Identification and Inference of Strategic and Social Interaction Models

战略和社交互动模型的识别和推理

• 批准号: 1123990
• 负责人: Aureo De Paula
• 金额: $ 21.74万
• 依托单位: University of Pennsylvania
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-08-15 至 2014-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1123990&HistoricalAwards=false
• 关键词: Identification; Inference; Strategic; Social; Interaction

The understanding of interactions not mediated via prices is important in many economic phenomena. For example, a firm's decision to enter a market may be affected by other firms' decisions. In the classroom, peer effects may affect outcomes and decisions which may in turn play an important role later in life. There are many other examples. Because of the interplay of decisions and outcomes across actors, the empirical analysis of these phenomena is related to the empirical analysis of individual outcomes and decisions but poses challenges beyond those typically found in the analysis of individual decision making. This research project investigates methodological aspects and substantive applications in the empirical analysis of interaction models.One of the main challenges arises as a given model may be amenable to more than one set of predicted outcomes or behaviors (i.e. "equilibria"). When this is the case it is not clear how to retrieve important model components from observed data. In a first set of studies, the researcher extends previous work where the investigator and a co-author study the identification of interaction models where certain information is private to participants (de Paula and Tang, 2011). Under typical assumptions in this literature they showed that when multiple solutions are possible and different data points correspond to potentially different solutions, whether someone is incentivized or dissuaded from taking an action (when others do) can be inferred even with a very loosely defined model. As a byproduct, the investigators are also able to test for the existence of multiple solutions in the data. This is important since multiplicity is problematic for many estimation techniques currently available. The researcher extends that analysis in several directions.First, whereas the basic insight on the detection of more than one solution remains applicable in dynamic games under standard assumptions, the results on the retrieval of interaction effects is affected since what a person does at present affects the environment in the future.Second, even in the static environment originally investigated, continuously distributed control variables such as prices or income pose practical complications in estimation. Because different values for those variables may induce a different number of solutions in the underlying economy, typical nonparametric estimates of the interaction effect aggregate over covariates that contaminate one's inference on the uniqueness of a solution and, consequently, on the sign of interaction effects.Finally, whereas in many applications one can easily label the relevant players (e.g., husband and wife, Wal-Mart and local stores), in other contexts the labels do not naturally present themselves. The researcher also investigates how this affects the inference of interaction effects and applies the methods to the analysis of peer effects among college roommates, a topic that has been studied extensively in recent years.In a related project, the researcher focuses on empirical models where people choose who to associate with (i.e. "network formation games"). Recently, a lot of interest has developed around network phenomena in Economics and the Social Sciences more generally. It is common, for example, to find multiple equilibria in both environments. Because a network formation model bears many similarities to models usually studied in the empirical Industrial Organization literature, ideas and techniques akin to those previously used in the analysis of that literature can be adapted to the study of social networks. To demonstrate the methodology developed in this research, an illustration using the AddHealth dataset is presented. The techniques proposed in this research will provide an important first step in the subsequent analysis of outcomes which are influenced by how individuals connect to each other. Examples include educational outcomes and information transmission.

对非价格中介的相互作用的理解在许多经济现象中很重要。例如，一家公司进入市场的决定可能会受到其他公司决定的影响。在课堂上，同伴效应可能会影响结果和决定，这反过来又可能在以后的生活中发挥重要作用。还有许多其他的例子。由于决策和结果之间的相互作用，这些现象的实证分析与个人结果和决策的实证分析有关，但提出的挑战超出了个人决策分析中通常发现的挑战。本研究计划探讨互动模式实证分析的方法论及实质应用。其中一个主要挑战是，一个特定的模式可能适用于一组以上的预测结果或行为（即“均衡”）。在这种情况下，不清楚如何从观测数据中检索重要的模型组件。在第一组研究中，研究人员扩展了先前的工作，其中研究人员和合著者研究了某些信息对参与者来说是私有的交互模型的识别（de Paula和Tang，2011）。在这篇文献中的典型假设下，他们表明，当多个解决方案是可能的，不同的数据点对应于潜在的不同解决方案时，即使使用非常松散定义的模型，也可以推断出某人是否被激励或劝阻采取行动（当其他人这样做时）。作为副产品，研究人员还能够测试数据中是否存在多个解决方案。这一点很重要，因为多重性对于目前可用的许多估计技术来说是有问题的。研究人员将这一分析扩展到几个方向。首先，尽管在标准假设下，检测多个解决方案的基本见解仍然适用于动态游戏，但由于一个人目前的行为会影响未来的环境，因此检索交互效果的结果会受到影响。其次，即使在最初研究的静态环境中，连续分布的控制变量，如价格或收入，在估计中造成实际的复杂性。由于这些变量的不同值可能会在基础经济中产生不同数量的解，因此交互效应的典型非参数估计会聚集在协变量上，从而影响人们对解的唯一性的推断，从而影响交互效应的符号。最后，在许多应用中，人们可以很容易地标记相关参与者（例如，丈夫和妻子，沃尔玛和当地商店），在其他情况下，标签不自然地呈现它们自己。此外，研究者还探讨了这对相互作用效果的推断的影响，并将其应用于近年来研究较多的大学室友间的同伴效应分析。在相关研究项目中，研究者重点研究了人们选择交往对象的实证模型（即“网络形成游戏”）。最近，在经济学和社会科学中，对网络现象产生了很大的兴趣。例如，在这两种环境中找到多重均衡是很常见的。由于网络形成模型与实证产业组织文献中通常研究的模型有许多相似之处，因此，类似于先前在分析该文献中使用的思想和技术可以适用于社交网络的研究。为了演示本研究中开发的方法，使用AddHealth数据集进行了说明。在这项研究中提出的技术将提供一个重要的第一步，在随后的分析结果的影响，个人如何连接到彼此。这方面的例子包括教育成果和信息传播。