Essays on Behavioral Economics

Abstract

This dissertation studies economic interactions with behavioral agents using both theory and experiments across three chapters. The first two chapters study human misperceptions of GenAI capabilities and their consequences for AI usage. The first introduces the notion of Human Projection: people project human features onto AI when forming beliefs about its performance, which affects their usage decisions. A lab experiment shows that people project human task difficulty when evaluating AI, leading them to overestimate AI performance on human-easy tasks and underestimate it on human-difficult ones. A field experiment shows that among mistakes made by AI, those deemed less reasonable---from a human perspective---induced significantly larger breaches of trust in AI and further reduced user engagement. The second chapter studies the consequences of projection for equilibrium adoption of AI. Projection-prone beliefs can produce an all-or-nothing strategy: users either fully delegate tasks to AI or fully reject it, even when optimal use is task-contingent. Manipulating the framing of AI to remove anthropomorphic cues raises welfare-maximizing adoption, thereby highlighting a potential pitfall of anthropomorphism. The last chapter studies the role of audience effects on displayed moral universalism. When anticipating future interactions with their audience, experimental subjects slant their decisions toward perceived audience preferences. This strategic display is effective in raising audience cooperation and increasing subjects' payoff. Results caution against inferring intrinsic social preferences from public behavior when strategic image concerns are active.