| Popis: |
This thesis contains two theoretical essays on reciprocity and one that analyzes the effects of perception biases on learning and decision-making. In the first chapter, I propose a new theory of intention-based reciprocity that addresses the question of when a mutually beneficial action is kind. When both benefit from the action, a player’s motive is unclear: he may be perceived as kind for improving the other player’s payoff, or as self-interested and not-kind for improving his own. I use trust as an intuitive mechanism to solve this ambiguity. Whenever a player puts himself in a vulnerable position by taking such an action, he can be perceived as kind. In contrast, if this action makes him better off than his alternative actions do, even if it is met by the most selfish response, he cannot be kind. My model explains why papers in the literature fail to find (much) positive reciprocity when players can reward and punish. The second chapter extends my theory of reciprocity to incomplete information. I outline how reciprocity can give rise to pay-what-you-want pricing schemes. In the classic bilateral trade setting, I show that sequential interactions can be more efficient than normal form mechanisms when some people are motivated by reciprocity. Reciprocity creates incentives for information sharing. The last chapter is co-authored with Manuel Staab. We study the effects of perception biases and incorrect priors on learning behavior, and the welfare ranking of information experiments. We find that both types of biases by themselves reduce expected utility in a model where payoff relevant actions also generate informative signals, i.e. when actions constitute information experiments. However, experiments can be affected to different degrees by these biases. We provide necessary and sufficient conditions for when any binary ranking of action profiles can be reversed. Building on these findings, we show that an agent can be better off suffering from both biases rather than just one. |
