Essays on Decision Making under Risk and Uncertainty

This dissertation uses economic theory, in tandem with experiments and other empirical methods, to better understand the role of information – for instance, missing information, conflicting information, and information overload – in determining outcomes in decision settings characterized by risk and uncertainty. In my first chapter, I use theory and experiments to compare market outcomes in a setting where the seller has better information on product quality than the buyer, and examine the effects of introducing a third-party who can credibly relay information on product quality. Under a range of conditions, I find market efficiency is higher when the information intermediaries interact with buyers instead of sellers. Moreover, market outcomes depend on many other factors, including the accuracy of the certification process and the level of competition in the certification market.

My other dissertation chapters examine decision making in settings characterized by ambiguity, where agents may not form unique beliefs about the probabilities associated with potential outcomes either because there is too little information, or too much conflicting information is provided. In the second chapter, I design a novel experiment that takes advantage of information updating to test the predictive power of two popular classes of ambiguity aversion models, and the experimental results suggest that the “kinked” rather than “smooth” specification better predict subjects’ behavior. In the third chapter, I conduct MATLAB simulations to study the timing of technology adoption in response to climate change when predictions from multiple climate models are available. Using the data from California’s Central Valley, my simulation results show that an increase in the confliction between projections accelerates the adaption speed of new technology, while an increase in the volatility of projections delay the procedure.