Cade Massey

About

Cade Massey is a Practice Professor in the Operations, Information and Decisions Department at the Wharton School. He received his PhD from the University of Chicago and has taught at Duke University and Yale University before joining Penn. His research focuses on judgment under uncertainty, specifically how and how well people predict future events. His work draws on experimental and real-world data, including employee stock options, 401k savings, NFL draft, and graduate school admissions, leading to collaborations with organizations such as Google, Merck, and various professional sports franchises. Massey's research has been published in leading psychology and management journals and has been covered by prominent media outlets including The New York Times, Wall Street Journal, Washington Post, The Economist, and NPR. He has extensive teaching experience in MBA and Executive MBA courses on negotiation, influence, organizational behavior, and human resources, and co-teaches the Wharton 'People Analytics' MOOC on Coursera. Additionally, he is faculty co-director of Wharton People Analytics, co-host of 'Wharton Moneyball' on SiriusXM, and co-creator of the Massey-Peabody NFL Power Rankings.

Research topics

• Computer science
• Psychology
• Economics
• Algorithm
• Social psychology

Selected publications

• Algorithm aversion: People erroneously avoid algorithms after seeing them err.

  Journal of Experimental Psychology General · 2014-11-17 · 2363 citations

  articleSenior author

  Research shows that evidence-based algorithms more accurately predict the future than do human forecasters. Yet when forecasters are deciding whether to use a human forecaster or a statistical algorithm, they often choose the human forecaster. This phenomenon, which we call algorithm aversion, is costly, and it is important to understand its causes. We show that people are especially averse to algorithmic forecasters after seeing them perform, even when they see them outperform a human forecaste…

  PublisherDOI

• Overcoming Algorithm Aversion: People Will Use Imperfect Algorithms If They Can (Even Slightly) Modify Them

  Management Science · 2016-11-04 · 1084 citations

  articleOpen accessSenior author

  Although evidence-based algorithms consistently outperform human forecasters, people often fail to use them after learning that they are imperfect, a phenomenon known as algorithm aversion. In this paper, we present three studies investigating how to reduce algorithm aversion. In incentivized forecasting tasks, participants chose between using their own forecasts or those of an algorithm that was built by experts. Participants were considerably more likely to choose to use an imperfect algorithm…

  PublisherOA PDFDOI

• Algorithm Aversion: People Erroneously Avoid Algorithms after Seeing Them Err

  SSRN Electronic Journal · 2014-01-01 · 353 citations

  articleOpen accessSenior author
  PublisherDOI

• The mixed effects of online diversity training

  Proceedings of the National Academy of Sciences · 2019-04-01 · 305 citations

  articleOpen access

  = 3,016) field experiment at a global organization testing whether a brief science-based online diversity training can change attitudes and behaviors toward women in the workplace. Our preregistered field experiment included an active placebo control and measured participants' attitudes and real workplace decisions up to 20 weeks postintervention. Among groups whose average untreated attitudes-whereas still supportive of women-were relatively less supportive of women than other groups, our diver…

  PublisherOA PDFDOI

• The Loser's Curse: Decision Making and Market Efficiency in the National Football League Draft

  Management Science · 2013-03-19 · 164 citations

  articleOpen access1st authorCorresponding

  A question of increasing interest to researchers in a variety of fields is whether the biases found in judgment and decision-making research remain present in contexts in which experienced participants face strong economic incentives. To investigate this question, we analyze the decision making of National Football League teams during their annual player draft. This is a domain in which monetary stakes are exceedingly high and the opportunities for learning are rich. It is also a domain in which…

  PublisherOA PDFDOI

Frequent coauthors

• James J. Choi

  68 shared

• Emily Haisley

  66 shared

• Jennifer Kurkoski

  Google (United States)

  66 shared

• Joseph P. Simmons

  11 shared

• Berkeley J. Dietvorst

  University of Chicago

  7 shared

• Richard H. Thaler

  6 shared

• Ron Kaniel

  University of Rochester

  5 shared

• George Wu

  Wenzhou Medical University

  5 shared

Labs

• Wharton People LabPI

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