PUBLICATIONS
FIGHTING COVID-19 MISINFORMATION ON SOCIAL MEDIA: EXPERIMENTAL EVIDENCE FOR A SCALABLE ACCURACY-NUDGE INTERVENTION
(PSYCHOLOGICAL SCIENCE, 2020)
Gordon Pennycook, Jon McPhetres, Yunhao Zhang, Jackson G. Lu, David G. Rand
David Holtz, Michael Zhao, Seth G Benzell, Cathy Y Cao, M Amin Rahimian, Jeremy Yang, Jennifer Nancy Lee Allen, Avinash Collis, Alex Vernon Moehring, Tara Sowrirajan, Dipayan Ghosh, Yunhao Zhang, Paramveer S Dhillon, Christos Nicolaides, Dean Eckles, Sinan Aral
HOW DO SUCCESSFUL SCHOLARS GET THEIR BEST RESEARCH IDEAS? AN EXPLORATION
(MARKETING LETTERS, 2019)
Cathy Cao, Xinyu Cao, Matthew Cashman, Madhav Kumar, Artem Timoshenko, Jeremy Yang, Shuyi Yu, Jerry Zhang, Yuting Zhu, and Birger Wernerfelt
WORKING PAPER
THE REVEALED EXPERTISE ALGORITHM: LEVERAGING ADVICE-TAKING TO IDENTIFY EXPERTS AND IMPROVE WISDOM OF CROWDS
JOB MARKET PAPER! RESUBMIT TO MANAGEMENT SCIENCE
Conference Talks at
Informs Advances in Decision Analysis Conference 2022
ACM Collective Intelligence Conference 2021
Informs SJDM Annual Meeting 2020
MIT Conference on Digital Experimentation 2020
Informs Marketing Science Conference 2020
Seminar Presentations at
Max Planck Institute Center for Adaptive Rational
MIT Behavioral Economics Lunch
MIT Behavioral Research Lab
MIT Sloan Marketing Seminar
MIT Human Cooperation Lab
With David G. Rand
Conference Talks at
University of Pennsylvania NoBeC (Norms and Behavioral Change) Talks for Early Career Researchers 2022
Informs Marketing Science Conference 2021
Seminar Presentations at
UCL Affective Brain Lab
MIT Behavioral Research Lab
MIT Sloan Marketing Seminar
MIT Human Cooperation Lab
UNDERSTANDING ALGORITHM AVERSION: WHEN DO PEOPLE ABANDON AI AFTER SEEING IT ERR?
(DRAFT AVAILABLE IN JULY 2022 UPON REQUEST)
With Renee R. Gosline
Conference Talks at
Informs Marketing Science Conference 2022
MIT Initiative on the Digital Economy Annual Conference 2022
Seminar Presentations at
MIT Human Cooperation Lab
MIT Behavioral Research Lab
A BOUNDEDLY RATIONAL MODEL OF THE DISTANCE EFFECT IN ADVICE-TAKING
(Note: this computational model was originally written for the paper about the Revealed Expertise Algorithm. Following the suggestions by the referees at Management Science, this model is now a separate paper under preparation for submission.)
Abstract:
The distance effect is a widely documented empirical phenomenon that the people tend to put less weight on advice (WOA) as the numerical distance between their initial estimate and the advice increases (Yaniv 2004, Minson et al. 2011, Schultze et al. 2015). Standard Bayesian models cannot capture this effect and no satisfactory model has been offered to explain what decision heuristics lead to this behavior. In this paper, we show that the distance effect may be a consequence of agents following semi-Bayesian advice-taking heuristics given fully Bayesian computations are cognitively infeasible. We further empirically test several additional predictions of our model and demonstrate their important implications in advice-taking and persuasion.