Cade Massey is a Practice Professor in the Wharton School’s Operations, Information and Decisions Department. He received his PhD from the University of Chicago and taught at Duke University and Yale University before moving to Penn. Massey’s research focuses on judgment under uncertainty – how, and how well, people predict what will happen in the future. His work draws on experimental and “real world” data such as employee stock options, 401k savings, the National Football League draft, and graduate school admissions. His research has led to long-time collaborations with Google, Merck and multiple professional sports franchises. Massey’s research has been published in leading psychology and management journals, and covered by the New York Times, Wall Street Journal, Washington Post, The Economist, and National Public Radio. He has taught MBA and Executive MBA courses for 15 years, receiving teaching awards for courses on negotiation, influence, organizational behavior and human resources. He also co-teaches Wharton’s “People Analytics” MOOC on Coursera. Massey is faculty co-director of Wharton People Analytics, co-host of “Wharton Moneyball” on SiriusXM Business Radio, and co-creator of the Massey-Peabody NFL Power Rankings for the Wall Street Journal. He lives in Center City Philadelphia.
Berkeley J. Dietvorst, Joseph Simmons, Cade Massey (2016), Overcoming Algorithm Aversion: People Will Use Algorithms If They Can (Even Slightly) Modify Them, Management Science, forthcoming.
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.
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 five 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.
James J. Choi, Emily Haisley, Jennifer Kurkoski, Cade Massey (Under Review), Small Cues Change Savings Choices.
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 (Under Review), Learning to Detect Change.
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.
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.
R. Kaniel, Cade Massey, D. Robinson (Working), Optimism and Economic Crisis.
R. Kaniel, Cade Massey, D. Robinson (Working), The Importance of Being an Optimist: Evidence from Labor Markets.
Full-time MBA: Negotiation, Influence
Executive MBA: Influence
Executive Education: Strategic Decision-making Mindset (2/year), Executive Negotiation Workshop (3/year)
Coursera, “People Analytics”
Building, protecting and using influence is critical for achieving your goals. This requires good personal decision making as well as understanding others' decision-making, proficiency at the negotiation table as well as with the tacit negotiations before and after sitting at the table. In this course we focus on building your facility with a wide range of influence tools to help with these efforts. Topics include persuasion, coalitional bargaining, social cognition, networks, and status, as well as their applications to analytics, organizational decision-making and policy.
This course examines the art and science of negotiation, with additional emphasis on conflict resolution. Students will engage in a number of simulated negotiations ranging from simple one-issue transactions to multi-party joint ventures. Through these exercises and associated readings, students explore the basic theoretical models of bargaining and have an opportunity to test and improve their negotiation skills. Cross-listed with MGMT 691/OPIM 691. Format: Lecture, class discussion, simulation/role play, and video demonstrations. Materials: Textbook and course pack.
This course examines the art and science of negotiation. This course develops managerial skills by combining lectures with practice, using exercises where students negotiate with each other. Over the course of the semester, students will engage in a number of simulated negotiations ranging from simple one issue transactions to multi-party joint ventures. Through these exercises and associated readings, students explore the basic theoretical models of bargaining and have an opportunity to test and improve their negotiation skills. Cross-listed with LGST 806/OPIM 691.
Negotiation is the art and science of creating good agreements. In this course we will work on both, studying economics and psychology for the science, and practicing actual negotiations for the art. Throughout we think of negotiation in general terms, relevant not only to salary negotiations and home buying, but performance evaluations, speeches, group collaborations and interpersonal relationships. We practice these kinds of negotiations in 2-, 3-, 4-, and 6-person exercises. Potential reasons to skip this particular negotiation course: 1) We have a strong attendance policy, 2) We have strong no-computers/phones policies, 3) the course is very discussion oriented, 4) We survey your work colleagues about your influence tactics, and 5) you have a short assignment due almost every class. Beginning with the second week of class, if you miss one class you lose a letter grade. If you miss two classes you fail. We have this policy because it is an experiential class, and because your attendance directly affects classmates you are paired with. For some weeks you can attend another section if necessary. Cross-listed with MGMT691 and LGST806.
Many people are averse to using algorithms when making decisions, preferring to rely on their instincts. New Wharton research says a simple adjustment can help them feel differently.Knowledge @ Wharton - 2017/02/13