Bilateral Bargaining through the Lens of Big Data
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.
This project is supported by the National Science Foundation under grant number 1629060.
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- Author: Shane Greenstein