Behavioral Science Workshops

Workshop Details

• Where: Chicago Booth Harper Center, Classroom C06. Workshops will be offered IN-PERSON ONLY.
• When: Mondays 10:10–11:30 a.m. (unless otherwise noted)
• Who can attend: Workshops are open to Roman Family Center faculty, researchers, staff, and students, plus invited guests. Additional requests to attend the workshop are handled on a case-by-case basis. Please email breanna.foster@chicagobooth.edu if you’d like to attend.
• Archive: For a full list of presenters 2004-present, see our workshop archive.

 

Fall Workshop Series 

 

Monday, September 28, 2026

Jon Bogard
University of Washington in St Louis

"Some consequences of categorical reasoning"

People frequently simplify continuous information into cruder categorical bins. Across two lines of research, we examine how this practically useful strategy can distort judgments of predictive quality, affecting both how people evaluate others’ forecasts and how they learn relationships between variables. We argue that categorical agreement becomes a criterion for predictive quality. First, in forecast evaluation, we show that people often prefer forecasts that correctly predict an event’s categorical outcome (e.g., the winning team) to those that minimize continuous prediction error (e.g., the margin of victory). We argue that this is a normative mistake. Second, we document how categorical reasoning shapes judgments about probabilistic relationships. Specifically, we show that people may use a “category matching” heuristic, judging the strength of an x-y relationship by how frequently the categorical encodings of each variable align (e.g., how often the team that spent more money won more games, neglecting the underlying dollars-to-wins continuous relationship). Holding true cue-outcome correlation constant, we find that decreasing the frequency of category matches causes participants to evaluate cues as less predictive, make less accurate out-of-sample predictions, become less confident in their predictions, and perceive spurious cues as more valid. Together, these two bodies of work suggest that categories used to simplify information can become standards for judging predictive quality, shaping subsequent perceptions of what counts as correct or equivalent, with consequences for judgments of predictive validity.

Monday, October 5, 2026

Dobromir Rahnev
Georgia Tech

"How the mind computes confidence"

How do we know whether our decisions are likely to be right? Humans generate a sense of confidence seemingly effortlessly, yet the computations that give rise to this sense remain poorly understood. Most existing theories have been developed using simple two-choice tasks, leaving open whether they can explain confidence in the richer, multi-alternative decisions that characterize everyday life. My lab uses perception as a model system to uncover the computations underlying confidence in such decisions. Our results point to surprisingly simple principles that can depart sharply from optimal Bayesian inference, revealing when confidence faithfully tracks decision accuracy and when it systematically fails to do so. We are now extending this work to artificial systems, asking whether humans and machines evaluate their decisions using similar computations and whether understanding human confidence can help build machines that better know when they are likely to be wrong.

Monday, October 12, 2026

Marina Milyavskaya
Carleton University

"Pursuing personal goals: the role of the individual, the goal, and the context"

Setting and pursuing personal goals is ubiquitous in our day-to-day lives. In this talk, I will provide an overview of my research on personal goal pursuit, focusing on the individual differences, contextual factors, and goal characteristics that predict (and fail to predict) goal progress, and the self-regulatory mechanisms through which these predictors operate. Overall, this research shows that the path to success is in making goal pursuit feel easier by reducing obstacles, using strategies, and making the experience feel more intrinsically rewarding. Throughout the talk I will also highlight how this program of research uses a multi-method approach that includes experimental, prospective, and experience-sampling research as well as a focus on idiosyncratic personal goals.

Monday, October 19, 2026

David Yeager
University of Texas at Austin

"Insights from the science of motivating young people"

A major challenge facing employers, parents, educators, and policymakers concerns the behavior of young people, conventionally defined as the age range between 10 and 25. When the next generation’s behavior is harmful, short-sighted, or counter-productive, then it leads to inefficiencies stemming from ineffective programs and potential dangers for civil society and the labor market, not to mention consternation among caregivers and anyone else concerned for the future. This talk summarizes a program of research over the past two decades that has sought to understand young people from a theory-informed behavioral-science perspective, and then develop experimental treatments as well as field-tested programs to influence and improve young people’s decision-making and overall trajectories. Findings draw from experiments conducted in settings as diverse as public health / medication adherence, education, and the workplace. Implications are discussed for the journey from basic laboratory processes to authentic field experiments, as well as heterogeneity in impacts, and ways to use and learn from heterogeneity to better understand real-world behavior and design more robust solutions. Pre-read: My interview with Steve Levitt, or my book 10 to 25.

Monday, October 26, 2026

Oriel FeldmanHall
Brown University

*Topic and more details to be announced.