Economics," Discussion Paper Series, Universitaet Augsburg, Institute for Economics 272, Universitaet Augsburg, Institute for Economics.

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