Choice, Learning, and Equilibrium

选择、学习和均衡

• 批准号: 1558205
• 负责人: Drew Fudenberg
• 金额: $ 30.24万
• 依托单位: Massachusetts Institute of Technology
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-07-15 至 2020-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1558205&HistoricalAwards=false
• 关键词: Choice; Learning; Equilibrium

This award funds research in game theory that develops new ways to analyze how people interact with each other in strategic settings, especially when people learn over time from the results of their actions. The PI plans six different research projects using a variety of methods; formal theory, numerical simulation, and lab experiments. The first and last projects characterize conditions under which evolutionary learning processes mean that behavior adjusts relatively quickly to a long run outcome. The second will produce and analyze a model of individual choice that is probabilistic and includes the time it takes to make a choice. The third project will examine how people decide whether to cooperate in a repeated interaction setting where they only observe their partner's intention with noise, and so can not be certain whether a failure to cooperate was intentional, and in addition people can make (possibly false) claims about whether they intended to cooperate. The fourth project will develop a new definition of equilibrium behavior in strategic situations to see what sorts of incorrect beliefs about other people's play are robust to rational learning when different people in the same role (e.g. "auditors" or "consumers") do not directly observe what other people in their own role do. The fifth project considers how people learn from experience when their beginning understanding of the world is not quite correct (eg, when people work to learn from misspecified models). The research will benefit society by developing new theories and methods that can help us better predict how people will respond to changes in government policies and business practices.The research will help develop a better understanding of human behavior in individual decisions and in interactive contexts. The research questions include the following. How can the widely-used drift-diffusion model of stochastic choice be improved to better match the observed data on the relationship between choice probability and decision time, while maintain its link to Bayesian optimization? What happens when the model is generalized to allow time-varying costs or other signal structures? What happens when people are trying to learn their optimal actions, and are prepared to tradeoff a lower current expected payoff for a more informative signal, but misperceive the information value of their actions because their model is mis-specified? What are the implications of learning with recency bias- the tendency to rely mostly on recent observations- for which Nash equilibria will be observed? When do evolutionary or learning models converge quickly enough that that their asymptotic behavior in large populations is relevant, and how does this relate to the amount of randomness in choice? When will players truthfully report their intended play when their actions are observed with error, and when will others learn to trust these cheap-talk and possibly false reports? What are the long-run implications of rational learning when players know their opponents' payoff functions and also know the sorts of observations (e.g. bids, values, etc.) that other players see but not their actual data? The project will be strengthen ties between economists and psychologists interested in either recency bias or the drift-diffusion model, and between economists and computer scientists interested in learning in games. Taking a longer term view, the proposed research may enhance our understanding of how and when reciprocal altruism leads to cooperation; this is of fundamental importance in many branches of social science and is also a key issue in evolutionary biology. Likewise, better understanding the foundations of stochastic choice is a fundamental issue in cognitive psychology and computational neuroscience.

该奖项资助博弈论研究，该研究开发新方法来分析人们在战略环境中如何互动，特别是当人们随着时间的推移从其行为结果中学习时。 PI 使用多种方法计划了六个不同的研究项目；形式理论、数值模拟和实验室实验。第一个和最后一个项目描述了进化学习过程意味着行为相对快速地适应长期结果的条件。第二个将产生并分析个人选择的概率模型，并包括做出选择所需的时间。第三个项目将研究人们如何在重复的交互环境中决定是否合作，在这种环境中，他们只能通过噪音观察对方的意图，因此无法确定不合作是否是有意的，此外，人们可以对他们是否打算合作做出（可能是错误的）声明。第四个项目将制定战略情境中均衡行为的新定义，以了解当担任同一角色的不同人（例如“审计员”或“消费者”）不直接观察其他人在自己的角色中所做的事情时，什么样的关于其他人的游戏的错误信念对于理性学习来说是稳健的。第五个项目考虑当人们对世界的开始理解不太正确时（例如，当人们努力从错误指定的模型中学习时），他们如何从经验中学习。这项研究将通过开发新的理论和方法来造福社会，帮助我们更好地预测人们将如何应对政府政策和商业实践的变化。这项研究将有助于更好地理解个人决策和互动环境中的人类行为。 研究问题包括以下内容。如何改进广泛使用的随机选择漂移扩散模型，以更好地匹配选择概率和决策时间之间关系的观测数据，同时保持其与贝叶斯优化的联系？当模型被推广以允许时变成本或其他信号结构时会发生什么？当人们试图学习他们的最佳行动，并准备用较低的当前预期回报换取信息量更大的信号时，但由于模型指定错误而误解了他们行动的信息价值，会发生什么？带有新近度偏差的学习（主要依赖最近的观察的倾向）会观察到纳什均衡，这意味着什么？进化或学习模型何时收敛得足够快，以至于它们在大群体中的渐近行为是相关的？这与选择的随机性有何关系？当玩家的行为被观察到错误时，什么时候他们才能如实报告他们的预期玩法？其他人什么时候才能学会相信这些廉价言论和可能的虚假报告？当玩家知道对手的支付函数并且也知道其他玩家看到的观察类型（例如出价、价值等）而不是他们的实际数据时，理性学习的长期影响是什么？该项目将加强对近因偏差或漂移扩散模型感兴趣的经济学家和心理学家之间的联系，以及对游戏学习感兴趣的经济学家和计算机科学家之间的联系。从长远来看，拟议的研究可能会增强我们对互惠利他主义如何以及何时导致合作的理解；这在社会科学的许多分支中具有根本重要性，也是进化生物学的一个关键问题。同样，更好地理解随机选择的基础是认知心理学和计算神经科学的一个基本问题。