About

Joseph Simmons is the Dorothy Silberberg Professor of Applied Statistics and a Professor of Operations, Information, and Decisions at the University of Pennsylvania's Wharton School. His research primarily investigates the psychology of judgment and decision-making, focusing on understanding and addressing errors and biases that affect people's judgments, predictions, and choices. His work explores topics such as advice taking, algorithm aversion, anchoring effects, consumer rating systems, forecasting biases, the illusion of control, intuitive biases, optimistic biases, outcome bias, and the wisdom of crowds. In addition to his psychological research, Simmons is renowned for his contributions to research methods. Alongside colleagues Leif Nelson and Uri Simonsohn, he has dedicated over 16 years to identifying and remedying research practices that contribute to false-positive findings and a perverse incentive structure in scientific publishing. His efforts include exposing p-hacking, developing tools like the p-curve for assessing evidential value, creating pre-registration platforms used by thousands of researchers, and establishing websites for posting research materials and data. Simmons is also a co-founder of the Wharton Credibility Lab, which provides online platforms to enhance research credibility. His work aims to improve the reliability and integrity of scientific research through systematic replication, transparency, and methodological innovation.

Research topics

• Computer Science
• Psychology
• Mathematics
• Computer Security
• Information Retrieval
• Political Science
• Statistics
• Artificial Intelligence
• Medicine
• Data science
• Engineering
• Management science
• Economics
• Pathology
• Mathematical economics
• Social psychology

Selected publications

• ResearchBox 6202, 'Figuring Out Figure 1', https://researchbox.org/6202

  Zenodo (CERN European Organization for Nuclear Research) · 2026-03-19

  otherOpen access1st authorCorresponding

  Box title: 'Figuring Out Figure 1' Reference: Joe Simmons, 'Figuring Out Figure 1', Data Coladahttps://datacolada.org/134Note: this backup was created automatically by a ResearchBox bot

  PublisherDOI

• ResearchBox 6202, 'Figuring Out Figure 1', https://researchbox.org/6202

  Zenodo (CERN European Organization for Nuclear Research) · 2026-03-19

  otherOpen access1st authorCorresponding

  Box title: 'Figuring Out Figure 1' Reference: Joe Simmons, 'Figuring Out Figure 1', Data Coladahttps://datacolada.org/134Note: this backup was created automatically by a ResearchBox bot

  PublisherDOI

• Why (and When) Are Uncertain Price Promotions More Effective Than Equivalent Sure Discounts?

  Journal of Consumer Research · 2025-06-18 · 4 citations

  articleOpen accessSenior author

  Abstract Past research suggests that offering customers a probabilistic promotion, such as an X% chance to get a product for free, is often more effective than providing a sure discount of equal expected value. In five studies (N = 8,969), we find that probabilistic price promotions are more effective than equivalent sure discounts only when those sure discounts are or seem trivial. Specifically, we find that probabilistic promotions are relatively more effective (1) when the sure discounts are actually smaller, (2) when the sure discounts are made to feel smaller by presenting them alongside a larger discount, and (3) when the sure discounts are made to feel smaller by framing them as a percentage discount rather than a dollar amount. These findings are inconsistent with two leading explanations of consumers’ preferences for probabilistic promotions—diminishing sensitivity and the overweighting of small probabilities—and suggest that people’s preferences for uncertainty are more strongly tethered to their perceptions of the size of the sure outcome than they are to their perceptions of the probability of getting the uncertain reward.

  PublisherOA PDFDOI

• Why (and When) Are Uncertain Price Promotions More Effective Than Equivalent Sure Discounts?

  SSRN Electronic Journal · 2025-01-01

  articleOpen accessSenior author
  PublisherDOI

• Different Methods Elicit Different Belief Distributions

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

  articleOpen accessSenior author
  PublisherDOI

• Looking our limitations in the eye: A call for more thorough and honest reporting of study limitations

  Social and Personality Psychology Compass · 2024-07-01 · 12 citations

  articleOpen access

  Abstract The replication crisis and subsequent credibility revolution in psychology have highlighted many suboptimal research practices such as p ‐hacking, overgeneralizing, and a lack of transparency. These practices may have been employed reflexively but upon reflection, they are hard to defend. We suggest that current practices for reporting and discussing study limitations are another example of an area where there is much room for improvement. In this article, we call for more rigorous reporting of study limitations in social and personality psychology articles, and we offer advice for how to do this. We recommend that authors consider what the best argument is against their conclusions (which we call the “steel‐person principle”). We consider limitations as threats to construct, internal, external, and statistical conclusion validity (Shadish et al., 2002), and offer some examples for better practice reporting of common study limitations. Our advice has its own limitations — both our representation of current practices and our recommendations are largely based on our own metaresearch and opinions. Nevertheless, we hope that we can prompt researchers to write more deeply and clearly about the limitations of their research, and to hold each other to higher standards when reviewing each other's work.

