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Best Experienced Payoff Dynamics and Cooperative Play in Extensive Form Games

广泛形式游戏中最有经验的支付动态和合作游戏

基本信息

批准号：
1728853
负责人：
William Sandholm
金额：
$ 28.8万
依托单位：
University of Wisconsin-Madison
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2017
资助国家：
美国
起止时间：
2017-08-01 至 2020-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1728853&HistoricalAwards=false
关键词：
Best Experienced Payoff Dynamics Cooperative

项目摘要

This award funds research in game theory. The investigator seeks to develop and analyze new models of population dynamics in situations where individuals interact with others in strategic situations over time. The first part of the project considers a situation in which individuals respond rationally to the outcomes they experience when randomly matched with a partner. The goal here is to demonstrate how the resulting dynamics lead to cooperative behavior even when the individuals involved know that they will only interact with the same person for a set time. The possibility of maintaining cooperative behavior in repeated relationships with a set end date is a basic question in game theory, one with direct relevance to many social sciences and many policy applications. The dynamics studied in this project can provide one explanation for the persistence of cooperative behavior and may help us develop policies and institutions that support cooperation. The PI will also develop new public domain software, and will demonstrate how to use that software to model game dynamics via an online textbook/course. The software, online book, and course will enable students and researchers in a wide variety of disciplines to experiment with evolutionary game dynamics, without requiring that these students have extensive backgrounds in game theory or mathematics. The first component of the project studies best experienced payoff dynamics, under which revising agents select a set of strategies to test, play each strategy against fixed numbers of randomly chosen opponents, and switch to the strategy with the highest realized payoff. Analysis based on backward induction predict fully uncooperative behavior, while experimental evidence points to higher levels of cooperation. The basic idea is that the dynamics possess an almost globally attractive rest point exhibiting high levels of cooperation. This rest point is quite robust to variations of the model. Because the dynamics take the form of polynomial equations with rational coefficients, techniques from computational algebra are useful to prove results and in describing the classes of games in which cooperative play can be sustained. The second project considers evolutionary game dynamics which incorporate intrinsic relations among different strategies. These relations may reflect similarities in what playing the strategies entails, perceptions that certain strategies are of a similar nature, or the contexts in which the game is played. Nested replicator dynamics arise when imitating agents evaluate strategies by comparing their payoffs not only to an exogenous standard, but also to the average payoff earned by similar strategies. These dynamics retail all basic convergence and stability properties of the replicator dynamic, and can be lined to models from discrete choice theory via models of reinforcement learning.

该奖项资助博弈论研究。研究人员试图开发和分析人口动态的新模型，在这种情况下，个人与他人互动的战略情况下，随着时间的推移。该项目的第一部分考虑了一种情况，即当与一个伙伴随机配对时，个体对他们所经历的结果做出理性反应。这里的目标是证明，即使参与者知道他们只会在一段时间内与同一个人互动，所产生的动态如何导致合作行为。在重复的关系中保持合作行为的可能性与设定的结束日期是博弈论中的一个基本问题，与许多社会科学和许多政策应用直接相关。在这个项目中研究的动力学可以提供一个解释合作行为的持久性，并可能帮助我们制定政策和机构，支持合作。 PI还将开发新的公共领域软件，并将通过在线教科书/课程演示如何使用该软件来模拟游戏动态。该软件，在线书籍和课程将使各种学科的学生和研究人员能够实验进化博弈动力学，而不要求这些学生具有广泛的博弈论或数学背景。该项目的第一个组成部分研究最有经验的回报动态，根据修改代理选择一组策略进行测试，发挥每个战略对固定数量的随机选择的对手，并切换到战略实现最高的回报。基于逆向归纳的分析预测完全不合作的行为，而实验证据表明，更高水平的合作。其基本思想是，动态拥有一个几乎具有全球吸引力的休息点，表现出高水平的合作。该静止点对于模型的变化是相当鲁棒的。由于动态的形式多项式方程与合理的系数，技术从计算代数是有用的，以证明结果，并在描述类的游戏，其中合作发挥可以持续。第二个项目认为进化博弈动态，其中包括不同的战略之间的内在关系。这些关系可能反映了玩这些策略所需要的相似性，某些策略具有相似性质的感知，或者玩游戏的背景。当模仿代理人通过比较他们的收益不仅与外生标准，而且与类似策略的平均收益来评估策略时，嵌套复制因子动态就出现了。这些动态零售复制动态的所有基本收敛和稳定性属性，并且可以通过强化学习模型与离散选择理论的模型相关联。