Essays on Cooperation and Competition in Strategic Environments

Alecia Evans, Purdue University

Abstract

In many economic settings agents behave strategically. Understanding and, sometimes regulating, that behavior is often crucial to enhance the efficiency with which scarce resources are allocated. A peculiar feature of economics is that cooperation among agents sometimes boosts efficiency, and sometimes hinders it. Social dilemmas, highly ubiquitous in economics, are situations in which cooperation boosts efficiency. Highly concentrated markets where a few firms operate, are situations in which cooperation (also known as collusion) among firms hinders efficiency. In such markets competition, rather than cooperation, boosts efficiency. In this dissertation, I study how uncertainty affects cooperation in social dilemmas, and how the presence of cooperative firms affects competition in concentrated markets.Both of the settings I study in this dissertation (social dilemmas with noisy payoffs and duopsony with endogenous location and pricing strategy) face a similar challenge. Their complexity compromises the tractability of conventional equilibrium concepts. In other words, Nash equilibria do not exist, or there is a multiplicity of equilibria. This, in turn, precludes comparative static analyses characterizing the effect of exogenous market forces (uncertainty and firm ownership structure) on market and welfare outcomes.I address this key challenge through a combination of genetic algorithms and laboratory experiments. A genetic algorithm consists of a selection process that identifies strategies that perform better than others, on average. Therefore, surviving strategies constitute, in a sense, average best responses. More than one strategy may survive. This happens when none of the surviving strategies is weakly dominated by the other surviving strategies. An equilibrium is a combination of surviving strategies. In this context, a comparative static analysis consists of the change in equilibrium (combination of surviving strategies) due to a change in exogenous forces. These comparative static analyses generate testable hypotheses. In Essays 1 and 2, I implement laboratory experiments to test these hypotheses.In Essay 1, I compare infinitely repeated social dilemmas with deterministic and noisy payoffs. I test whether noise in payoffs (where noisy payoffs are generated by a random shock and are uncorrelated amongst agents), which introduces imperfect monitoring, affects cooperation. Experimental evidence shows that imperfect monitoring reduces cooperation because it hinders agents’ ability to threaten defectors with a reciprocal defection. Therefore, noise reduces efficiency by unraveling cooperation in social dilemmas. In Essay 2, I study whether correlation among agents’ noisy payoffs strengthens monitoring and restores cooperation. Experimental evidence shows that stronger (though still imperfect) monitoring due to correlation helps cooperation if and only if agents are prone to cooperate in the initial rounds of the repeated game. Therefore, correlation among shocks affecting agents’ payoffs may or may not increase efficiency depending on the type of players participating in the social dilemma.Finally, in Essay 3, I use a genetic algorithm to generate comparative statics characterizing the effect of a cooperative firm on market equilibrium and efficiency in a spatial duoposony. A Nash equilibrium in this setting does not exist when location, price, and the degree of spatial price discrimination are all endogenous in the seminal Hotelling’s model. I use a genetic algorithm to identify a stable equilibrium in this setting. I find that a cooperative firm increases efficiency. But, counterintuitively, it does so when the cooperative does not directly compete with the privately owned firm. This is because the cooperative maximizes market share when its procurement region does not overlap with the privately owned firm’s procurement region.

Degree

Ph.D.

Advisors

Sesmero, Purdue University.

Subject Area

Agriculture|Artificial intelligence|Theoretical Mathematics

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