Berkeley J. Dietvorst

About

Berkeley J. Dietvorst is an Associate Professor of Marketing at The University of Chicago Booth School of Business. His research focuses on consumer and managerial decision making, with a particular emphasis on understanding how people use information to make judgments and decisions in risky or uncertain domains. A central theme of his work is the psychology of prediction, especially how consumers and managers utilize predictive algorithms to make forecasts and choices. Beyond this, his research explores various aspects of judgment and decision making under risk or uncertainty, including consumers' attitudes toward corporate experiments, researchers' use of replication to assess generalizability, consumers' risk preferences, choice architecture, people's ability to disregard discredited information, and the consequences of performance expectations for persistence.

Research topics

• Artificial Intelligence
• Computer Science
• Machine Learning
• Psychology
• Political Science
• Mathematics
• Algorithm
• Economics
• Law
• Business

Selected publications

• People Reject Algorithms in Uncertain Decision Domains Because They Have Diminishing Sensitivity to Forecasting Error

  Psychological Science · 2020 · 263 citations

  1st authorCorresponding

  = 4,820), we found that (a) people have diminishing sensitivity to each marginal unit of error that a forecast produces, (b) people are less likely to use the best possible algorithm in decision domains that are more unpredictable, (c) people choose between decision-making methods on the basis of the perceived likelihood of those methods producing a near-perfect answer, and (d) people prefer methods that exhibit higher variance in performance (all else being equal). To the extent that investing,…

  PublisherDOI

• Consumers Object to Algorithms Making Morally Relevant Tradeoffs Because of Algorithms’ Consequentialist Decision Strategies

  Journal of Consumer Psychology · 2021 · 91 citations

  1st authorCorresponding

  Why do consumers embrace some algorithms and find others objectionable? The moral relevance of the domain in which an algorithm operates plays a role. The authors find that consumers believe that algorithms are more likely to use maximization (i.e., attempting to maximize some measured outcome) as a decision‐making strategy than human decision makers (Study 1). Consumers find this consequentialist decision strategy to be objectionable in morally relevant tradeoffs and disapprove of algorithms ma…

  PublisherDOI

• How Artificial Intelligence Constrains the Human Experience

  Journal of the Association for Consumer Research · 2024 · 57 citations

  Artificial intelligence (AI) and related technologies are transforming many consumption activities, powering breakthroughs that expand the human experience by enhancing human capabilities, performance, and creativity. While this explains the consumer enthusiasm and rapid adoption of these technologies, AI systems can also have the opposite effect: reducing and constraining the range of experiences that are available to consumers. This article examines the mechanisms through which AI can constrai…

  PublisherDOI

• Intentionally “biased”: People purposely use to-be-ignored information, but can be persuaded not to.

  Journal of Experimental Psychology General · 2018-12-27 · 23 citations

  article1st authorCorresponding

  Abundant research has shown that people fail to disregard to-be-ignored information (e.g., hindsight bias, curse of knowledge), which has contributed to the popular notion that people are unwillingly and unconsciously affected by information. Here we provide evidence that, instead, people simply do not want to ignore such information. The findings: In Studies 1 and 2, the majority of participants explicitly indicated a desire to use to-be-ignored information in classic paradigms. In Study 3, the…

  PublisherDOI

• The minimum mean paradox: A mechanical explanation for apparent experiment aversion

  Proceedings of the National Academy of Sciences · 2019-11-06 · 19 citations

  letterOpen access

  Proceedings of the National Academy of Sciences (PNAS), a peer reviewed journal of the National Academy of Sciences (NAS) - an authoritative source of high-impact, original research that broadly spans the biological, physical, and social sciences.

  PublisherOA PDFDOI

Frequent coauthors

• Cade Massey

  University of Pennsylvania

  7 shared

• Joseph P. Simmons

  6 shared

• Uri Simonsohn

  University of Pennsylvania

  6 shared

• Robert Mislavsky

  4 shared

• Bradford Tuckfield

  Texas Medical Board

  3 shared

• Katherine L. Milkman

  California University of Pennsylvania

  3 shared

• Hengchen Dai

  Anderson University - South Carolina

  3 shared

• Maurice E. Schweitzer

  3 shared

Education

• Ph.D., Marketing

  University of Chicago

  2010

• M.S., Marketing

  University of Chicago

  2007

• B.A., Psychology

  University of California, Berkeley

  2004

Awards & honors

• Distinguished Alumni Award Honorees

Similar researchers at University of Chicago

• Pradeep K. Chintaguntah-75
• Sanjay K. Dharh-47
• Daniel Bartelsh-30