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Endogenous Networks In Random Population Games


  • Giorgio Fagiolo
  • Luigi Marengo
  • Marco Valente


Population learning in dynamic economies traditionally has been studied in contexts where payoff landscapes are smooth. Here, dynamic population games take place over “rugged” landscapes, where agents are uncertain about payoffs from bilateral interactions. Notably, individual payoffs from playing a binary action against everyone else are uniformly distributed over [0, 1]. This random population game leads the population to adapt over time, with agents updating both actions and partners. Agents evaluate payoffs associated to networks thanks to simple statistics of the distributions of payoffs associated to all combinations of actions performed by agents out of the interaction set. Simulations show that: (1) allowing for endogenous networks implies higher average payoff compared to static networks; (2) the statistics used to evaluate payoffs affect convergence to steady-state; and (3) for statistics 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.

Suggested Citation

  • Giorgio Fagiolo & Luigi Marengo & Marco Valente, 2004. "Endogenous Networks In Random Population Games," Mathematical Population Studies, Taylor & Francis Journals, vol. 11(2), pages 121-147.
  • Handle: RePEc:taf:mpopst:v:11:y:2004:i:2:p:121-147 DOI: 10.1080/08898480490480622

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    Cited by:

    1. Giorgio Fagiolo & Marco Valente, 2005. "Minority Games, Local Interactions, and Endogenous Networks," Computational Economics, Springer;Society for Computational Economics, vol. 25(1), pages 41-57, February.
    2. A. Pyka & G. Fagiolo, 2007. "Agent-based Modelling: A Methodology for Neo-Schumpetarian Economics," Chapters,in: Elgar Companion to Neo-Schumpeterian Economics, chapter 29 Edward Elgar Publishing.
    3. 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, vol. 10(2), pages 1-8.
    4. 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, Springer;Society for Computational Economics, vol. 30(3), pages 195-226, October.
    5. Zhang, Wei & Sun, Yuxin & Feng, Xu & Xiong, Xiong, 2015. "Evolutionary Minority Game with searching behavior," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 436(C), pages 694-706.

    More about this item


    dynamic population games; bounded rationality; endogenous networks; fitness landscapes; evolutionary environments; adaptive expectations;

    JEL classification:

    • C72 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Noncooperative Games
    • C73 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Stochastic and Dynamic Games; Evolutionary Games
    • D80 - Microeconomics - - Information, Knowledge, and Uncertainty - - - General


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