About

Carey Morewedge is a Professor of Marketing, Everett W. Lord Distinguished Faculty Scholar, and Chair of the Marketing Department at Boston University's Questrom School of Business. His research employs experiments to understand the psychology behind consumer decision making, focusing on how biased thinking influences perceptions of value related to experiences, money, and emerging technologies such as digital goods and artificial intelligence. Morewedge has received over $2.4 million in external research funding and has been recognized with numerous awards, including the Wegner Theoretical Innovation Prize from the Society for Personality and Social Psychology, a Best Paper award from the Journal of Consumer Research, and recognition as an MSI Scholar. His work has been featured in prominent academic journals such as Science, PNAS, Nature Medicine, and the Journal of Consumer Research, among others, and his popular writings have appeared in media outlets including The New York Times, TIME Magazine, Forbes, Fast Company, and Harvard Business Review. He has also been interviewed on radio and television programs including the BBC, NPR, and ABC World News Tonight. Morewedge earned his Ph.D. in Social Psychology from Harvard University and has held academic positions at Carnegie Mellon University and the Princeton School of Public and International Affairs, as well as a visiting fellowship at Harvard Kennedy School.

Research topics

• Computer Science
• Psychology
• Artificial Intelligence
• Marketing
• Business
• Data science
• Market economy
• Epistemology
• Commerce
• Industrial organization
• Social psychology
• Mathematics
• Law
• Economics
• Medicine

Selected publications

• Decisions with Algorithms

  2025-05-01 · 1 citations

  book-chapter1st authorCorresponding

  Decision making has changed in the age of information. While it was once an individual pursuit or an endeavor shared by multiple people, it is increasingly becoming a collaboration between humans and algorithms.

  PublisherDOI

• Large Language Models Improve Hypothesis Generation by Reducing Effort

  SSRN Electronic Journal · 2025-01-01

  preprintOpen accessSenior author
  PublisherDOI

• Debiasing training reduces confirmation bias in national risk analysts

  Scientific Reports · 2025-11-24

  articleOpen accessSenior author

  State risk forecasts are crucial for allocating resources to address international and domestic threats such as war, pandemics, and climate change. These risk forecasts largely rely on human judgment, which is often susceptible to cognitive biases. We conducted an experiment involving the majority of national risk analysts in a European country and a matched sample of masters students to compare their susceptibility to confirmation bias and bias blind spot in judgments related and unrelated to national risk. Additionally, we evaluated the effectiveness of a one-shot debiasing training intervention across both samples. We find that analysts exhibit less confirmation bias than students within and outside risk-related judgments. Crucially, a one-shot debiasing training session reduced confirmation bias in both analyst and student groups. These findings suggest that cost-effective debiasing interventions can improve expert judgment in national risk forecasting and provide evidence that experience and expertise reduce cognitive bias more broadly than previously recognized.

  PublisherOA PDFDOI

• GenAI and the psychology of work

  Trends in Cognitive Sciences · 2025-05-09 · 11 citations

  reviewSenior author
  PublisherDOI

• Evolution of Consumption

  2025-03-11

  book-chapterSenior author

  This chapter explores identity construction in the digital age. Traditional forms of expressing identity through private ownership of material goods and physical social networks are being replaced by the temporary consumption of digital experiential goods and participation in online social networks. It examines the impact of these evolving consumption patterns on the development and expression of self. The digitization of everyday life has created a new era where digital privacy, ethical consumption, and technological progress are central to modern identity formation. The digital economy has produced a sharing economy that challenges traditional identity markers and introduces new collaborative consumption dynamics. Concerns around digital inequality, algorithmic curation, and privacy shape how individual consumers and communities interact and perceive themselves. Some consumers seek a balance between physical and digital realms, using digital breaks and commitment devices to avoid over-reliance or digital addiction. Others embrace offline experiences to maintain authenticity and control, better understanding themselves in an increasingly virtual world. This chapter advocates for further research to explore the tension between digital and physical identity markers, the role of access-based consumption in identity development, and the ethical implications of algorithms and personalized digital content in shaping identity and autonomy.

  PublisherDOI

• Training to reduce cognitive bias may improve decision making after all

  2024-01-24

  articleSenior author
  PublisherDOI

• Algorithms help people see and correct their biases, study shows

  2024-05-10

  article1st authorCorresponding
  PublisherDOI

• A contest study to reduce attractiveness-based discrimination in social judgment.

