About

Matthew Backus is an associate professor in the Economic Analysis and Policy Group at Berkeley Haas. His focus is on industrial organization, a field of economics that studies market power in its myriad forms. His research has touched on auctions, bargaining, cartels, communication, and productivity. A recurrent theme in his work is the development of tools that allow us to empirically distinguish between theoretical models of behavior. Backus joined Berkeley from Columbia University, where he was an associate professor in the economics division of Columbia Business School. He is a faculty research fellow of the National Bureau of Economic Research and a research affiliate of the Center for Economic and Policy Research. He holds a PhD in Economics from the University of Michigan.

Research topics

• Computer Science
• Economics
• Finance
• Business
• Microeconomics
• Financial economics
• Accounting
• Mathematics
• Monetary economics
• Market economy
• Econometrics

Selected publications

• Thinking and Confidence (TAO)

  OSF Preprints (OSF Preprints) · 2026-02-17

  other
  Publisher

• Communication, Learning, and Bargaining Breakdown: An Empirical Analysis

  Management Science · 2025-08-18 · 2 citations

  article1st authorCorresponding

  Bargaining breakdown is common in bargaining in environments with incomplete information. We study whether, in these environments, permitting communication impacts bargaining outcomes. On May 23, 2016, eBay Germany’s Best Offer platform introduced unstructured communication allowing desktop users, but not mobile users, to accompany offers with a message. Using this natural experiment, our difference-in-differences approach documents a 14% decrease in the rate of breakdown among compliers. Though adoption is immediate, the effect is not. We show, using text analysis, that the dynamics are consistent with repeat players learning how to use communication in bargaining. Finally, tying the two results together, we show that messages that emulate the text content of experienced sellers are more likely to be accepted. This paper was accepted by Joshua Gans, business strategy. Funding: Part of this research was supported by the NSF [Grant SES-1629060]. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2023.00366 .

  PublisherDOI

• Assumptions, Disagreement, and Overprecision: Theory and Evidence

  2025-08-22

  articleOpen accessSenior author

  Constructing beliefs about the world often requires simplifying assumptions. However, it is often cognitively costly or even impossible to consider how all possible assumptions might affect beliefs. We develop a formal model of individuals who properly recognize uncertainty conditional on their assumptions (“within-model uncertainty”), but do not fully appreciate the uncertainty they assume away (“across-model uncertainty”). Our main results connect this tendency to use simplified models with overprecision (too-small variance estimates) and disagreement (interpersonal variance in mean predictions). If individuals independently choose an assumption in proportion to its probability of being true, across-model uncertainty, overprecision, and disagreement exactly coincide. We explore these predictions in an experimental setting where people are given a scatterplot and provide mean and ariance estimates for out-of-sample predictions. Consistent with the theory, we find that variance stimates are more responsive to changes in within-model uncertainty than across-model uncertainty, nd that overprecision and disagreement rise with across-model uncertainty. Finally, we analyze observational data from the Survey of Professional Forecasters, and find that forecasts are overprecise, and more overprecise in problems with more disagreement.

  PublisherOA PDFDOI

• Assumptions, Disagreement, and Overprecision: Theory and Evidence

  2025-08-26

  articleOpen accessSenior author

  Constructing beliefs about the world usually requires simplifying assumptions. We analyze the beliefs of agents who make reasonable assumptions to model a complex situation and who make predictions conditional on those assumptions. Our theory identifies tight connections between model uncertainty (the extent to which different models lead to different predictions), overprecision (too-small variance estimates), and interpersonal disagreement (variance in mean predictions). We test these predictions in an experiment in which participants view a scatterplot and report mean and variance estimates for out-of-sample predictions. Consistent with our theory, different people focus on different plausible models but provide reasonable estimates of uncertainty conditional on their model. As a result, model uncertainty increases both overprecision and disagreement. Outside of the lab, we find similar evidence in the Survey of Professional Forecasters, including that overprecision positively covaries with disagreement.

  PublisherOA PDFDOI

• Real and Assumed Information

  AEA Papers and Proceedings · 2025-05-01

  articleOpen access

  We consider a simplified version of the model in Augenblick et. al (2025), in which people use models to translate data into beliefs and predictions. We posit that they make correct inferences given a model but not unconditionally, forgetting model uncertainty, and demonstrate two implications: first, that new information can decrease confidence rather than increase it, if it prompts reconsideration of assumed models; second, that people with models may misunderstand what kind of information is persuasive to share with others.

  PublisherOA PDFDOI

• Real and Assumed Information

  2025-06-29

  preprintOpen access

  We introduce a formal model of how individuals form beliefs by simplifying complex real information through assumptions. We demonstrate that such assumed information, while often necessary, can lead to systematic excess certainty. By modeling belief formation under model uncertainty, we show that gaining new perspectives can increase subjective uncertainty, contrary to traditional models. The paper explores implications for persuasion and disagreement, emphasizing how differing unacknowledged assumptions obstruct belief convergence. A companion paper extends the theory and provides empirical support for the predicted patterns of overprecision and disagreement.

  PublisherOA PDFDOI

• Model uncertainty and overprecision (TAO-RRT)

  AEA Randomized Controlled Trials · 2024-10-31

  dataset
  PublisherDOI

• Dynamic Demand Estimation in Auction Markets

  The Review of Economic Studies · 2024-03-27 · 5 citations

  article1st authorCorresponding

  Abstract We study demand estimation in a large auction market. In our model, a dynamically evolving population of buyers with unit demand and heterogeneous and privately known preferences for a finite set of differentiated products compete in a sequence of auctions that occur in discrete time. We define an empirically tractable equilibrium concept in which bidders behave as though they are competing with the stationary distribution of opposing bids, characterize bidding strategies, and prove existence of equilibrium. Having developed this demand system, we prove that it is non-parametrically identified from panel data. We extend the model to consider a random coefficients demand system akin to workhorse demand models in industrial organization, and show that this too is non-parametrically identified. We apply the model to estimate demand and show how large sellers can exercise market power by using persistent reserve price policies, which induce higher bids and, therefore, revenues. Our analysis highlights the importance of both dynamic bidding strategies and panel data sample selection issues when analysing these markets.

  PublisherDOI

• Model uncertainty and overprecision (TAO-RRT)

  AEA Randomized Controlled Trials · 2024-10-31

  dataset
  PublisherDOI

• Bargaining in Online Markets

  2022-01-01 · 1 citations

  book-chapter1st authorCorresponding
  PublisherDOI

Frequent coauthors

• Steven Tadelis

  University of California, Berkeley

  105 shared

• Thomas Blake

  Amazon (United States)

  48 shared

• Dimitriy V. Masterov

  30 shared

• Tom Blake

  eBay (United States)

  28 shared

• Brad Larsen

  Stanford University

  26 shared

• Michael Sinkinson

  Yale University

  20 shared

• Christopher T. Conlon

  New York University

  20 shared

• Gregory Lewis

  University of Ontario Institute of Technology

  20 shared

Education

• Ph.D.

  UC Berkeley