Working Papers

Talking to a Chatbot: AI Recommendations and Belief Updating

Job Market Paper

Abstract: This study investigates whether AI-powered chatbots are more persuasive than static sources of information. In a lab experiment, participants reported their beliefs about the correct answer to a series of logical reasoning questions both before and after receiving recommendations generated by the same AI model. In one treatment, participants received real-time recommendations from a chatbot, while in the other, they received static, pre-generated recommendations. The informational content of the recommendations, including both the recommended answers and the reasoning behind them, was distributionally matched across treatments. Participants who interacted with the chatbot were slightly more likely to update their beliefs following the AI recommendations, with a 31.46 percentage point change compared to 26.31 in the static treatment (p = 0.084). Further analyses reveal that the chatbot was especially persuasive among participants whose initial beliefs clashed with the recommendations.

Monitoring as a Service

Abstract: This study explores whether a monitoring service can help mitigate procrastination and improve commitment. In a real-effort experiment, participants needed to complete 80 tasks across three sessions over three weeks. In the first week, after making a non-binding plan for how many tasks to complete during the final two weeks, they were randomly assigned to either a Control or a Monitored group. In the Monitored group, participants were connected with a Monitor via WhatsApp, who would observe their progress and send out reminders if they delayed their work. Monitoring improved adherence to the initial plan by 12.75 percentage points (p < 0.05) and completing all the required tasks by 19.47 percentage points (p < 0.001), mainly by reducing dropout rate. The findings suggest that combining external oversight with performance-related reminders can be an effective method to combat procrastination.



Work in Progress

Emotional Discrimination

(with Sigrid Suetens and Boris van Leeuwen)

Abstract: We study whether emotionality in the decision context has an influence on ethnic discrimination. Participants from an ethnic majority group act as responders in an ultimatum game experiment where they receive a low offer. The experiment uses a two-by-two design, varying the proposer’s ethnic background and the responder’s emotional state. Proposers either have a similar ethnic background to the responder or belong to an ethnic minority group. Responders make decisions in either a “hot” context (seeing the offer before deciding) or a “cold” context (deciding before seeing the offer). Our results show no evidence that emotions increase ethnic discrimination: responders are no more likely to reject low offers from minority proposers than majority proposers, regardless of the decision context.