  Journal of Personality and Social Psychology · 2024-11-14 · 5 citations

  articleOpen access

  > 20,000). Using a signal detection theory approach to evaluate interventions, we identified two interventions that reduced discrimination by lessening both decision noise and decision bias, while two other interventions reduced overall discrimination by only lessening noise or bias. The most effective interventions largely provided concrete strategies that directed participants' attention toward decision-relevant criteria and away from socially biasing information, though the fact that very similar interventions produced differing effects on discrimination suggests certain key characteristics that are needed for manipulations to reliably impact judgment. The effects of these four interventions on decision bias, noise, or both also replicated in a different discrimination domain, political affiliation, and generalized to populations with self-reported hiring experience. Results of the contest for decreasing attractiveness-based favoritism suggest that identifying effective routes for changing discriminatory behavior is a challenge and that greater investment is needed to develop impactful, flexible, and scalable strategies for reducing discrimination. (PsycInfo Database Record (c) 2025 APA, all rights reserved).

  PublisherOA PDFDOI

• People see more of their biases in algorithms

  Proceedings of the National Academy of Sciences · 2024-04-10 · 18 citations

  articleOpen accessSenior authorCorresponding

  Algorithmic bias occurs when algorithms incorporate biases in the human decisions on which they are trained. We find that people see more of their biases (e.g., age, gender, race) in the decisions of algorithms than in their own decisions. Research participants saw more bias in the decisions of algorithms trained on their decisions than in their own decisions, even when those decisions were the same and participants were incentivized to reveal their true beliefs. By contrast, participants saw as much bias in the decisions of algorithms trained on their decisions as in the decisions of other participants and algorithms trained on the decisions of other participants. Cognitive psychological processes and motivated reasoning help explain why people see more of their biases in algorithms. Research participants most susceptible to bias blind spot were most likely to see more bias in algorithms than self. Participants were also more likely to perceive algorithms than themselves to have been influenced by irrelevant biasing attributes (e.g., race) but not by relevant attributes (e.g., user reviews). Because participants saw more of their biases in algorithms than themselves, they were more likely to make debiasing corrections to decisions attributed to an algorithm than to themselves. Our findings show that bias is more readily perceived in algorithms than in self and suggest how to use algorithms to reveal and correct biased human decisions.

  PublisherOA PDFDOI

• Acceptance of Automated Vehicles Is Lower for Self than Others

  Journal of the Association for Consumer Research · 2024-02-07 · 8 citations

  articleSenior author

  Road traffic accidents are the leading cause of death worldwide for people aged 2–59. Nearly all deaths are due to human error. Automated vehicles could reduce mortality risks, traffic congestion, and air pollution of human-driven vehicles. However, their adoption depends on consumer acceptance, among other factors. In a nationally representative sample of Americans (N=580) and direct replication (N=193), we find consumers prefer lower levels of vehicle automation for themselves than for others. This difference is mediated by self-enhancing comparative evaluations. Relative to automated vehicles, consumers believe they are safer and more trustworthy drivers than other drivers. In a second experiment (N=803), enhanced assessments of self, not different assessments of automated vehicle capabilities, explained different preferences for self and others. Our findings show how biased self-evaluations reduce the acceptance of automated vehicles. This yields practical insights for policymakers and firms seeking to increase acceptance of automated vehicles.

  PublisherDOI

Frequent coauthors

• Haewon Yoon

  Indiana University

  28 shared

• Joachim Vosgerau

  Bocconi University

  25 shared

• Young Eun Huh

  24 shared

• Karim Kassam

  Homerton University Hospital

  21 shared

• Daniel T. Gilbert

  21 shared

• Yang Yang

  Sumy National Agrarian University

  20 shared

• Irene Scopelliti

  St George's, University of London

  18 shared

• Nicholas Epley

  University of Chicago

  14 shared

Education

• Social Psychology, Psychology

  Harvard University

  2006

Awards & honors

• Wegner Theoretical Innovation Prize from the Society for Per…
• Best Paper award from the Journal of Consumer Research
• recognition as a MSI Scholar
• Idea of the Year from The New York Times
• Poets and Quant's Top 40 under 40 Business School Professors