Benjamin Golub

Assistant Professor
Department of Economics

Harvard University

[Google Scholar profile]
[CV]   [Bio]
ben.golub [at]
Due to some problems with my web hosting at Harvard, the latest/most reliable version of this page is at


Learning in Social Networks (with Evan Sadler
A broad overview of two kinds of network learning models: (i) sequential ones in the tradition of information cascades and herding, and (ii) iterated linear updating models (DeGroot), along with their variations, foundations, and critiques. Ideal for a graduate course. [  More]

Current Teaching

A Mini-course on Learning and Influence in Networks
Northwestern University, October 15, 16, and 18, 3:30-5:00pm, Global Hub 2130
An abstract for the mini-course can be found here.


Social Learning in a Dynamic Environment (with Krishna Dasaratha and Nir Hak)
Agents learn from one another in a network about the value of a changing state. Can they aggregate information fast enough to keep up with the changes? We offer a tractable model of learning suited to addressing that question. In this model, a simple linear (DeGroot) rule of aggregating neighbors' estimates is part of a Bayesian equilibrium. [  More]

Targeting Interventions in Networks (with Andrea Galeotti and Sanjeev Goyal
Revise and resubmit, Econometrica 
If a planner has limited resources to shape incentives, whom should she target, e.g., to maximize welfare? A principal component analysis, new to network games, identifies the planner's priorities across various network intervention problems.  [ More]

Signaling, Shame, and Silence in Social Learning (with Arun Chandrasekhar and He Yang) 
Does the fear of appearing ignorant deter people from asking questions, and is that an important friction in information-gathering? In an experiment, we show that people seek information less when needing it is related to ability. [ More]

When Less is More: Experimental Evidence on Information Delivery During India's Demonetization (with Abhijit Banerjee, Emily Breza, and Arun Chandrasekhar
Suppose people are worried about how asking questions makes them look. Then giving information to fewer people can make for greater diffusion and better learning, in theory and in practice. [ More]

Expectations, Networks, and Conventions (with Stephen Morris
We study certain games in which there is both incomplete information and a network structure. The two turn out to be, in a sense, the same thing: A unified analysis nests classical incomplete-information results (e.g., on common priors) and network results (e.g. relating equilibria to network centralities). [ More] [Slides]

Higher-Order Expectations (with Stephen Morris
Motivated by their role in games, we study limits of iterated expectations with heterogeneous priors: how priors matter, how the order in which expectations are taken matters, and when the two enter "separably". [ More]

Illiquidity Spirals in Coupled Over-the-Counter Markets (with Christoph Aymanns and Co-Pierre Georg
Traders are involved in two different networks simultaneously; each wants to be active only if it has enough active neighbors in both networks. The equilibrium outcomes are much more fragile to shocks in such a coupled-network game than a one-network game. The leading application is to the collapse of liquidity provision in secured lending. [ More]

A Network Approach to Public Goods (with Matthew Elliott
Forthcoming, Journal of Political Economy 
Perron eigenvalues are a natural way to measure whether an economic system is at an efficient point, and eigenvector centrality relates naturally to efficient negotiated outcomes. We demonstrate these connections in a simple model of investment with externalities, without parametric assumptions. [ More] [Slides] [4-page version]

Ranking Agendas for Negotiations (with Matthew Elliott
Countries are hashing out the agenda for a summit in which each will make costly concessions to help the others. Should the summit focus on pollution, trade tariffs, or disarmament? This is a theory to help them decide based on marginal costs and benefits, without transferable utility. [ More]

Financial Networks and Contagion (with Matthew Elliott and Matthew O. Jackson
American Economic Review, 104(10), October 2014
Diversification (more counterparties) and integration (deeper relationships with each counterparty) have different, non-monotonic effects on financial contagions. [ More] [Slides]

How Homophily Affects the Speed of Learning and Best-Response Dynamics (with Matthew O. Jackson
Quarterly Journal of Economics, 127(3), August 2012.
Group-level segregation patterns in networks seriously slow convergence to consensus behavior when agents' choices are based on an average of neighbors' choices. When the process is a simple contagion, homophily doesn't matter.
[ More] [Download] [Slides]

How Sharing Information Can Garble Experts' Advice (with Matthew Elliott and Andrei Kirilenko)
American Economic Review: Papers & Proceedings, 104(5): 463–468, 2014
Do we get better advice as our experts get more information? Two experts, who like to be right, make predictions about whether an event will occur based on private signals about its likelihood.  It is possible for both experts' information to improve unambiguously while the usefulness of their advice to any third party unambiguously decreases. [ More] [Long Version]

Strategic Random Networks and Tipping Points in Network Formation (with Yair Livne) 
If agents form networks in an environment of uncertainty, then arbitrarily small changes in economic parameters (such as costs and benefits of linking) can discontinuously change the properties of the equilibrium networks, especially efficiency. [ More]

Naive Learning in Social Networks and the Wisdom of Crowds (with Matthew O. Jackson)
American Economic Journal: Microeconomics, 2(1):112-149, February 2010.
In what networks do agents who learn very naively get the right answer? 
[ More] [3-page version] [Slides]

Using Selection Bias to Explain the Observed Structure of Internet Diffusions (with Matthew O. Jackson
Proceedings of the National Academy of Sciences, 107(24):10833-10836, June 15, 2010.
David Liben-Nowell and Jon Kleinberg have observed that the reconstructed family trees of chain letter petitions are strangely tall and narrow. We show that this can be explained with selection and observation biases within a simple model. [ More] [PNAS blurb]

Does Homophily Predict Consensus Times? Testing a Model of Network Structure via a Dynamic Process (with Matthew O. Jackson)
Review of Network Economics, 11(3), 2012.
Many random network models forget most of the details of a network, focusing on just a few dimensions of its structure. Can such models nevertheless make good predictions about how a process would run on real networks, in all their complexity? [ More]

Network Structure and the Speed of Learning: Measuring Homophily Based on its Consequences (with Matthew O. Jackson)
Annals of Economics and Statistics, 107/108, 2012.
A simple measure of segregation in a network (in which less popular people matter more) predicts quite precisely how long convergence of beliefs will take under a naive process in which agents form their own beliefs by averaging those of their neighbors. [ More]

The Leverage of Weak Ties: How Linking Groups Affects Inequality (with Carlos Lever
Arbitrarily weak bridges linking social groups can have arbitrarily large consequences for inequality. [ More]
Firms, Queues, and Coffee Breaks: A Flow Model of Corporate Activity with Delays (with R. Preston McAfee)  
Review of Economic Design, 15(1), March 2011.
How and when to decentralize networked production — in a model that takes into account 'human' features of processing. [ More]

Stabilizing Brokerage (with Katherine Stovel and Eva Meyersson Milgrom
Proceedings of the National Academy of Sciences, 108(Suppl. 4):21326-21332, December 27, 2011.
Brokers facilitate transactions across gaps in social structure, and there are many reasons for their position to be unstable. Here, we take a look, from a sociological and an economic perspective, at what institutions stabilize brokerage. [ More]


[A brief research statement]