An Exploration of Behavior in Dynamic Games

动态环境的经济模型对政策的形成非常重要，这些环境的应用理论已经影响了整个经济领域：公共财政、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很发达，但总的来说，它并不能提供准确的预测。恰恰相反。相反，一般理论表明，在一个动态的环境中，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、行业中其他竞争者的数量、大气污染物)和观察到的过去特征(特别是其他行为者的行为)的反应。为了处理预测中的不确定性，应用经济学理论转而依赖于强有力的假设--在很大程度上未经检验。这些假设既用于检验政策变化的理论化影响，也用于产生用于政策的估计。尽管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截然不同的政策结果。在预测了社会收益的地方，可能反而会观察到损失。这个项目的目的是利用对动态环境中人类行为的受控观察来检验这些理论假设何时可能成立。更深入地了解环境运动的哪些特征将允许更有力的政策讨论，并随后从更好的经济政策中受益于社会。该项目解决了一个在经济学中越来越多的应用兴趣的主题，但在这个主题中，理论(通常)不能做出准确的预测。它将从人类行为中提供证据，首要目标是构建预测性选择标准，以表明标准假设可能在哪些环境中成立。此外，在标准假设失败的情况下，该项目旨在为替代方案提供证据。在指定替代假设时，这样的替代方案可以提供更大的力量。在这个项目中，PI提出了一系列实验，以检查动态战略环境中的行为。提出了四个子项目：i)研究其他参与者战略选择的不确定性如何影响选择；ii)检查团队和个人在这些环境中的不同行为程度；iii)检查利己主义专家在长期关系中透露的信息；以及iv)检查环境中活跃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，每个修改都是为了从相关的兴趣特征中分离和衡量影响。这项研究的广泛影响源于对战略环境的哪些方面导致人们将重点放在最近过去的观察(历史产出、价格、开采水平等)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，结果旨在为动态游戏应用文献中最常见的假设产生基于证据的标准。更深入地了解什么是推动选择的动力，将允许更有力的政策讨论，并随后为社会带来好处。

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.