| dc.description.abstract | Chapter 1- The importance of the Probability of Winning in Risky Choices: Media reports say that high earners and syndicates buy lottery tickets in bulk. Experimental evidence shows that agents aggressively bid in auctions and contests. Do people try to trade-off probability of winning with other basic risk dimensions (for example, cost) to achieve a subjective threshold probability of winning (in environments they can) even when such choices are second-order stochastic dominated? The literature on risky choices suggests so. In the main design of this experiment, we deconstruct the expected value with variance and skewness of a lottery with Bernoulli distribution to examine the decision-making process. Based on the results, a proportion is classified as expected utility maximizer (EUM) while another proportion seems to achieve a subjective threshold probability of winning (termed as target probability of winning (TPW)). More TPWs prefer higher probabilities compared to EUMs in a constant value lottery set which may explain the preference for negative skewness in experiments. Additionally, we test two contest designs and find TPWs in the population which may explain the puzzle of equilibrium effort more than risk-neutral Nash equilibrium in experiments.
Chapter 2- Reinforcement Learning in Contests: We study contests as an example of winner-take-all competition with linearly ordered large strategy space. We study a model in which each player optimizes the probability of winning above some subjective threshold. The environment we consider is that of limited information where agents play the game repeatedly and know their own efforts and outcomes. Players learn through reinforcement. Predictions are derived based on the model dynamics and asymptotic analysis. The model is able to predict individual behavior regularities found in experimental data and track the behavior at the aggregate level with reasonable accuracy.
Chapter 3- A Mechanism and Matching in a Social Dilemma: Cooperation can be achieved via incentives from future interactions, specifically in the case of public monitoring. But, today, our social and professional spheres keep shifting rapidly and we interact often with strangers. We are interested in such sporadic interactions which can be modeled as a continuous Prisoner’s Dilemma in an environment of the symmetric market where the whole population is competing among themselves to interact with other agents who will contribute the most. The interaction is private, only the agents involved know how much they have contributed to each other’s well-being, and partners may change in the next period. In such an environment if the reputation of agents is not available, then there is no incentive to cooperate. In this paper, we show that if an experience reporting mechanism facilitates assortative matching, then cooperation and honest reporting is evolutionarily (neutral) stable. | en_GB |
