Bargaining and Pricing in Networked Economic Systems

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Doctor of Philosophy (PhD)
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Computer and Information Science
Theory and Algorithms
Computational Economics
Algorithmic Game Theory
Computational Finance
Network Economics
Behavioral Economics
Economic Theory
Theory and Algorithms
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Economic systems can often be modeled as games involving several agents or players who act according to their own individual interests. Our goal is to understand how various features of an economic system affect its outcomes, and what may be the best strategy for an individual agent. In this work, we model an economic system as a combination of many bilateral economic opportunities, such as that between a buyer and a seller. The transactions are complicated by the existence of many economic opportunities, and the influence they have on each other. For example, there may be several prospective sellers and buyers for the same item, with possibly differing costs and values. Such a system may be modeled by a network, where the nodes represent players and the edges represent opportunities. We study the effect of network structure on the outcome of bargaining among players, through theoretical modeling of rational agents as well as human subject experiments, when cost and values are public information. The interactions get much more complex when sellers' cost and buyers' valuations are private. We design and analyze revenue maximizing strategies for a seller in the presence of many buyers, when the seller has uncertain information or no information about the buyers' valuations. We focus on developing pricing strategies, and compare their performance against truthful auctions. We also analyze trading strategies in financial markets, where a player quotes both buying and selling prices, again with uncertain or no information about future price evolution of the financial instrument.

Michael Kearns
Sanjeev Khanna
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