Collaborative Research: New Algorithims for Computing Equilibria of Stochastic Games

合作研究：计算随机博弈均衡的新算法

• 批准号: 1530823
• 负责人: Benjamin Brooks
• 金额: $ 25.92万
• 依托单位: University of Chicago
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-08-01 至 2020-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1530823&HistoricalAwards=false
• 关键词: Collaborative; Research; New; Algorithims; Computing

This research focuses on models of dynamic strategic behavior, in which two parties (individuals, nations, corporations, agencies) interact repeatedly over a long horizon and make decisions that have a persistent impact on the environment. The PIs are developing algorithms to simulate behavior in which each party has consistent beliefs about how others will behave and is maximizing their own welfare given those beliefs. These computational methods can be applied to a wide range of models that are used in various fields of economics, as well as in other disciplines such as international relations and evolutionary biology.More specifically, the PIs study the pure strategy subgame perfect equilibria of infinitely repeated games with geometric discounting and a stochastic state variable. The state variable determines the set of actions that can be taken by the agents in each period of the game and the resultant payoffs. Conditional on the actions taken by the players, the state evolves as a Markov chain. The objective of the research is to develop algorithms for computing the discounted payoffs that players can obtain in subgame perfect equilibria starting in each state. The PIs will also develop and distribute software packages that implement the algorithms.

本研究重点关注动态战略行为模型，其中两方（个人、国家、公司、机构）长期反复互动，并做出对环境产生持续影响的决策。 PI 正在开发算法来模拟行为，其中每一方对其他人的行为方式都有一致的信念，并在给定这些信念的情况下最大化自己的福利。这些计算方法可以应用于经济学各个领域以及国际关系和进化生物学等其他学科中使用的广泛模型。更具体地说，PI 研究无限重复的纯策略子博弈完美均衡具有几何折扣和随机状态变量的游戏。状态变量决定了智能体在游戏的每个阶段可以采取的动作集以及由此产生的收益。根据参与者采取的行动，状态会演变为马尔可夫链。研究的目的是开发算法来计算玩家在从每个状态开始的子博弈完美均衡中可以获得的折扣收益。 PI 还将开发和分发实施算法的软件包。