Analysis of the Evolutionary Game Theory among Iran & Saudi Arabia in the Context of Genetic Algorithm
Subject Areas : Labor and Demographic Economics
samaneh
khatami
1
(Shahid Bahonar University)
alireza
shakibaee
2
(Shahid Bahonar University OF kerman faculty member)
Keywords: OPEC, Genetic algorithm, JEL Classification: C63, C70, C73. Keywords: Game, Evolutionary Computation, Iterated Prisoner’s Dilemma,
Abstract :
Evolutionary game theory has been known as the most suitable tool for modeling the dynamics of strategic interactions. In this regard, evolutionary algorithms present the new approach for learning and decision modeling of bounded rationality factors. The objective of this study is providing the new model of searching for optimal strategies in iterated prisoner's dilemma (IPD) using genetic algorithm. For this purpose, by simulating competition between Iran and Saudi Arabia in OPEC oil Coalition, we used 12 strategy types over 20 runs of genetic algorithm for maximizing individual’s scores and also minimizing competitor fitness scores. Results show that “Tit for Tat” with the highest average fitness in both competitions known as the optimal strategy. The other strategis like; Soft Majority, Trigger & TF2T are next in ranking. The strategy “All D” is known as inefficient strategy in competition with the lowest productivity.
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