Choice, Learning, and Equilibrium

该奖项资助博弈论研究，开发新的方法来分析人们如何在战略环境中相互作用，特别是当人们随着时间的推移从他们的行动结果中学习时。PI使用多种方法计划了六个不同的研究项目;正式理论、数值模拟和实验室实验。第一个和最后一个项目的特征条件下，进化学习过程意味着行为调整相对较快的长期结果。第二个将产生和分析一个模型的个人选择是概率性的，并包括所需的时间，使一个选择。第三个项目将研究人们如何决定是否在一个重复的互动设置，他们只观察他们的合作伙伴的意图与噪音，所以不能肯定是否合作失败是故意的，此外，人们可以作出（可能是错误的）声明，他们是否打算合作。第四个项目将开发一个新的平衡行为的定义在战略情况下，看看什么样的不正确的信念对其他人的发挥是强大的理性学习时，不同的人在同一个角色（例如“审计员”或“消费者”）不直接观察其他人在自己的角色做什么。第五个项目考虑人们如何从经验中学习，当他们对世界的最初理解不太正确时（例如，当人们努力从错误的模型中学习时）。这项研究将通过发展新的理论和方法来帮助我们更好地预测人们对政府政策和商业惯例变化的反应，从而造福社会。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，使其在保持与贝叶斯优化的联系的同时，更好地匹配选择概率与决策时间之间关系的观测数据？当模型被推广到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，并准备以较低的当前预期回报换取更多信息的信号时，会发生什么，但由于他们的模型被错误指定，他们的行为会误解信息价值？带有近因偏差的学习--主要依赖于最近的观察结果--的含义是什么，纳什均衡将被观察到？什么时候进化或学习模型收敛得足够快，以至于它们在大种群中的渐近行为是相关的，这与选择的随机性有什么关系？当玩家的行为被错误地观察到时，他们什么时候才能如实地报告他们的意图，什么时候其他人才能学会相信这些廉价的谈话和可能的虚假报告？当参与者知道对手的支付函数，也知道观察结果的种类（例如出价、价值等）时，理性学习的长期含义是什么？其他参与者看到的但不是他们的实际数据？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，拟议中的研究可能会提高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的重要性，也是进化生物学中的一个关键问题。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。

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.