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on Evolutionary Economics |
| By: | Edgar Sanchez Carrera |
| Abstract: | We study an evolutionary game in which the individual behavior of the economic agents can lead the economy either into a low-level or a high-level equilibrium. The model represents two asymmetric populations, “leaders and followers”, where in each round an economic agent of population 1 is paired with a member of population 2. Our evolutionary game is a signaling game in which only the leader has private information. The leader moves first; the follower observes the leader's action, but not the leader's type, before choosing her own action. We found the equilibria both as self-confirming and evolutionarily stable strategies. Furthermore, considering an imitative behavior of the followers, we show that to overcome the poverty trap there exists a threshold value equals to the ratio "education costs-efficiency wages" of the number of high-profile economic agents |
| Keywords: | Evolutionary games, imitation rule, poverty traps, replicator dynamics, signaling games, strategic complementarities |
| JEL: | C70 C72 C73 I30 O10 O40 |
| Date: | 2009–02 |
| URL: | http://d.repec.org/n?u=RePEc:usi:wpaper:555&r=evo |
| By: | Waltman, L.R.; Eck, N.J.P. van (Erasmus Research Institute of Management (ERIM), RSM Erasmus University) |
| Abstract: | We present a mathematical analysis of the long-run behavior of genetic algorithms that are used for modeling social phenomena. The analysis relies on commonly used mathematical techniques in evolutionary game theory. Assuming a positive but infinitely small mutation rate, we derive results that can be used to calculate the exact long-run behavior of a genetic algorithm. Using these results, the need to rely on computer simulations can be avoided. We also show that if the mutation rate is infinitely small the crossover rate has no effect on the long-run behavior of a genetic algorithm. To demonstrate the usefulness of our mathematical analysis, we replicate a well-known study by Axelrod in which a genetic algorithm is used to model the evolution of strategies in iterated prisoner’s dilemmas. The theoretically predicted long-run behavior of the genetic algorithm turns out to be in perfect agreement with the long-run behavior observed in computer simulations. Also, in line with our theoretically informed expectations, computer simulations indicate that the crossover rate has virtually no long-run effect. Some general new insights into the behavior of genetic algorithms in the prisoner’s dilemma context are provided as well. |
| Keywords: | genetic algorithm;long-run behavior;social modeling;economics;evolutionary game theory |
| Date: | 2009–03–09 |
| URL: | http://d.repec.org/n?u=RePEc:dgr:eureri:1765015181&r=evo |