This thesis is composed of three essays, the first two of which are on the economics of linguistic diversity and the last on the evolutionary foundation of the preference for surprise. Chapter 1 is joint work with Leanna Mitchell. We propose a theory that relates linguistic diversity (i.e. the number of languages within a region) to cooperative and competitive incentives in a game theoretic framework. In our model, autonomous groups interact periodically in games that represent either cooperation, competition, or no interaction. Language matters in these interactions because language common to a pair of groups facilitates cooperation; whereas language unique to one group affords that group an advantage in competitions against other groups. The relative frequency of cooperation and conflict in a region provide incentives for each group to modify their own language, and therefore leads to changes in linguistic diversity over time. Hence, a main contribution of our paper is to model strategic incentives as a cause of linguistic divergence. Our model predicts that higher frequency of cooperative interactions relative to competitive ones reduces a region’s linguistic diversity. Chapter 2 reports a laboratory experiment designed to test the theory proposed in the previous chapter. In the experiment, pairs of subjects endowed with a set of words interact repeatedly in a series of underlying games, in which they use the words to signal their intended action. The underlying games are either coordination or zero-sum. As the subjects are allowed to modify their vocabularies by learning words from their counterpart and creating new words, I observe that, over time, the pairs of vocabularies in coordination games tend to converge, while in zero-sum games, the vocabularies experience constant pressure to diverge. This finding is consistent with the theoretical predictions in Chapter 1. Chapter 3 uses a principal-agent model to provide an evolutionary explanation of the preference for surprise, where surprise is measured by the Kullback-Leibler divergence between a decision-maker’s prior and posterior. The principal in the model is interpreted as the blind force of evolution, who tries to maximize the fitness of the agent—generations of human beings—whose objective in turn is to maximize a utility function designed by the principal. In a typical period, the agent first decides how many signals about the state to purchase, and then he chooses an action that, together with the state, determines his fitness. The variance of the signal distribution changes across time, but the agent is predisposed to believe that it is the same as the one in the previous period. I show that if the variance of the signal distribution decreases at a sufficiently fast rate over time, it is evolutionarily optimal for the utility function to include a component that rewards surprises.
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Thesis advisor: Robson, Arthur
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