About

Daniel M. Bartels is an Associate Professor at the University of Chicago, specializing in judgment and decision making, consumer behavior, moral psychology, and concepts and categories. His research focuses on understanding how individuals make decisions, how they perceive themselves and others, and how these perceptions influence their behavior.

Research topics

• Computer Science
• Social psychology
• Economics
• Psychology
• Mathematics
• Data science
• Cognitive psychology
• Artificial Intelligence
• Statistics
• Political Science
• Machine Learning
• Business
• World Wide Web
• Applied psychology
• Law
• Knowledge management
• Marketing
• Econometrics
• Microeconomics
• Algorithm

Selected publications

• Between Confidence and Clarity: Objective and Subjective Expertise Differentially Predict Use of Alignable and Nonalignable Product Attributes

  Journal of Behavioral Decision Making · 2026-03-01 · 1 citations

  articleOpen accessSenior author

  ABSTRACT Expertise is multifaceted. This project builds on the difference between subjective expertise (the confidence one holds in understanding a particular topic) and objective expertise (one's actual understanding of that topic), which can vary independently. We examine how subjective and objective expertise differentially relate to making better choices and seeking additional choice‐relevant information. We hypothesized that some participants would be poorly calibrated in their ability to assess the value of more difficult‐to‐evaluate nonalignable attributes (in our setting, categorical attributes that are present or absent, such as torque‐vectoring) compared with easier‐to‐evaluate alignable attributes (e.g., miles per gallon). We measure and manipulate subjective and objective expertise and find that participants with greater objective expertise (either naturally or through provided information) tend to base their choices on the values of nonalignable attributes and that this pattern is generally not predicted by measures of subjective expertise. High subjective expertise, instead, predicts a reduced willingness to seek additional information or take advice, as well as a greater willingness to pay for customized items, independent of objective expertise. These studies point to important differences between these facets of expertise, underscoring the importance of incorporating measures of each when examining the role of expertise in decision‐making.

  PublisherDOI

• Consumers’ Mental Representation of Expenditures: Implications for Spending and Savings Decisions

  Journal of Consumer Research · 2025-05-21

  article

  Abstract People’s mental representation of expenditures is crucial to their budgeting. This article proposes that much like how they represent natural kinds (e.g., animals and plants), people represent expenditures in a hierarchical taxonomy. Seven studies, supported by six norming studies and three pilots, revealed that expenditures are represented hierarchically. We first recover people’s mental representations using a successive pile-sort method that asks people to form hierarchies of categories with common expenditures (e.g., rent, dining out, etc.). The pile-sort reveals consensus in people’s representations of expenditures and that these representations are relatively stable over time. Further, people’s adjustment in their spending behavior can be predicted by the distance between items in their representation. Specifically, when people overspent on an item, they spontaneously adjust spending more on taxonomically closer items. We examine this spontaneous adjustment behavior using both laboratory studies and field data with 6.5 million grocery shopping trips over 12 years. The findings highlight the connection between mental representation and consumer behavior, and they emphasize the importance of studying concepts and categories in the context of consumption.

  PublisherDOI

• A Primer for Evaluating Large Language Models in Social-Science Research

  Advances in Methods and Practices in Psychological Science · 2025-04-01 · 20 citations

  articleOpen access

  Autoregressive large language models (LLMs) exhibit remarkable conversational and reasoning abilities and exceptional flexibility across a wide range of tasks. Subsequently, LLMs are being increasingly used in scientific research to analyze data, generate synthetic data, or even write scientific articles. This trend necessitates that authors follow best practices for conducting and reporting LLM research and that journal reviewers can evaluate the quality of works that use LLMs. We provide authors of social-scientific research with essential recommendations to ensure replicable and robust results using LLMs. Our recommendations also highlight considerations for reviewers, focusing on methodological rigor, replicability, and validity of results when evaluating studies that use LLMs to automate data processing or simulate human data. We offer practical advice on assessing the appropriateness of LLM applications in submitted studies, emphasizing the need for transparency in methodological reporting and the challenges posed by the nondeterministic and continuously evolving nature of these models. By providing a framework for best practices and critical review, in this primer, we aim to ensure high-quality, innovative research in the evolving landscape of social-science studies using LLMs.

