An illustration of the essential difference between individual and social learning, and its consequences for computational analyses

Abstract

Drawing a conclusion from recent insights in evolutionary game theory, we show that a so-called spite e!ect implies that there is an essential di!erence between individual and social learning. We illustrate its consequences for the choice of computational tools in economics and social settings in general by analyzing two variants of a Genetic Algorithm.

Validation and Functional Complexity

“…This was acceptable when the players were not differentiated and when the flowo fi nformation from parents to offspring at the genotype levelwas used as an issue (Vriend 2000), but when the players are modelling heterogeneous actors -in realistic coevolution, for instance -each player must be modelled with a separate population.…”

Economic modeling using evolutionary algorithms: the effect of a binary encoding of strategies

“…To facilitate comparison, we consider exactly the same Cournot market as Alkemade et al (2006Alkemade et al ( , 2007Alkemade et al ( , 2009). For other studies in which quantity competition among firms is modeled using EAs, we refer to Arifovic (1994), Price (1997), Dawid and Kopel (1998), Franke (1998), Vriend (2000, Arifovic and Maschek (2006), Wheeler et al (2006), Casari (2008), and Maschek (2010.…”

“…As discussed by Vriend (2000), there are two quite different ways in which EAs can be employed to model the behavior of economic agents. In the individual learning approach, each agent learns exclusively from its own experience (e.g., Arifovic 1994;Price 1997;Arifovic and Maschek 2006;Casari 2008).…”

“…They note that the social learning approach can be implemented in two quite different ways. On the one hand, one can employ an EA with a population size that equals the number of interacting agents (e.g., Arifovic 1994;Dawid and Kopel 1998;Franke 1998;Vriend 2000). This results in a one-to-one relationship between strategies and agents.…”

“…If the agents whose behavior is being modeled have to make decisions that are intrinsically binary, such as decisions between cooperation and defection in a prisoner's dilemma (e.g., Axelrod 1987), the use of a binary encoding of strategies is a very natural choice. However, in the case of nonbinary decisions, such as decisions by firms on their production level (e.g., Arifovic 1994;Price 1997;Dawid and Kopel 1998;Franke 1998;Vriend 2000;Alkemade et al 2006Alkemade et al , 2007Alkemade et al , 2009; Arifovic and Maschek 2006;Wheeler et al 2006;Casari 2008;Maschek 2010), there is no clear reason for the use of a binary encoding of strategies.…”