Identification and Inference of Strategic and Social Interaction Models

对不以价格为中介的相互作用的理解在许多经济现象中都很重要。例如，一个企业进入市场的决定可能会受到其他企业决策的影响。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。还有很多其他的例子。由于行为者之间的决策和结果的相互作用，对这些现象的实证分析与对个人结果和决策的实证分析有关，但提出了超出个人决策分析通常发现的挑战。本研究项目探讨交互模型实证分析的方法论方面和实质性应用。一个主要的挑战是，一个给定的模型可能适用于一组以上的预测结果或行为(例如：“平衡”)。在这种情况下，如何从观察到的数据中检索重要的模型组件并不清楚。在第一组研究中，研究人员扩展了之前的工作，其中研究者和共同作者研究了某些信息对参与者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，当多种解决方案是可能的，并且不同的数据点对应于潜在的不同解决方案时，即使使用非常松散的定义模型，也可以推断出某人是否受到激励或劝阻而不采取行动（当其他人采取行动时）。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，因为目前可用的许多估计技术都存在多重性问题。研究人员将这种分析扩展到了几个方面。首先，尽管在标准假设下，关于检测多个解决方案的基本见解仍然适用于动态博弈，但由于一个人当前的行为会影响未来的环境，因此交互效应检索的结果受到影响。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）在估计中也会造成实际的复杂性。因为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决方案，所以典型的相互作用效应的非参数估计会聚集在协变量上，这些协变量会影响人们对解决方案唯一性的推断，从而影响相互作用效应的符号。最后，尽管在许多应用程序中，人们可以很容易地标记相关的参与者（例如，丈夫和妻子，沃尔玛和当地商店），但在其他环境中，标签不会自然地呈现出来。研究人员还研究了这如何影响互动效应的推断，并将方法应用于分析近年来被广泛研究的大学室友之间的同伴效应。在一个相关的项目中，研究人员将重点放在人们选择与谁交往的经验模型上。“网络编队游戏”)。最近，人们对经济学和社会科学中的网络现象产生了浓厚的兴趣。例如，在两种环境中找到多重平衡是很常见的。由于网络形成模型与通常在实证产业组织文献中研究的模型有许多相似之处，因此类似于先前在这些文献分析中使用的思想和技术可以适用于社会网络的研究。为了演示本研究中开发的方法，给出了使用AddHealth数据集的示例。本研究中提出的技术将为后续分析受个体相互联系方式影响的结果提供重要的第一步。例子包括教育成果和信息传递。

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.