Cade Massey

Edward Chang, Katherine L. Milkman, Dena Gromet, Reb Rebele, Cade Massey, Angela Duckworth, Adam Grant (2019), The Mixed Effects of Online Diversity Training, Proceedings of the National Academy of Sciences, 116 (15), pp. 7778-7783.

Abstract: We present results from a large (n = 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.

Cade Massey (2019), How You Can Have More Impact as a People Analyst, MIT Sloan Management Review, Spring 2019 ().

Berkeley J. Dietvorst, Joseph Simmons, Cade Massey (2018), Overcoming Algorithm Aversion: People Will Use Algorithms If They Can (Even Slightly) Modify Them, Management Science, 64 (), pp. 1155-1170.

Abstract: 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.

James J. Choi, Emily Haisley, Jennifer Kurkoski, Cade Massey (2017), Small Cues Change Savings Choices, Journal of Economic Behavior & Organization, 142 (), pp. 378-395.

Berkeley Dietvorst, Joseph Simmons, Cade Massey (2015), Algorithm Aversion: People Erroneously Avoid Algorithms After Seeing Them Err, Journal of Experimental Psychology: General, 144 (February) (), pp. 114-126.

Abstract: 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.  

Cade Massey (2013), Rev. of Schelling’s Game Theory: How to Make Decisions by Robert V. Dodge, Journal of Economic Literature.

Cade Massey and Richard H Thaler (2013), The Loser’s Curse: Decision Making and Market Efficiency in the National Football League Draft, Management Science.

Y. Li, Cade Massey, G. Wu (Working), Learning to Detect Change.

Joseph Simmons and Cade Massey (2012), Is Optimism Real?, Journal of Experimental Psychology: General, 141 (November) (), pp. 630-634.

Abstract: Is optimism real, or are optimistic forecasts just cheap talk? To help answer this question, we investigated whether optimistic predictions persist in the face of large incentives to be accurate. We asked National Football League football fans to predict the winner of a single game. Roughly half (the partisans) predicted a game involving their favorite team and the other half (the neutrals) predicted a game involving two teams they were neutral about. Participants were promised either a small incentive ($5) or a large incentive ($50) for correctly predicting the game’s winner. Optimism emerged even when incentives were large, as partisans were much more likely than neutrals to predict partisans’ favorite teams to win. Strong optimism also emerged among participants whose responses to follow-up questions strongly suggested that they believed the predictions they made. This research supports the claim that optimism is real.

Cade Massey, Joseph Simmons, David A. Armor (2011), Hope Over Experience: Desirability and the Persistence of Optimism, Psychological Science, 22 (February), 274-281.

Abstract: Many important decisions hinge on expectations of future outcomes. Decisions about health, investments, and relationships all depend on predictions of the future. These expectations are often optimistic: People frequently believe that their preferred outcomes are more likely than is merited. Yet it is unclear whether optimism persists with experience and, surprisingly, whether optimism is truly caused by desire. These are important questions because life’s most consequential decisions often feature both strong preferences and the opportunity to learn. We investigated these questions by collecting football predictions from National Football League fans during each week of the 2008 season. Despite accuracy incentives and extensive feedback, predictions about preferred teams remained optimistically biased through the entire season. Optimism was as strong after 4 months as it was after 4 weeks. We exploited variation in preferences and matchups to show that desirability fueled this optimistic bias.