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Equilibrium selection by co-evolution for bargaining problems under incomplete information about time preferences

机译:在时间偏好信息不完整的情况下,通过协同进化均衡选择讨价还价问题

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The main purpose of this work is to measure the impact of players' information completeness on the outcomes in dynamic strategic games. We apply co-evolutionary algorithms to solve four incomplete information bargaining problems and investigate the experimental outcomes on players' shares from agreements, the efficiency of agreements and the evolutionary time for convergence. Empirical analyses indicate that in the absence of complete information on the counterpart(s)' preferences, co-evolving populations are still able to select equilibriums which are Pareto-efficient and stationary. This property of the co-evolutionary algorithm supports its future applications on complex dynamic games.
机译:这项工作的主要目的是衡量玩家信息完整性对动态战略游戏结果的影响。我们使用协进化算法来解决四个不完全的信息讨价还价问题,并研究协议,参与者的效率和收敛的进化时间对参与者份额的实验结果。实证分析表明,在没有关于对方偏好的完整信息的情况下,共同发展的群体仍然能够选择帕累托有效和平稳的均衡。协同进化算法的这一特性支持其在复杂动态游戏中的未来应用。

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