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Essays on information, competition, and cooperation

Citation

Boosey, Luke Anthony (2013) Essays on information, competition, and cooperation. Dissertation (Ph.D.), California Institute of Technology. http://resolver.caltech.edu/CaltechTHESIS:10272012-131405182

Abstract

This thesis consists of three papers that study the relationships between information, competition, and cooperation in two novel environments. We first examine the competitive behavior of firms with private information in two-sided matching markets. This part of the thesis employs purely game-theoretic tools. Second, we study voluntary contributions towards a linear public good by players who are connected through a network. In this environment, we use experimental and theoretical techniques to examine the effects of different information treatments and network structures on contributions.

In Chapter 2, we study the behavior of firms in a competitive market for workers. In particular, we study a game in which firms with private information compete for workers by committing to a single salary offer. Workers care only about salary and the matching process follows the deferred-acceptance approach introduced by Gale and Shapley (1962). For a two-firm, two-worker model, there exists a Bayesian-Nash equilibrium in which each firm type chooses a distributional strategy with interval support in the salary space. This equilibrium exhibits a separation of types, in the sense that types with a common most preferred worker choose non overlapping, adjacent supports. The type that makes higher offers is determined by the relative marginal value for the preferred worker. In larger markets, which replicate the two-firm, two-worker case, a comparable Bayesian-Nash equilibrium exists and the separation result endures. In the limit, there is no aggregate uncertainty about the realization of firm types, and competition is confined to the most popular worker type. Numerical results suggest that the finite market equilibrium strategies converge with replication to the corresponding equilibrium strategies in the limit case.

Chapter 3 studies individual contributions in a repeated public goods experiment. Subjects are connected through a circle network, and consumption of the public good depends on a player's own contribution and the contributions of his neighbors. We study whether contributions depend on the nature of the information players are shown about others between rounds of the repeated game. We extend the approach of Arifovic and Ledyard (2009), which merges a modified model of other-regarding preferences (ORP) with a theory of learning. Our model predicts individual behavior that ranges from free-riding, to conditional cooperation, to unconditional giving. Many subjects switch between these different behavioral strategies across games with different information treatments. Individual contributions are remarkably consistent with our model, which combines other-regarding preferences, learning, and the information treatment. Both the data and model simulations suggest that learning (to play the benchmark Nash equilibrium of the game) is differential and contagious across players. Free-riders and unconditional givers learn faster than conditional cooperators, and provide an anchor that accelerates learning by their neighbors. These results suggest that the network or neighborhood structure may be important for contributions through its effects on learning.

In Chapter 4, we extend the analysis of learning and contributions in network public goods experiments. Using a set of five different network structures, we examine three key aspects of individual behavior. First, we report a negative finding regarding the predictions from our theory of other-regarding preferences. The theory provides certain predictions about how a particular subject should and should not behave across networks. We find several violations of these predictions, particularly in small, complete network groups, but also in the larger, more interesting networks. Second, we report on the average contributions by players in groups that consist of all conditional cooperators. In the one-shot game, these groups have a continuum of equilibria, in which every player contributes the same amount. While one might expect contributions to average half of the endowment, we find in both the data and learning simulations that average contributions decline over time to less than half of the endowment. We conjecture that learning dynamics may provide a method of equilibrium selection, for players trying to coordinate on one equilibrium in the repeated game.

Our main finding in this chapter is that learning is contagious in networks other than the circle, which we studied in Chapter 3. We find considerable evidence at the individual match level that free-riders and altruists provide an anchor that stabilizes behavior and accelerates learning by their conditional cooperator neighbors. Our analysis highlights the possibility that, even when the distribution of free-riders, altruists, and conditional cooperators is the same across networks, the different neighborhood structures may affect contributions differently through their effects on learning. Thus, the main contribution of this chapter is the confirmation that learning is contagious across a range of different network structures.

Item Type:Thesis (Dissertation (Ph.D.))
Subject Keywords:Information, Matching, Networks, Learning, Other-regarding preferences, Public goods
Degree Grantor:California Institute of Technology
Division:Humanities and Social Sciences
Major Option:Social Science
Thesis Availability:Public (worldwide access)
Research Advisor(s):
  • Ledyard, John O.
Thesis Committee:
  • Ledyard, John O. (chair)
  • Echenique, Federico
  • Yariv, Leeat
  • Rosenthal, Jean-Laurent
Defense Date:10 July 2012
Record Number:CaltechTHESIS:10272012-131405182
Persistent URL:http://resolver.caltech.edu/CaltechTHESIS:10272012-131405182
Default Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:7247
Collection:CaltechTHESIS
Deposited By: Luke Boosey
Deposited On:15 Nov 2012 17:23
Last Modified:17 Jun 2013 19:19

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