Robust Evolutionary Algorithm Design for Socio-Economic Simulation: A Correction

Abstract

Recently we have discovered an error in the implementation of the mutation operator in our earlier work on robust evolutionary algorithm design for socio-economic simulation (Alkemade et al. 2006(Alkemade et al. , 2007. 1 The original paper compared two commonly used approaches to socio-economic simulation. In the first approach parameter settings for the evolutionary algorithm are directly derived from the underlying economic model while in the second approach to social learning parameter settings are chosen so as to optimise evolutionary algorithm performance. Main conclusions of the original paper are that the first approach may hinder the performance of the evolutionary algorithm and thereby hinder agent learning, that is, that social learning evolutionary algorithms are able to overcome the so-called spite-effect and obtain high profit outcomes. These main conclusions are still confirmed when the error in the mutation operator is corrected. However, the convergence behaviour of some of the individual runs differs significantly from the (incorrect) results presented in the earlier papers. More specifically, in the corrected experiments we do not observe the same type of premature convergence in approach I. In this paper we present the corrected results. The average convergence behaviour for the two approaches is shown in Fig. 1, where we see convergence to the higher profit Cournot Nash outcome (at output 40) using approach II whereas approach I leads to the lower-profit competitive outcome (at output 50) for these set of EA parameters. The corrected results for the individual runs are shown in Fig.

Robust Evolutionary Algorithm Design for Socio-Economic Simulation: Some Comments

“…This seemed a quite curious result. We are happy that the correction (Alkemade et al 2008) makes clear that the degree of premature convergence is much lower than originally reported. There are three further comments that we would like to make on the work of Alkemade et al…”

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

“…Our research is inspired by results reported by Alkemade et al (2006Alkemade et al ( , 2007Alkemade et al ( , 2009; see also Waltman and Van Eck 2009b). Alkemade et al show that under certain conditions an EA that is employed for modeling purposes may exhibit premature convergence.…”

“…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.…”

“…For early research in which genetic algorithms are employed, we refer to Miller (1986Miller ( , 1996, Axelrod (1987), Marks (1992), Arifovic (1994Arifovic ( , 1996, Andreoni and Miller (1995), and Dawid (1996). Examples of more recent research can be found in the work of, among others, Lux and Schornstein (2005), Alkemade et al (2006Alkemade et al ( , 2007Alkemade et al ( , 2009), Arifovic and Maschek (2006), Wheeler et al (2006), Xu (2006), Casari (2008), and Maschek (2010).…”

“…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.…”