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

Davide Fiaschi and Pier Mario Pacini ()
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Pier Mario Pacini: University of Pisa

Chapter 3 in New Tools of Economic Dynamics, 2005, pp 43-59 from Springer

Abstract: 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.

Keywords: Coalition formation; Learning; Genetic Algorithms; Increasing returns to scale; Numerical simulations (search for similar items in EconPapers)
Date: 2005
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Persistent link: https://EconPapers.repec.org/RePEc:spr:lnechp:978-3-540-28444-4_3

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DOI: 10.1007/3-540-28444-3_3

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