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

The original paper compared two com-monly used approaches to socio-economic simulation. In the first approach param-eter settings for the evolutionary algorithm are directly derived from the underlyingeconomic model while in the second approach to social learning parameter settingsare chosen so as to optimise evolutionary algorithm performance. Main conclusionsof the original paper are that the first approach may hinder the performance of theevolutionary algorithm and thereby hinder agent learning, that is, that social learn-ing evolutionary algorithms are able to overcome the so-called spite-effect and ob-tain high profit outcomes. These main conclusions are still confirmed when the er-ror in the mutation operator is corrected. However, the convergence behaviour ofsome of the individual runs differs significantly from the (incorrect) results pre-sented in the earlier papers. More specifically, in the corrected experiments we donot observe the same type of premature convergence in approach I. In this paperwe present the corrected results. The average convergence behaviour for the twoapproaches is shown in Fig. 1, where we see convergence to the higher profit Cour-not Nash outcome (at output 40) using approach II whereas approach I leads to thelower-profit competitive outcome (at output 50) for these set of EA parameters. Thecorrected results for the individual runs are shown in Fig. 2.