Simulation of ultimatum game with artificial intelligence and biases

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Julio Añasco, Bryan Josué Naranjo Navas, Pamela Anahí Proaño Mora, Maria Anastasia Vasileuski Kramskova
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引用次数: 1

Abstract

In this research we have developed experimental designs of the ultimatum game with supervised agents. This agents have unbiased and biased thinking depending on the case. We used Reinforcement Learning and Bucket Brigade to program the artficial agentes. We used simulations and behavior comparison to answer the following questions: Does artificial intelligence reach a perfect subgame equilibrium in the ultimatum game experiment? How would Artificial Intelligence behave in the Ultimatum Game experiment if biased thinking is included in it? This exploratory analysis showed one important result: artificial inteligence by itself doesn´t reach a perfect subgame equilibrium. Whereas, the experimental designs with biased thinking agents quickly converge to an equilibrium. Finally, we demonstrated that the agents with envy bias behaves the same as the ones with altruistic bias.
带有人工智能和偏见的最后通牒博弈模拟
在本研究中,我们开发了具有监督代理的最后通牒博弈的实验设计。这些代理人根据情况有公正和偏见的想法。我们使用强化学习和Bucket Brigade对人工智能体进行编程。我们通过模拟和行为对比来回答以下问题:人工智能在最后通牒博弈实验中是否达到了完美的子博弈均衡?如果在最后通牒博弈实验中包含偏见思维,人工智能会如何表现?这种探索性分析显示了一个重要的结果:人工智能本身并不能达到完美的子博弈平衡。然而,有偏见思维主体的实验设计很快收敛到平衡状态。最后,我们证明了具有嫉妒偏见的代理人与具有利他偏见的代理人的行为相同。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Avances en Ciencias e Ingenieria
Avances en Ciencias e Ingenieria ENGINEERING, MULTIDISCIPLINARY-
自引率
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发文量
16
审稿时长
14 weeks
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