An Exploration of Behavior in Dynamic Games

动态环境的经济模型对于政策的制定非常重要，这些环境的应用理论已经在整个经济领域产生了影响：公共财政学，环境经济学，宏观经济学，劳动经济学和产业组织。然而，尽管动态环境的经济理论已经发展得很好，但一般来说，它并不能提供精确的预测。恰恰相反。相反，一般定理表明，在动态环境中，几乎任何事情都可能发生，这取决于参与者对环境的同期特征（价格，行业中其他竞争对手的数量，大气污染物）和过去观察到的特征（特别是其他参与者的行为）的反应。为了处理预测中的不确定性，应用经济学理论依赖于强有力的假设--在很大程度上未经检验。这些假设既可以用来检验政策变化的理论效应，也可以用来产生用于政策的估计。虽然这些假设可以使复杂的环境变得容易处理，但假设的失败可能会产生与假设非常不同的政策结果。在预测社会收益的情况下，可能会观察到损失。该项目的目的是使用动态环境中人类行为的受控观察来检查这些理论假设何时可能成立。更深入地了解环境驱动力的特征将有助于更有力的政策讨论，并从更好的经济政策中为社会带来后续利益。该项目解决了一个在经济学中日益增长的应用兴趣的主题，但理论并没有（一般）做出精确的预测。它将提供来自人类行为的证据，其总体目标是构建预测性选择标准，以表明标准假设在哪些环境中可能成立。此外，在标准假设不成立的情况下，该项目旨在为替代品提供证据。当指定替代假设时，这种替代方案可以提供更大的功效。在这个项目中，PI提出了一系列实验，将检查在动态战略环境中的行为。四个子项目提出：一）检查其他参与者的战略选择的不确定性如何影响选择;二）检查团队和个人在这些设置中表现不同的程度;三）检查在长期关系中自利专家所揭示的信息;和四）检查在一个环境中的积极参与者的数量对行为的影响。在所有四个子项目中，PI构建了一个简单的基线环境，并对其进行了几次修改，每一次修改都被选择用于隔离和测量感兴趣的相关特征的影响。研究的更广泛影响来自于对战略环境的哪些方面导致人类关注最近的观察（历史产出，价格，提取水平等）的更深入理解。以确定他们目前的行动，而不是目前的条件（投入价格，需求，竞争对手的数量等）。在政策方面，研究结果旨在为动态博弈应用文献中最常见的假设提供基于证据的标准。更深入地了解是什么推动了选择，将有助于更有力的政策讨论，并为社会带来后续利益。

Economic models of dynamic environments are highly important for forming policy, and applied theory for these settings has been influential across the entire economic spectrum: public finance, environmental economics, macroeconomics, labor economics, and industrial organization. However, while economic theory for dynamic environments is well-developed, in general it does not provide a precise prediction. Quite the opposite. General theorems instead indicate that most anything can happen in a dynamic environments, dependent on how the participants react to both contemporaneous features of the environment (prices, the number of other competitors in an industry, atmospheric pollutants) and observed features of the past (in particular, other actors behavior). To deal with indeterminacy in the predictions applied economic theory has instead relied on strong assumption - for the most part untested. These assumptions serve to examine both theorized effects from a policy change, but also to produce estimates for use in policy. While these assumptions can make complex environments tractable, failure of the assumption has the potential to produce very different policy outcomes than those posited. Where a societal gain was predicted, a loss may instead be observed. This project's aim is to use controlled observations of human behavior in dynamic environments to examine when these theoretical assumptions are likely to hold true. Greater insight into what features of the environment drive will allow for more-robust policy discussions, and a subsequent benefit to society from better economic policy.The project addresses a topic with growing applied interest in economics, but where theory does not (generically) make precise predictions. It will provide evidence from human behavior, with the overarching aim being the construction of predictive selection criteria to indicate in which settings the standard assumptions are likely to hold true. Further, in those settings where the standard assumptions fail, the project aims to provide evidence for alternatives. Such alternatives can provide greater power when specifying alternative hypotheses. In this project, the PIs propose a series of experiments that will examine behavior in dynamic strategic environments. Four sub-projects are proposed: i) an examination of how uncertainty over other participants' strategic choices affects selection; ii) an examination of the extent to which teams and individuals behave differently in these settings; iii) an examination of the information revealed by self-interested experts across a long-run relationship; and iv) an examination of the effects on behavior from the number of active participants in an environment. In all four sub-projects, the PIs construct a simple baseline environment, and several modifications of it, each of which is chosen to isolate and measure the effects from a relevant feature of interest.Broader impacts from the study follow from a greater understanding of which facets of a strategic environment lead humans to focus on observations from the recent past (historical outputs, prices, extraction levels, etc.) to determine their present actions, as opposed to current conditions (input prices, demand, number of competitors, etc.). In terms of policy, the results aim to produce evidence-based criteria for the most-common assumptions in the applied literature on dynamic games. Greater insight into what drives selection will allow for more-robust policy discussions, and subsequent benefits to society.