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

• Learning to detect change: an experimental investigation

  Experimental Economics · 2025-04-14 · 1 citations

  articleOpen access

  Abstract People, across a wide range of personal and professional domains, need to accurately detect whether the state of the world has changed. Previous research has documented a systematic pattern of over- and under-reaction to signals of change due to system neglect , the tendency to overweight the signals and underweight the system producing the signals. We investigate whether experience, and hence the potential to learn, improves people’s ability to detect change. Participants in our study made probabilistic judgments across 20 trials, each consisting of 10 periods, all in a single system that crossed three levels of diagnosticity (a measure of the informativeness of the signal) with four levels of transition probability (a measure of the stability of the environment). We found that the system-neglect pattern was only modestly attenuated by experience. Although average performance did not increase with experience overall, the degree of learning varied substantially across the 12 systems we investigated, with participants showing significant improvement in some high diagnosticity conditions and none in others. We examine this variation in learning through the lens of a simple linear adjustment heuristic, which we term the “ δ - ϵ ” model. We show that some systems produce consistent feedback in the sense that the best δ and ϵ responses for one trial also do well on other trials. We show that learning is related to the consistency of feedback, as well as a participant’s “scope for learning” how close their initial judgments are to optimal behavior.

  PublisherOA PDFDOI

• Decision-making with Ordinal Ratings

  SSRN Electronic Journal · 2025-01-01

  articleOpen access
  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 diversity training successfully produced attitude change but not behavior change. On the other hand, our diversity training successfully generated some behavior change among groups whose average untreated attitudes were already strongly supportive of women before training. This paper extends our knowledge about the pathways to attitude and behavior change in the context of bias reduction. However, the results suggest that the one-off diversity trainings that are commonplace in organizations are unlikely to be stand-alone solutions for promoting equality in the workplace, particularly given their limited efficacy among those groups whose behaviors policymakers are most eager to influence.

  PublisherOA PDFDOI

• Small cues change savings choices

  Journal of Economic Behavior & Organization · 2017-08-25 · 90 citations

  articleOpen accessSenior author
  PublisherOA PDFDOI

• 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 when they could modify its forecasts, and they performed better as a result. Notably, the preference for modifiable algorithms held even when participants were severely restricted in the modifications they could make (Studies 1–3). In fact, our results suggest that participants’ preference for modifiable algorithms was indicative of a desire for some control over the forecasting outcome, and not for a desire for greater control over the forecasting outcome, as participants’ preference for modifiable algorithms was relatively insensitive to the magnitude of the modifications they were able to make (Study 2). Additionally, we found that giving participants the freedom to modify an imperfect algorithm made them feel more satisfied with the forecasting process, more likely to believe that the algorithm was superior, and more likely to choose to use an algorithm to make subsequent forecasts (Study 3). This research suggests that one can reduce algorithm aversion by giving people some control—even a slight amount—over an imperfect algorithm’s forecast. Data, as supplemental material, are available at https://doi.org/10.1287/mnsc.2016.2643 . This paper was accepted by Yuval Rottenstreich, judgment and decision making.

  PublisherOA PDFDOI

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

  SSRN Electronic Journal · 2015-01-01 · 15 citations

  articleOpen accessSenior author
  PublisherDOI

• 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 forecaster. This is because people more quickly lose confidence in algorithmic than human forecasters after seeing them make the same mistake. In 5 studies, participants either saw an algorithm make forecasts, a human make forecasts, both, or neither. They then decided whether to tie their incentives to the future predictions of the algorithm or the human. Participants who saw the algorithm perform were less confident in it, and less likely to choose it over an inferior human forecaster. This was true even among those who saw the algorithm outperform the human.

  PublisherDOI

• Algorithm Aversion: Seeing (Superior) Algorithms Perform Makes People Reject Them

  PsycEXTRA Dataset · 2014-01-01

  datasetSenior author
  PublisherDOI

• Understanding Algorithm Aversion: Forecasters Erroneously Avoid Algorithms After Seeing them Err

  Academy of Management Proceedings · 2014-01-01 · 27 citations

  articleSenior author

  When faced with the choice between a human judge and a (superior) statistical algorithm, forecasters often choose the human judge, but the basis for this choice is unknown. We hypothesized that forecasters are less likely to choose an algorithm after seeing it err. In three experiments, participants either saw an algorithm’s prediction performance, a human’s prediction performance, both, or neither. Then they decided whether to tether their incentives to the future predictions of the algorithm or the human. Participants who saw the algorithm’s performance were less likely to choose it, even for those who saw the algorithm outperform the human.

  PublisherDOI

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

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

  articleOpen accessSenior author
  PublisherDOI

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