  PublisherOA PDFDOI

• Thoughts of God and acceptance of artificial intelligence: A replication

  2024-03-11 · 2 citations

  preprintOpen accessSenior author

  We report our attempts to replicate results by Karataş and Cutright (2023), in which thoughts of God increased people’s receptivity to advice from artificially intelligent advisors. We attempt faithful replications of the five online studies from the original paper all with larger sample sizes than the originals. We fail to find evidence consistent with the claims of Karataş and Cutright. Our results suggest that if the original effect exists, it is too small to have been detected by the original studies.

  PublisherOA PDFDOI

• How Does Rating Specific Features of an Experience Alter Consumers' Overall Evaluations of That Experience?

  Journal of Consumer Research · 2024-01-23 · 6 citations

  articleOpen accessSenior author

  Abstract How does the way companies elicit ratings from consumers affect the ratings that they receive? In 10 pre-registered experiments, we find that consumers rate subpar experiences more positively overall when they are also asked to rate specific aspects of those experiences (e.g., a restaurant's food, service, and ambiance). Studies 1–4 established the basic effect across different scenarios and experiences. Study 5 found that the effect is limited to being asked to rate specific features of an experience, rather than providing open-ended comments about those features. Studies 6–9 provided evidence that the effect does not emerge because rating positive aspects of a subpar experience reminds consumers that their experiences had some good features. Rather, it emerges because consumers want to avoid incorporating negative information into both the overall and the attribute ratings. Lastly, study 10 found that asking consumers to rate attributes of a subpar experience reduces the predictive validity of their overall rating. We discuss implications of this work and reconcile it with conflicting findings in the literature.

  PublisherDOI

• Different methods elicit different belief distributions.

  Journal of Experimental Psychology General · 2024-09-26 · 3 citations

  articleSenior author

  = 14,553), we find that Distribution Builder elicits more accurate belief distributions than Sliders, except when true distributions are right-skewed, for which the results are mixed. This result holds when we assess accuracy (a) relative to a normative benchmark and (b) relative to participants' own beliefs. Our evidence suggests that participants approach these two methods differently: Sliders users are more likely to start with the lowest bins in the interface, which in turn leads them to put excessive mass in those bins. Our research sheds light on the process by which people construct belief distributions while offering a practical recommendation for future research: All else equal, Distribution Builder yields more accurate belief distributions. (PsycInfo Database Record (c) 2025 APA, all rights reserved).

  PublisherDOI

• Does thinking about God increase acceptance of artificial intelligence in decision-making?

  Proceedings of the National Academy of Sciences · 2024-07-23 · 7 citations

  letterOpen accessSenior author

  We report our attempts to replicate results by Karata and Cutright (2023), in which thoughts of God increased people's receptivity to advice from artificially intelligent advisors.We attempt faithful replications of the five online studies from the paper all with larger sample sizes than the originals.We fail to find evidence consistent with the claims of Karata and Cutright.Our results suggest that if the original effect exists, it is too small to have been detected by the original studies.

  PublisherOA PDFDOI

Frequent coauthors

• Leif D. Nelson

  83 shared

• Uri Simonsohn

  University of Pennsylvania

  34 shared

• Jeff Galak

  Carnegie Mellon University

  34 shared

• Robyn A. LeBoeuf

  Washington University in St. Louis

  34 shared

• Cade Massey

  University of Pennsylvania

  11 shared

• Celia Gaertig

  University of California, Berkeley

  10 shared

• Hannah Perfecto

  Washington University in St. Louis

  9 shared

• Berkeley J. Dietvorst

  University of Chicago

  6 shared

Awards & honors

• Wharton Credibility Lab (co-founder)