An Exploration of Behavior in Dynamic Games

动态环境的经济模型对制定政策非常重要，这些环境的应用理论在整个经济领域都有影响：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，虽然动态环境的经济理论很发达，但总的来说，它并没有提供精确的预测。恰恰相反。相反，一般定理表明，大多数事情都可能发生在动态环境中，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）和过去观察到的特征（特别是其他参与者的行为）的反应。为了应对预测中的不确定性，应用经济理论转而依赖于强有力的假设——这些假设在很大程度上未经检验。这些假设既可以检验政策变化的理论化影响，也可以为政策制定提供估计。虽然这些假设可以使复杂的环境变得容易处理，但假设的失败有可能产生与假设截然不同的政策结果。在预计会有社会收益的地方，可能反而会观察到社会损失。这个项目的目的是在动态环境中对人类行为进行有控制的观察，以检验这些理论假设何时可能成立。更深入地了解环境驱动的哪些特征将允许更有力的政策讨论，以及随后更好的经济政策对社会的好处。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（通常）不能做出精确的预测。它将提供来自人类行为的证据，其首要目标是构建预测选择标准，以表明在哪些情况下标准假设可能成立。此外，在标准假设失效的情况下，本项目旨在为替代方案提供证据。当指定替代假设时，这些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，将检查动态战略环境中的行为。本文提出了四个子项目：1)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)对长期关系中自私自利的专家所披露的信息进行检查；iv)一个环境中活跃参与者的数量对行为的影响的检验。在所有四个子项目中，pi构建了一个简单的基线环境，并对其进行了一些修改，选择每一个都是为了从感兴趣的相关特征中分离和测量效果。这项研究的更广泛影响来自于对战略环境的哪些方面的更深入的理解，这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），以确定他们当前的行动，而不是当前的条件（投入价格、需求、竞争对手的数量等）。就政策而言，结果旨在为动态游戏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驱动了选择，将允许更有力的政策讨论，并随后给社会带来好处。

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.