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
• Statistics
• Microeconomics
• Engineering
• Social psychology

Selected publications

• Understanding People’s Preferences for Predictions: People Prioritize Being Right over Minimizing How Wrong They Are in Expectation

  Management Science · 2025-12-19

  article1st authorCorresponding

  This work explores the preferences that laypeople exhibit when making and evaluating predictions in the form of point estimates (e.g., the high temperature will be 66°). I propose that people typically have diminishing sensitivity to prediction error: the absolute difference between a prediction and a realized outcome. As a result, people often prioritize “being right,“ focusing on achieving near perfect predictions and placing less emphasis on the magnitude of errors when errors occur. Across 16 studies using varying methods and stimuli, participants exhibited multiple behaviors consistent with diminishing sensitivity to prediction error: (i) predicting the mode of distributions, (ii) restricting predictions to possible outcomes, (iii) reporting decreasing reactions to increasing marginal units of error, and (iv) preferring predictive models built with diminishing sensitivity to error. This behavior diverges from traditional methods of building predictive models and common interpretations of people’s predictions, which often prioritize avoiding large errors and assume that people are predicting the mean. Ultimately, this work not only highlights the discrepancies between our current practices and people’s preferences for predictions but also calls for a more thorough exploration of human objectives before we build models for them to use or make inferences about their beliefs in light of a decision they made. This paper was accepted by Jack Soll, behavioral economics and decision analysis. Funding: I thank the University of Chicago Booth School of Business for financial support. Supplemental Material: Preregistrations, materials, data, code, and supplements are available at https://doi.org/10.1287/mnsc.2024.07257 and at ResearchBox at https://researchbox.org/3130 .

  PublisherDOI

• Understanding when laypeople adopt predictive algorithms

  Nature Human Behaviour · 2025-03-17 · 2 citations

  article1st authorCorresponding
  PublisherDOI

• <p>Consumers Opt for More Attribute Upgrades When Selecting Among Preconfigured Products as Opposed to Configuring the Products Themselves</p>

  SSRN Electronic Journal · 2025-01-01

  preprintOpen accessSenior author
  PublisherDOI

• Understanding When Laypeople Adopt Predictive Algorithms

  SSRN Electronic Journal · 2025-01-01

  articleOpen access1st authorCorresponding
  PublisherDOI

• Understanding People's Preferences for Predictions: People Prioritize Being Right over Minimizing How Wrong They Are in Expectation

  SSRN Electronic Journal · 2024-01-01 · 2 citations

  preprintOpen access1st authorCorresponding
  PublisherDOI

• How Should Time Estimates Be Structured to Increase Customer Satisfaction?

  Management Science · 2024-12-20 · 5 citations

  articleSenior author

  Businesses across industries, such as food delivery apps and GPS navigation systems, routinely provide customers with time estimates in inherently uncertain contexts. How does the format of these time estimates affect customers’ satisfaction? In particular, should companies provide customers with a point estimate representing the best estimate, or should they communicate the inherent uncertainty in outcomes by providing a range estimate? In eight preregistered experiments (N = 5,323), participants observed time estimates provided by an app, and we manipulated whether the app presented the time estimates as a point estimate (e.g., “Your food will arrive in 45 minutes.”) or a range (e.g., “Your food will arrive in 40–50 minutes.”). After participants learned about the app’s prediction performance by sampling a set of past outcomes, we measured participants’ evaluation of the app. We find that participants judged the app more positively when it provided a range rather than a point estimate. These results held across different domains, different time durations, different underlying outcome distributions, and an incentive-compatible design. We also find that this preference is not simply due to people’s dislike of late outcomes, as participants also rated ranges more positively than conservative point estimates corresponding to the upper (i.e., later) bound of the range. These findings suggest that companies can increase customer satisfaction with realized time estimates by communicating the uncertainty inherent in these time estimates. This paper was accepted by Jack Soll, behavioral economics and decision analysis. Funding: This research was supported by the Bakar Faculty Fellowship at the Haas School of Business at UC Berkeley and the Beatrice Foods Co. Faculty Research Fund at the University of Chicago Booth School of Business. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2023.00137 .

  PublisherDOI

• Taking the Full Measure: Integrating Replication into Research Practice to Assess Generalizability

  Journal of Consumer Research · 2024-05-15 · 11 citations

  articleOpen accessSenior author

  Abstract In this article, we review the ways in which replication has been and could be featured in consumer behavior, using Journal of Consumer Research as a specific setting. We present a framework for thinking about the generalizability of research findings and differentiate various potential benefits that replication can have for understanding variability in consumer research findings. We then define four different types of replications, describe how researchers can use these approaches to produce distinct benefits, and give guidance regarding conducting, interpreting, and the potential contributions of these different types of replications. We conclude with a discussion of various ways in which replication could be more fully integrated into different phases of the scientific research process, taking into account the contribution necessary for publication. In particular, we identify opportunities to incorporate independent replication into original papers, to increase the replication-based contribution in papers that build on prior work, and to use systematic replication in conjunction with meta-analysis to synthesize and confirm conclusions from a mature research literature. More fully integrating replication into scientific practice can yield a new equilibrium, in which replication is routine, typically consistent with previous results, and recognized as necessary for establishing an empirical generalization.

  PublisherOA PDFDOI

• How Artificial Intelligence Constrains the Human Experience

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

  • Artificial Intelligence
  • Computer Science
  • Artificial Intelligence

  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 constrain the human experience, considering individual, interpersonal, and societal processes. Our analysis uncovers a complex interplay between the advantages of AI and its inadvertent negative repercussions, which potentially restrict human autonomy, self-identity, relational dynamics, and social behavior. In this article, we propose three different mechanisms at the core of these constraining forces: parametric reductionism, agency transference, and regulated expression. Our exploration of these mechanisms highlights the risks connected to system design and points to questions and implications for future researchers and policymakers.

  PublisherDOI

• Reason Defaults: Presenting Defaults With Reasons for Choosing Each Option Helps Decision Makers With Minority Interests

  SSRN Electronic Journal · 2023-01-01

  articleOpen accessSenior author
  PublisherDOI

• Reason Defaults: Presenting Defaults With Reasons for Choosing Each Option Helps Decision-Makers With Minority Interests

  Psychological Science · 2023-11-06 · 1 citations

  articleSenior author

  Defaults are powerful tools for nudging individuals toward potentially beneficial options. However, defaults typically guide all decision-makers toward the same option and, consequently, may misguide individuals with minority interests. We test whether presenting defaults with information about heterogeneity can help individuals with minority interests select alternative options, and we dub this intervention a “reason default.” Reason defaults preselect the option that is best for most individuals (like standard defaults) but also explain (a) why the default was selected and (b) who should opt for an alternative. In five preregistered studies using online convenience samples of adults ( N = 4,210), we find that reason defaults can improve decision-makers’ outcomes over standard defaults and forced choices by guiding most individuals toward the default option while helping individuals with minority interests select an alternative. Further, participants reported that reason defaults enhance transparency, decision ease, and understanding of the choice relative to standard defaults and forced choices.

  PublisherDOI

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