  PublisherDOI

• A timeline of cognitive costs in decision-making

  Trends in Cognitive Sciences · 2025-05-19 · 10 citations

  review
  PublisherDOI

• A Primer for Evaluating Large Language Models in Social Science Research

  2025-02-10 · 1 citations

  preprintOpen access

  Autoregressive Large Language Models (LLMs) exhibit remarkable conversational and reasoning abilities, and exceptional flexibility across a wide range of tasks. Subsequently, LLMs are being increasingly used in scientific research, to analyze data, generate synthetic data, or even to write scientific papers. This trend necessitates that authors follow best practices for conducting and reporting LLM research and that journal reviewers are able to evaluate the quality of works that use LLMs. We provide authors of social scientific research with essential recommendations to ensure replicable and robust results using LLMs. Our recommendations also highlight considerations for reviewers, focusing on methodological rigor, replicability, and validity of results when evaluating studies that use LLMs to automate data processing or simulate human data. We offer practical advice on assessing the appropriateness of LLM applications in submitted studies, emphasizing the need for transparency in methodological reporting and the challenges posed by the non-deterministic and continuously evolving nature of these models. By providing a framework for best practices and critical review, this primer aims to ensure high-quality, innovative research within the evolving landscape of social science studies using LLMs.

  PublisherDOI

• Can cognitive discovery be incentivized with money?

  Journal of Experimental Psychology General · 2025-01-21 · 1 citations

  article

  The ability to discover patterns or rules from our experiences is critical to science, engineering, and art. In this article, we examine how much people's discovery of patterns can be incentivized by financial rewards. In particular, we investigate a classic category learning task for which the effect of financial incentives is unknown (Shepard et al., 1961). Across five experiments, we find no effect of incentive on rule discovery performance. However, in a sixth experiment requiring category recognition but not learning, we find a large effect of incentives on response time and a small effect on task performance. Participants appear to apply more effort in valuable contexts, but the effort is disproportionate with the performance improvement. Taken together, the results suggest that performance in tasks that require novel inductive insights is relatively immune to financial incentives, while tasks that require rote perseverance of a fixed strategy are more malleable. (PsycInfo Database Record (c) 2025 APA, all rights reserved).

  PublisherDOI

• Author response for "A Primer for Evaluating Large Language Models in Social-Science Research"

  2025-02-03

  peer-review
  PublisherDOI

• Author response for "A Primer for Evaluating Large Language Models in Social-Science Research"

  2024-09-24

  peer-review
  PublisherDOI

• Author response for "A Primer for Evaluating Large Language Models in Social-Science Research"

  2024-10-01

  peer-review
  PublisherDOI

• More than Money Over Time: A Verbal Self-Report Scale for Measuring Intertemporal Preferences

  2024-06-18

  preprintOpen access

  IIntertemporal preferences have become an increasingly important construct for understanding individual behavior. Most measures of intertemporal preferences have focused on tradeoffs between smaller monetary amounts that are available sooner and larger later monetary amounts. But individuals make time-value tradeoffs in many other domains, too, like health and work. We developed a new approach, the Intertemporal Preferences in Choice Behavior (IPICB) scale, which uses verbal self-report items derived from crowdsourcing and previous research. The IPICB measures preferences in three domains: Health: maintaining a healthy lifestyle; Personal Finance: saving, investing and spending wisely; and Effort: a preference for getting things done rather sooner than later. We find domain-specific predictive validity for a large variety of behaviors and indices (e.g., Credit scores, intentions to get vaccinated, filing tax returns on time). The IPICB explains additional variance beyond standard elicited intertemporal preferences, even when self-control and other covariates (numeracy, social desirability and parents’ SES). The IPICB’s accessible verbal format allows widespread application for studying individuals’ intertemporal preferences.

  PublisherOA PDFDOI

Frequent coauthors

• Eric J. Johnson

  Columbia University

  40 shared

• Ye Li

  40 shared

• Antonia Krefeld-Schwalb

  Erasmus University Rotterdam

  38 shared

• Olivier Toubia

  Columbia University

  37 shared

• Daniel Wall

  California University of Pennsylvania

  37 shared

• Oleg Urminsky

  University of Chicago

  29 shared

• Nicholas Reinholtz

  University of Colorado Boulder

  13 shared

• Justin F. Landy

  Nova Southeastern University

  11 shared

Education

• Ph.D., Cognitive Psychology

  Northwestern University

  2007

• M.S., Cognitive Psychology

  Northwestern University

  2002

• B.S., Psychology

  University of Wisconsin-Green Bay

  2001