PUBLICATIONS

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

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

SINCERE OR MOTIVATED? PARTISAN BIAS IN ADVICE-TAKING

(SUBMITTED TO NATURE HUMAN BEHAVIOUR)

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 UPON REQUEST. PREPARING FOR SUBMISSION TO MANAGEMENT SCIENCE)

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

(DRAFT AVAILABLE UPON REQUEST)

(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 to Management Science.)

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.