About

Matthew O. Jackson is the William D. Eberle Professor of Economics at Stanford University and an external faculty member of the Santa Fe Institute. He received his BA from Princeton University in 1984 and his PhD from Stanford in 1988. His research interests include game theory, microeconomic theory, and the study of social and economic networks, on which he has published numerous articles and authored the books 'The Human Network' and 'Social and Economic Networks'. Jackson has made significant contributions to understanding how social relations shape economic outcomes and has been recognized with several honors, including membership in the National Academy of Sciences, fellowships in the American Academy of Arts and Sciences, the Econometric Society, the Game Theory Society, and the Economic Theory Fellow. His accolades also include a Guggenheim Fellowship, the Social Choice and Welfare Prize, the von Neumann Award from Rajk Laszlo College, an honorary doctorate from Aix-Marseille University, and the B.E.Press Arrow Prize for Senior Economists. He has served as co-editor of prominent journals such as Games and Economic Behavior, the Review of Economic Design, and Econometrica.

Research topics

• Political Science
• Sociology
• Social psychology
• Demographic economics
• Demography
• Social Science
• Psychology
• Economics
• Economic growth

Selected publications

• Data and Code for: "Behavioral Communities and the Atomic Structure of Networks"

  ICPSR Data Holdings · 2026-01-01

  datasetOpen access1st authorCorresponding

  When people coordinate their behaviors with their friends—for example, choosing whether to adopt a new technology, protest against a government, or attend university—divisions within a social network can lead to contrasting norms of behavior in different parts of the network.We define a society’s atoms as groups of people who adopt the same behavior in every equilibrium. We show that the atoms are at least as coarse as blocks in stochastic block models and demonstrate that using knowledge of the atoms to seed the diffusion of a behavior significantly increases diffusion compared to seeding based on standard community detection algorithms.<br><br><br>

  PublisherDOI

• Data and Code for: "Behavioral Communities and the Atomic Structure of Networks"

  ICPSR Data Holdings · 2026-01-01

  datasetOpen access1st authorCorresponding

  When people coordinate their behaviors with their friends—for example, choosing whether to adopt a new technology, protest against a government, or attend university—divisions within a social network can lead to contrasting norms of behavior in different parts of the network.We define a society’s atoms as groups of people who adopt the same behavior in every equilibrium. We show that the atoms are at least as coarse as blocks in stochastic block models and demonstrate that using knowledge of the atoms to seed the diffusion of a behavior significantly increases diffusion compared to seeding based on standard community detection algorithms.<br><br><br>

  PublisherDOI

• Be.FM: Open Foundation Models for Human Behavior

  ArXiv.org · 2025-05-29

  preprintOpen access

  Despite their success in numerous fields, the potential of foundation models for modeling and understanding human behavior remains largely unexplored. We introduce Be.FM, one of the first open foundation models designed for human behavior modeling. Built upon open-source large language models and fine-tuned on a diverse range of behavioral data, Be.FM can be used to understand and predict human decision-making. We construct a comprehensive set of benchmark tasks for testing the capabilities of behavioral foundation models. Our results demonstrate that Be.FM can predict behaviors, infer characteristics of individuals and populations, generate insights about contexts, and apply behavioral science knowledge.

  PublisherOA PDFDOI

• A Network Formation Model Based on Subgraphs

  The Review of Economic Studies · 2025-02-25 · 20 citations

  articleSenior author

  Abstract We develop a new class of random graph models for the statistical estimation of network formation—subgraph generated models (SUGMs). Various subgraphs—e.g. links, triangles, cliques, stars—are generated and their union results in a network. We show that SUGMs are identified and establish the consistency and asymptotic distribution of parameter estimators in empirically relevant cases. We show that a simple four-parameter SUGM matches basic patterns in empirical networks more closely than four standard models (with many more dimensions): (1) stochastic block models; (2) models with node-level unobserved heterogeneity; (3) latent space models; and (4) exponential random graphs. We illustrate the framework’s value via several applications using networks from rural India. We study whether network structure helps enforce risk-sharing and whether cross-caste interactions are more likely to be private. We also develop a new central limit theorem for correlated random variables, which is required to prove our results and is of independent interest.

