UC Santa Cruz

This dissertation presents three studies with an emphasis on the influence of information and technology in people’s behavior. The first chapter focuses on theoretical and experimental evaluation of the information revelation strategies in a persuasion game. The second and third chapters focus on how advanced technology such as ridehail and social media apps can change people’s access to information and their behavior.

In Chapter 1, we design a persuasion game in which two players compete for limited resources under asymmetric information and conflicting interests to study whether verifiable but vague messages can improve information transmission. The predictions are derived using two theoretical solution concepts, Perfect Bayesian Equilibrium (PBE) and Iterative Admissibility (IAS), both restricted to pure strategies. In a laboratory experiment, we observe behavior to be consistent with the highest reasoning level under IAS. Our evidence shows that the senders' pure strategies which are PBE and satisfy the highest-level IAS are the most commonly chosen strategies. When vague messages can be sent, senders reveal more information using vague messages, and receivers have more accurate beliefs about the true state.

In Chapter 2, we analyze what ridehail drivers do when searching for paid fares. We use a dataset of 5.3 million trips in San Francisco and partition each search trip into cruising, repositioning, and parking segments. We find that repositioning accounts for nearly two-thirds (63%) of the time between trips, with cruising and parking accounting for 23% and 14% respectively (these figures exclude short trips). Our regression models suggest that drivers tend to make reasonable choices between repositioning and parking, heading to high-demand locations based on the time of day. However, we also find suggestive evidence of racial bias, supporting previous studies of both taxis and ridehailing that indicate that drivers tend to avoid neighborhoods with high proportions of residents of color.

The final chapter investigates personalization in online social networks, which has been constantly criticized for intensifying opinion polarization. Yet polarization can result from confounding effects. We build a model which combines an endogenous network formation process and endogenous probability of observing agents. By separating the influences of different factors on polarization, the model is able to evaluate the pure effects of personalization and shows that stronger polarization occurs under personalization when agents are easier to be persuaded by others. We further conduct a novel lab experiment, and the results confirm our theoretical predictions. Additionally, the experiment results indicate that without personalization, a transitionary polarization occurred under a low disconnection threshold environment.