Social, Behavioral and Experimental Economics Seminar: Park Sinchaisri, UC Berkeley

Title:  Algorithmic Advice, Human Compliance, and Learning

Abstract:

Organizations increasingly use algorithms to support operational decisions, yet these systems often influence human behavior rather than determine actions directly. This creates a central behavioral operations question: how should firms design decision support when performance depends not only on the quality of the recommendation, but also on whether people follow it and what they learn from it over time? In this talk, I present research on the design of algorithmic advice in sequential decision environments. I first show that there is a fundamental trade-off between precise, action-level advice and broader, strategic guidance. Precise advice is easier to implement and improves short-run performance, whereas broader guidance can foster more transferable learning when conditions change. I then discuss ongoing work on recommendation design in settings where humans retain meaningful discretion, highlighting how compliance, trust, and the surrounding decision environment shape the realized value of algorithmic support. Together, these projects offer a behavioral operations perspective on how firms can design advice systems that improve immediate operational performance without neglecting long-run human capability.

Current version of the paper: https://www.dropbox.com/scl/fi/7mlt8xeswtj2gk81qeiyu/Precise-or-Broad-2026.pdf?rlkey=jkp7e8nil8abkua2ovpmi70mm&dl=0

Bio:

Park Sinchaisri is an Assistant Professor of Operations and IT Management at the Haas School of Business, University of California, Berkeley. His research draws on operations management, economics, machine learning, and behavioral science to study human decision-making in complex environments, design human-AI systems that improve decision-making, and develop strategies for managing the future of work. His work has been published in Management Scienceand Manufacturing & Service Operations Management, and has also appeared in leading human-computer interaction venues including CSCW. He received his PhD in Operations, Information and Decisions and an AM in Statistics from the Wharton School of the University of Pennsylvania, an SM in Computational Science and Engineering from MIT, and an ScB in Computer Engineering and Applied Mathematics-Economics from Brown University. Originally from Bangkok, Thailand, he hopes his research can help address urban challenges and improve outcomes for marginalized workers.

CV: https://www.dropbox.com/scl/fi/38rt0hsxk8113eaxr2gom/cv.pdf?rlkey=ivrm1kf9ve71bmf63081of0rd&dl=0

About the SBEE Seminar Series

The Social, Behavioral and Experimental Economics seminar series brings together a community of economics scholars from three units at the University of Michigan — the School of Information, the Department of Economics and the Ross Business School — whose research aims to broaden the understanding of the social, economic and political consequences of real-life decisions and behaviors.

Top researchers from around the globe come to Michigan to present their work at the SBEE seminar series, exploring the intersection of economics, psychology, computer science and information science.

The seminar series is organized by U-M faculty members Yan Chen (UMSI), Alain Cohn (UMSI), Erin Krupka (UMSI), Stephen Leider (Ross), Christine Exley (Econ), A. Yesim Orhun (Ross), Tanya Rosenblat (UMSI), Karthik Srinivasan (UMSI) and Basit Zafar (Econ). Todd Stuart and Robin Kocher serve as seminar coordinators.