Bilateral Bargaining through the Lens of Big Data

双边谈判无处不在，几千年来一直是人类互动的一部分。人们在零售商品、专业服务、工资、房地产、领土边界、兼并和收购、家务等方面讨价还价。对于一项如此普遍的经济活动，它已被证明是一项不容易分析的活动，更不用说完全理解了。由于谈判双方不确定对方在谈判中的立场，经济理论认为，在达成协议时将出现诸如不必要的延误甚至谈判完全破裂等低效率现象。本项目将探讨这些信息摩擦如何影响讨价还价，以及是否可以通过使用双方之间的沟通来减轻它们，这可以作为建模谈判的框架。研究人员将利用经济理论、计量经济学和机器学习等现代工具，探索讨价还价中的沟通过程，以弥合理论丰富与人们实际讨价还价的实证研究之间的一些差距。在此过程中，研究人员将雇用和培训研究生和本科生，他们将学习如何利用和分析“大数据”，以及如何将数据见解与博弈论和经济学中的讨价还价理论概念联系起来。研究结果将揭示如何促进议价和创造价值，随着在线议价平台的日益普及，这可能会发挥重要作用。这个项目是基于对易趣网“最佳报价”平台上数百万笔议价交易的新数据的访问，在这个平台上，卖家以列出的价格提供商品，并邀请买家进行交替的、连续的报价议价。数据的性质——高维，有时稀疏和非结构化——不仅受益于，而且实际上需要经济学、统计学和机器学习方法的合作，这解释了该项目方法的多样性和研究小组的组成。调查人员使用各种方法来利用从eBay.com获得的独特而广泛的数据。第一组分析描述了ebay上买卖双方讨价还价的方式。第二组分析利用eBay的数据探讨了参与议价的代理之间的沟通，这些数据包含了与Best Offer列表的报价一起发送的存储信息。某些结果（例如，卖方的还价）的概率将使用机器学习技术进行建模，并且在所得的预测模型中，研究人员将隔离消息内容的作用。这种内容效应将为沟通在议价中的重要性提供描述性分析。最后一组分析探讨了“廉价谈话”在讨价还价中的作用。基于廉价谈话信号的理论模型，研究人员提出了一套揭示均衡行为的测试，并应用一套回归不连续技术来验证行为符合均衡理论预测的方式。

Bilateral bargaining is pervasive, and has been part of human interaction for millennia. People bargain over retail goods, professional services, salaries, real estate, territorial boundaries, mergers and acquisitions, household chores, and more. For an economic activity that is so pervasive, it has proven to be one that is not easily analyzed, let alone fully understood. Because bargaining parties are uncertain about the bargaining position of their counterpart in bargaining, economic theory suggests that there will be inefficiencies such as unnecessary delays in reaching an agreement or even the complete breakdown of negotiations. This project will explore how these informational frictions affect bargaining and whether they can be mitigated with the use of communication between the parties, which serves as a framework for modeling negotiation. The investigators will explore the communication process in bargaining using modern tools from economic theory, econometrics and machine learning in order to bridge some of the gap between the abundance of theory and the rather slim availability of empirical studies on how people actually bargain. In the process, the investigators will employ and train graduate and undergraduate students who will learn how to exploit and analyze "big data" and how to tie the data insights to theoretical notions of bargaining from game theory and economics. The results will shed light on how to facilitate bargaining and create value, which is likely to play an important role with the growing prevalence of online bargaining platforms. This project is based on access to novel data of millions of bargaining transactions on the eBay.com "Best Offer" platform, where sellers offer items at a listed price and invite buyers to engage in alternating, sequential-offer bargaining. The nature of the data -- high-dimensional and sometimes sparse and unstructured -- not only benefits from, but in fact requires the collaboration of methods from economics, statistics and machine learning, which explains the diversity of approaches and the composition of the group of investigators for this project. The investigators use a variety of methods that will exploit the unique and expansive data obtained from eBay.com. A first set of analyses describe the way in which bargaining unfolds between buyer-seller pairs on eBay.com. A second set of analyses explore the communication between agents engaged in bargaining using data from eBay that contains stored messages sent alongside offers for Best Offer listings. The probability of certain outcomes (e.g., a counteroffer from the seller) will be modeled using machine learning techniques, and within the resulting predictive models the investigators will isolate the role of message content. This content effect will provide a descriptive analysis of the importance of communication in bargaining. The last set of analyses explores the role of "cheap talk" in bargaining. Based on theoretical models of cheap talk signaling, the investigators propose a set of tests that unveil equilibrium behavior and apply a set of regression discontinuity techniques to verify the ways in which behavior conforms with equilibrium theory predictions.