Research Interests
Behavioral Economics; Experimental Methods; Applied AI
Professional Affiliations
Center for Applied AI, Research Affiliate
J-PAL, Invited Researcher
Innovation Growth Lab, Network Member
JILAEE, Research Affiliate
CESifo, Network Affiliate
Social Profiles
Twitter
LinkedIn
Google Scholar
Booth profile
ORCID
Contact Information
brian.jabarian@chicagobooth.edu
The University of Chicago
Booth School of Business
5807 S. Woodlawn Ave.
Chicago, Illinois 60637 USA
I am an economist studying how emergent technologies reshape human behaviors and economic institutions.
I use experimental methods at the intersection of behavioral economics and applied AI to study how artificial intelligence transforms work, decision-making, and the design of firms and markets: (1) I partner with AI-driven companies to conduct natural field experiments that identify the real-world causal impacts of AI; (2) In parallel, I design lab and simulated experiments to investigate how AI reshapes the cognitive foundations and behavioral mechanisms of human work.
I am the Howard and Nancy Marks Principal Research Fellow at the Roman Family Center for Decision Research, University of Chicago Booth Business School, and an affiliated researcher with the Booth Center for Applied AI. I received a PhD in economics from PSE in July 2023.
Curriculum Vitae
brian.jabarian@chicagobooth.edu
Thaler-Tversky Indenpdent Research Grant, 2025
Booth Center for Applied Artificial Intelligence Seed Grant, 2025
Google Cloud Education Research Program Grant, 2025
Becker-Friedman Institute, Research Program in Behavioral Economics, Research Seed Grant, 2024
Swiss National Fund Research Grant, co-PI recipient with P. G. Piacquadio, 2024-2027
Working Papers
Distributional Approach to Risk Preferences [New Paper]
with N. Chemaya, C. Johnson, E. Yeung, G. Charness
[pre-print available on request]
Abstract. We propose a distributional framework for eliciting risk preferences that treats an individual's attitude towards risk as a complete probability distribution rather than a point estimate. By parameterizing preferences with the flexible beta family, our approach encompasses the entire spectrum from extreme risk aversion to risk neutrality and even risk-seeking behavior, while simultaneously allowing for heterogeneous stability of those attitudes across contexts. Our agent-based simulations show that (i) the true underlying preference distribution is recoverable with negligible bias, and (ii) the precision of recovery is a systematic function of the elicitation design richness, providing clear guidance for experimental design. Benchmarking on the comprehensive laboratory dataset of Holzmeister & Schmidt (2021) confirms two central results: (1) the out-of-sample predictive accuracy is at least on par with the canonical point estimation methods, and (2) our method delivers a second, policy-relevant moment, the subject-specific variance of risk taking, without sacrificing parsimony.
A Two-Ball Ellsberg Paradox [New Update]
with S. Lazarus
[pre-print] [CESifo No. 10745]
Abstract. We introduce a novel experimental framework, the two-ball Ellsberg gamble, which allows us to explore a wider range of possible drivers of ambiguity attitudes than usually considered by the literature. In an incentivized experiment on a representative sample from the US with 708 participants, we find that 55 % of the subjects prefer avoiding ambiguity even when it means choosing dominated risky options -- what we call the Two-Ball Ellsberg Paradox. This aversion to mere exposure to ambiguity violates the monotonicity axiom of most current ambiguity models. In a series of treatments, we establish that the primary drivers of these behaviors are an aversion to complexity generated by the presence of ambiguity. Such a complexity aversion explains about 43% of the variations in the two-ball Ellsberg and 38% of the variations in the original Ellsberg paradox. We further explore the possible different cognitive foundations underlying such a result. Participants who are more likely to display the Two-Ball Ellsberg Paradox are also more likely to perceive the setting as complex.
Critical Thinking and Storytelling Contexts
with E. Sartori
[pre-print] [CESifo No. 11282]
Abstract. We argue that storytelling contexts – the way information is communicated through varying credibility sources, visual designs, writing styles, and content delivery – impact the effectiveness of surveys and elections in eliciting preferences formed through critical thinking (reasoned preferences). Through an artefactual field experiment with a US sample (N = 725), incentivized by an LLM, we find that intermediate storytelling contexts prompt critical thinking more effectively than basic or sophisticated ones. Sensitivity to these contexts is linked to individual cognitive traits, and participants with a high need for cognition are particularly responsive to intermediate contexts. In a conceptual framework, we explore how critical thinkers impact the efficiency of elections and polls in aggregating reasoned preferences. Storytelling contexts that effectively prompt critical thinking improve election efficiency. However, the indecisiveness of critical thinkers can have ambiguous effects on election bias, potentially posing challenges for principals who are required to act on these election outcomes.
Book Chapters, Surveys, Scientific Methods, and Policy Papers
The Next Generation of Experimental Research with LLMs
with Gary Charness, John List
Nature Human Behaviour, 2025: 1-3
[pre-print] [NBER No. 31679]
Covered in World Economic Forum, Chicago Booth Review, VoxEU Column
LLMs for Behavioral Economics: Ensuring Internal Validity and Elicitating Mental Models
Entry under preparation for the Elgar Encyclopedia of Experimental Social Science
[pre-print] [SSRN No. 4880892]
LLMs for Behavioral Economics: Synthetic Mental Models and Data Generalization
Entry under preparation for the Elgar Encyclopedia of Experimental Social Science
[pre-print] [SSRN No. 4880894]
The Virtues of Lab Experiments
with Charness, Gary Charness, James Cox, Charles Holt, and Catherine Eckel