About

Benjamin Golub is a professor in the Department of Economics at Northwestern University. He earned his PhD from Stanford Graduate School of Business in 2012. His research focuses on social and economic networks, with particular emphasis on models of social learning, local public goods, peer effects, and the formation of social capital. A key aspect of his work involves capturing features of networks through theory-based summary statistics that are useful in empirical studies and policy analyses.

Research topics

• Computer Science
• Computer Security
• Political Science
• Artificial Intelligence
• Economics
• Psychology
• Microeconomics

Selected publications

• Incentive Design With Spillovers

  2025-07-02 · 1 citations

  articleOpen access

  Performance incentives tied to joint outcomes — such as equity for startup executives or bonuses for marketing teams — are a common tool for motivating teams. How should such incentive schemes be designed and how should they take into account the team's production function? We examine these questions in a simple non-parametric model of a team working on a joint project. Each member of the team chooses a costly effort level. These actions jointly determine a real-valued team performance according to a sufficiently smooth, increasing function of the efforts, which may entail interactions such as complementarities among agents' efforts. Any performance level determines a probability distribution over observable project outcomes.

  PublisherOA PDFDOI

• Robust Market Interventions

  2025-07-02

  articleOpen access

  We study when interventions can robustly increase market surplus despite imprecise information about economic primitives, in a setting with many strategic firms possessing market power. The key sufficient condition, recoverable structure, requires large-scale product complementarities. The analysis works by decomposing the incidence of interventions in terms of principal components of a Slutsky matrix. Under recoverable structure, a noisy signal of this matrix reveals enough about these principal components to design robust interventions. Our results demonstrate the utility of spectral methods for analyzing imperfectly observed strategic interactions with many agents.

  PublisherOA PDFDOI

• Eigenvalues in microeconomics

  ArXiv.org · 2025-02-17

  preprintOpen access1st authorCorresponding

  Square matrices often arise in microeconomics, particularly in network models addressing applications from opinion dynamics to platform regulation. Spectral theory provides powerful tools for analyzing their properties. We present an accessible overview of several fundamental applications of spectral methods in microeconomics, focusing especially on the Perron-Frobenius Theorem's role and its connection to centrality measures. Applications include social learning, network games, public goods provision, and market intervention under uncertainty. The exposition assumes minimal social science background, using spectral theory as a unifying mathematical thread to introduce interested readers to some exciting current topics in microeconomic theory.

  PublisherOA PDFDOI

• Games on Endogenous Networks

  2025-07-02 · 17 citations

  preprintOpen accessSenior author

  We study network games in which players choose partners and an effort level. There is a finite set of players N, each choosing an action si; from an ordered set Si and forming links G, with payoffs ui(G, s). We study two stability concepts: an outcome (G, s) is strictly pairwise stable if (i) actions s are a Nash equilibrium for fixed G; (ii) no player wishes to unilaterally sever an existing link; and (iii) no pair of unlinked players both weakly desire to form a link. The network is strictly pairwise Nash stable if no player can strictly benefit from simultaneously changing their action and severing some links.

  PublisherOA PDFDOI

• Managing Strategic Complexity

  2024-07-08

  article

  Standard game-theoretic analysis yields highly incomplete descriptions of behavior in complex games of complete information. A key part of the reason is that standard models do not account for the role of complexity in shaping players' strategic behavior. We investigate the implications of complexity empirically and theoretically, focusing on the game of chess---a good setting for our study because it is a rare example of an extensive-form game that is played by experienced, motivated players, and for which we have vast amounts of data.

  PublisherDOI

• Robust Market Interventions

  arXiv (Cornell University) · 2024-11-05

  preprintOpen access

  When can interventions in markets be designed to increase surplus robustly—i.e., with high probability—accounting for uncertainty due to imprecise information about economic primitives? In a setting with many strategic firms, each possessing some market power, we present conditions for such interventions to exist. The key condition, significant structure, requires large-scale complementarities among families of products. The analysis works by decomposing the incidence of interventions in terms of principal components of a Slutsky matrix. Under significant structure, a noisy signal of this matrix reveals enough about these principal components to design robust interventions. Our results demonstrate the usefulness of spectral methods for analyzing imperfectly observed strategic interactions with many agents.

  PublisherOA PDFDOI

• Incentive Design with Spillovers

  arXiv (Cornell University) · 2024-11-12

  preprintOpen access

  A principal uses payments conditioned on stochastic outcomes of a team project to elicit costly effort from the team members. We develop a multi-agent generalization of a classic first-order approach to contract optimization by leveraging methods from network games. The main results characterize the optimal allocation of incentive pay across agents and outcomes. Incentive optimality requires equalizing, across agents, a product of (i) individual productivity (ii) organizational centrality and (iii) responsiveness to monetary incentives. We specialize the model to explore several applied questions, including whether compensation should reward individual ability or collaborativeness and how the strength of complementarities shapes pay dispersion.

  PublisherOA PDFDOI

• Multiplexing in Networks and Diffusion

  SSRN Electronic Journal · 2024-01-01

  preprintOpen access
  PublisherDOI

• Incentive Design With Spillovers

  SSRN Electronic Journal · 2024-01-01

  preprintOpen access
  PublisherDOI

• Multiplexing in Networks and Diffusion

  arXiv (Cornell University) · 2024-12-16

  preprintOpen access

  Social and economic networks are often multiplexed, meaning that people are connected by different types of relationships -- such as borrowing goods and giving advice. We make two contributions to the study of multiplexing and the understanding of simple versus complex contagion. On the theoretical side, we introduce a model and theoretical results about diffusion in multiplex networks. We show that multiplexing impedes the spread of simple contagions, such as diseases or basic information that only require one interaction to transmit an infection. We show, however that multiplexing enhances the spread of a complex contagion when infection rates are low, but then impedes complex contagion if infection rates become high. On the empirical side, we document empirical multiplexing patterns in Indian village data. We show that relationships such as socializing, advising, helping, and lending are correlated but distinct, while commonly used proxies for networks based on ethnicity and geography are nearly uncorrelated with actual relationships. We also show that these layers and their overlap affect information diffusion in a field experiment. The advice network is the best predictor of diffusion, but combining layers improves predictions further. Villages with greater overlap between layers -- more multiplexing -- experience less overall diffusion. Finally, we identify differences in multiplexing by gender and connectedness. These have implications for inequality in diffusion-mediated outcomes such as access to information and adherence to norms.

  PublisherOA PDFDOI

Frequent coauthors

• Matthew O. Jackson

  26 shared

• Andrea Galeotti

  26 shared

• Sanjeev Goyal

  24 shared

• Matthew Elliott

  University of Cambridge

  18 shared

• Krishna Dasaratha

  Boston University

  12 shared

• Arun G. Chandrasekhar

  National Bureau of Economic Research

  11 shared

• Matt V. Leduc

  Groupement de Recherche en Économie Quantitative d’Aix-Marseille

  9 shared

• Nir Hak

  Uber AI (United States)

  8 shared

Education

• Ph.D., Economics

  University of California, Berkeley

  1995

• M.A., Economics

  University of California, Berkeley

  1991

• B.A., Economics

  University of California, Berkeley

  1988