Research

People often face environments where multiple models compete to explain the same observations. This paper examines how people update beliefs in such settings and how preferences over payoff-relevant states shape model selection and belief updating. This paper first develops a framework where preference-driven bias distorts the perceived model, affecting Bayesian and best-fit updating differently. In a laboratory experiment, most participants are classified as Bayesian updaters, who average across models, while a substantial minority are classified as best-fit updaters, who select the model that best fits the observed signal. Within-participant comparisons between the symmetric payoff and asymmetric payoff conditions indicate that asymmetric payoffs shift reported beliefs toward the preferred state, particularly among participants classified as best-fit updaters. Relative to symmetric payoffs, asymmetric payoffs increase the reported belief of the preferred state by about 8 percentage points among best-fit updaters, while the estimated effect among Bayesian updaters is close to zero. These findings help us better understand model-based learning and have implications for domains such as political polarization and financial investment, where competing narratives and strong preferences often coexist.

This paper investigates the roles of responsibility, ex-post rationalization, and belief in sunk-cost decisions through a two-stage investment task in a laboratory setting. The experiment finds a reverse sunk-cost effect after a low signal: Participants are about 10 percentage points less likely to make additional investments in the presence of exogenous sunk costs. This effect is not detectable when participants are not responsible for their initial investment decisions. I find no evidence that ex-post rationalization affects the magnitude of sunk-cost bias, and sunk costs do not systematically distort participants’ beliefs about future project success.

Cooperation failures in social dilemmas persist because individually rational behavior yields inefficient collective outcomes. Advances in AI raise two possibilities: AI may improve outcomes by advising humans or by acting autonomously. We test both in a repeated threshold public-goods experiment with heterogeneous valuations of the public good. Such threshold games model a broad class of burden-sharing problems (crowdfunding, shared infrastructure, multilateral agreements) in which efficiency requires not only coordination but agreement on a cost-sharing norm. We compare a human-only benchmark to treatments with an AI advisor (OpenAI's GPT-5) and to treatments in which AI agents make allocations directly. AI Only groups outperform human groups, reaching the threshold more often. The mechanism is illuminating: AI agents contribute near-equal amounts largely independent of valuations, whereas humans scale contributions with valuations. In contrast, AI advisors do not improve human-only outcomes. We distinguish two bottlenecks to coordination-an information bottleneck, in which parties lack the calculations needed to condition on others' behavior, and a legitimacy bottleneck, in which parties reject cost-sharing rules that conflict with their fairness norms. AI overcomes the first but not the second.

Workers and organizations routinely choose between complex tasks that differ in their success-payment tradeoff. We study how cognitive ability and overconfidence shape this choice of task complexity, both theoretically and experimentally. Consistent with our model, we find that more cognitively able individuals choose more complex tasks. Conditional on cognitive ability, we find that more overconfident individuals also choose more complex tasks. In about 75% of the decisions, subjects pick their payoff-maximizing task, and higher cognitive ability is weakly associated with more efficient choices of task complexity. But errors are strikingly one-sided: those who fail to choose optimally pick tasks that are too complex. Our findings suggest that organizations should pair task menus and incentives for choosing more complex tasks with information and feedback that help workers assess their ability and probability of success. This preserves the benefits of self-selection while reducing costly overreach.

In a laboratory bargaining experiment with a query-and-answer stage, informed sellers privately observe the realized value and provide buyers with multi-attribute reports before negotiating the price. Bounded recall makes fabrication harder than truth-telling: misreporting sellers must recall attributes they can no longer see. Relative to perfect recall, bounded recall increases truthful reporting, acceptance rates, and total surplus, while reducing inequality among realized trades. Buyers respond to the internal consistency of sellers’ answers. Seller-selected questions weaken these gains. The cognitive burden of sustaining a fabrication thus generates credibility, while control over question selection shapes bargaining power.

A student’s major choice is pivotal for academic progress and later labor-market outcomes. This paper examines an overlooked driver of major selection: unexpected grade shocks in early years in college. Using administrative records from a large public research university in the Midwest, we combine course evaluation data that elicit students’ expected letter grades just before final exams with realized grades to construct grade shocks (actual minus expected). We find that unexpected negative shocks significantly increase the likelihood that students switch out of their current major in the subsequent term. The response is stronger for women: a one standard-deviation negative shock raises the probability that a female student leaves her major by about 2.5 percentage points relative to female peers without such a shock. These results highlight the role of early performance signals in major sorting and reveal meaningful gender heterogeneity in responsiveness to adverse academic feedback.