Profit seven ea211/7/2022 ![]() ![]() ![]() However, the approach taken by Dawid is quite different from the approach that we take in the present paper. We note that the consequences of the use of a binary encoding of strategies are also studied extensively by Dawid ( 1996 see also Dawid and Kopel 1998). (i.e., a Cournot oligopoly market), we demonstrate that premature convergence does occur in the case of EAs with a binary encoding while it does not occur in the case of EAs without a binary encoding. Using the same economic environment as Alkemade et al. depends crucially on their use of a binary encoding of strategies. We show that the observation of premature convergence by Alkemade et al. In this paper, we report results that point in a different direction. argue that premature convergence is caused by a too small population size. mean that different runs of the EA can lead to very different results. show that under certain conditions an EA that is employed for modeling purposes may exhibit premature convergence. Our research is inspired by results reported by Alkemade et al. In order to avoid these artifacts, we argue that in most cases researchers should not use a binary encoding of strategies. In general, these effects do not have a meaningful economic interpretation and should be regarded as artifacts. It turns out that the use of a binary encoding can have quite significant effects. In this paper, we examine to what extent the use of a binary encoding of strategies may influence the results of studies in which EAs are employed. 2006 Casari 2008 Maschek 2010), there is no clear reason for the use of a binary encoding of strategies. However, in the case of non-binary 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. 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. Researchers who apply genetic algorithms as a tool for modeling boundedly rational behavior typically do not justify why they use a binary encoding of strategies. Examples of more recent research can be found in the work of, among others, Lux and Schornstein ( 2005), Alkemade et al. For early research in which genetic algorithms are employed, we refer to Miller ( 1986, 1996), Axelrod ( 1987), Marks ( 1992), Arifovic ( 1994, 1996), Andreoni and Miller ( 1995), and Dawid ( 1996). EAs that use a binary encoding of strategies are commonly referred to as genetic algorithms. This means that strategies are represented by bit strings (i.e., strings of zeros and ones, often referred to as chromosomes) and that evolutionarily inspired operations such as crossover and mutation take place at the level of individual bits. When EAs are applied as a modeling tool in economic research, a binary encoding of strategies is typically used. In economic research, EAs frequently serve as a tool for modeling boundedly rational behavior. Nowadays, EAs are also regularly employed in the field of economics. EAs have their origins in the field of computer science, where they are mainly applied for optimization purposes. Evolutionary algorithms (EAs) are algorithms that are inspired by the process of natural evolution. ![]()
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