  PublisherDOI

• Experimenting with Networks

  ArXiv.org · 2025-06-12

  preprintOpen accessSenior author

  We provide an overview of methods for designing and implementing experiments (field, lab, hybrid, and natural) when there are networks of interactions between subjects.

  PublisherOA PDFDOI

• Experimenting with Networks

  SSRN Electronic Journal · 2025-01-01

  preprintOpen accessSenior author
  PublisherDOI

• Using Large Language Models to Categorize Strategic Situations and Decipher Motivations Behind Human Behaviors

  ArXiv.org · 2025-03-20

  preprintOpen accessSenior author

  By varying prompts to a large language model, we can elicit the full range of human behaviors in a variety of different scenarios in classic economic games. By analyzing which prompts elicit which behaviors, we can categorize and compare different strategic situations, which can also help provide insight into what different economic scenarios induce people to think about. We discuss how this provides a first step towards a non-standard method of inferring (deciphering) the motivations behind the human behaviors. We also show how this deciphering process can be used to categorize differences in the behavioral tendencies of different populations.

  PublisherOA PDFDOI

• Be.FM: Open Foundation Models for Human Behavior

  SSRN Electronic Journal · 2025-01-01

  preprintOpen access
  PublisherDOI

• Welfare optimization for resource allocation with peer effects

  PNAS Nexus · 2025-08-30

  articleOpen access

  Abstract Allocating students to schools or universities, people to teams or groups, people to urban housing, and matching users on social platforms are prominent examples of allocating limited goods, spaces, or positions to optimize social welfare. We study a welfare maximization problem that arises when such resource allocation scenarios involve peer effects, where people have preferences over the others who are nearby (e.g. their classmates, teammates, neighbors, or partners). We first develop a unified mathematical framework for this “position allocation problem,” which assigns people to positions in a given network, with people caring about both their positions and their neighbors’ attributes. We show that welfare maximization for the corresponding position allocation problem is computationally intractable, even when people have preferences that depend only on who is allocated to nearby positions, and those preferences satisfy simple constraints that arise naturally in urban and other real-world systems. In contrast to this computational lower bound, we show that if people can be classified into a fixed number of (demographic) groups and the network satisfies certain realistic spatial conditions, then efficiently computable allocations can be obtained for many natural scenarios. Importantly, the achieved social welfare is either optimal or arbitrarily close to optimal for natural forms of preferences. Our methods provide a foundation for position allocation with peer effects, and guide the design of optimal allocation strategies when people can be classified into a fixed number of groups in which members share similar preferences.

  PublisherOA PDFDOI

• Using Language Models to Decipher the Motivation Behind Human Behaviors

  SSRN Electronic Journal · 2025-01-01 · 2 citations

  preprintOpen accessSenior author
  PublisherDOI

Recent grants

• Collaborative Research: Multiplexing: Theories and Tests of Interactions Between Types of Relationships

  NSF · $278k · 2016–2020

• Collaborative Research: Social Networks and Microfinance

  NSF · $73k · 2012–2015

• Studies of Social Structure and Economic Behavior

  NSF · $303k · 2010–2014

• Theoretical and Empirical Investigations of the Dynamics of Homophily and its Impact on Students' Achievement, Decisions, and Well-Being

  NSF · $394k · 2020–2024

• Studies of Social Structure and Economic Behavior

  NSF · $177k · 2007–2011

Frequent coauthors

• Arun G. Chandrasekhar

  National Bureau of Economic Research

  83 shared

• Leeat Yariv

  Centre for Economic Policy Research

  50 shared

• Philippe Aghion

  Collège de France

  43 shared

• Abhijit Banerjee

  40 shared

• Daron Acemoğlu

  Massachusetts Institute of Technology

  39 shared

• Esther Duflo

  Massachusetts Institute of Technology

  37 shared

• Yves Zénou

  Monash University

  34 shared

• Benjamin Golub

  26 shared

Education

• B.A.

  Princeton University

  1984

• Ph.D.

  Stanford University

  1988

Awards & honors

• Fellow of the American Academy of Arts and Sciences
• Fellow of the Econometric Society
• Game Theory Society Fellow
• Economic Theory Fellow
• Guggenheim Fellowship