Working Papers

These papers are working drafts of research which often appear in final form in academic journals. The published versions may differ from the working versions provided here.

Anqi Li, Davin Raiha, Ken Shotts
March 2020

We develop a model of electoral accountability with mainstream and alternative media. In addition to regular high- and low-competence types, the incumbent may be an aspiring autocrat who controls the mainstream media and will...

Sanath Kumar Krishnamurthy, Susan Athey
February 23, 2020

We consider a variant of the contextual bandit problem. In standard contextual bandits, when a user arrives we get the user’s complete feature vector and then assign a treatment (arm) to that user. In a...

Daniel Chen, Darrell Duffie
February 19, 2020

We model a simple market setting in which fragmentation of trade of the same asset across multiple exchanges improves allocative efficiency. Fragmentation reduces the inhibiting effect of price-impact avoidance on order submission. Although fragmentation reduces...

Benjamin Hébert, Jennifer La'O...
February 7, 2020

This paper analyzes non-fundamental volatility and efficiency in a class of large games (including e.g. linear-quadratic beauty contests) that feature strategic interaction and endogenous information acquisition. We adopt the rational inattention approach to information acquisition...

VItor Hadad, David A. Hirshberg, Ruohan Zhan, Stefan Wager, Susan Athey
February 2020

Adaptive experiment designs can dramatically improve statistical efficiency in randomized trials, but they also complicate statistical inference. For example, it is now well known that the sample mean is biased in adaptive trials. Inferential challenges...

Benjamin Hébert, Michael Woodford
January 31, 2020

We derive a new cost of information in rational inattention problems, the neighborhood-based cost functions, starting from the observation that many applications involve exogenous states with a

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Susan Athey, Kevin Bryan, Joshua S. Gans
January 10, 2020

The allocation of decision authority by a principal to either a human agent or an artificial intelligence (AI) is examined. The principal trades off an AI’s more aligned choice with the need to motivate the...

Morris A. Cohen, Shiliang Cui, Ricardo Ernst, Hau L. Lee, Arnd Huchzermeier, Panos Kouvelis, Hau L. Lee, Hirofumi Matsuo, Marc Steuber
January 8, 2020

This paper reports on the results of a global field study conducted in 2014 and 2015 among leading manufacturers from a wide range of industries. It provides insights on managerial practices that concern production sourcing...

Anat R. Admati
January 5, 2020

A healthy and stable financial system enables efficient resource allocation and risk sharing. A reckless and distorted system, however, causes enormous harm. The cycles of boom, bust, and crisis that repeatedly plague banking and finance...

Kevin Smith, Eric C. So
January 2020

We develop a measure of how information events impact investors’ perceptions of firms’ riskiness. We derive this measure from an option-pricing model where investors anticipate an announcement containing information on the mean and variance of...

Kun Kuang, Ruoxuan Xiong, Peng Cui, Susan Athey, Bo Li
January 2020

For many machine learning algorithms, two main assumptions are required to guarantee performance. One is that the test data are drawn from the same distribution as the training data, and the other is that the...

Yonatan Gur, Ahmadreza Momeni
2020

Sequential experiments are often designed to strike a balance between maximizing immediate payoffs based on available information, and acquiring new information that is essential for maximizing future payoffs. This trade-off is captured by the multi-armed...

Kostas Bimpikis, Wedad J. Elmaghraby, Ken Moon, Wenchang Zhang
2020

Platforms can obtain sizable returns by operationally managing their market thickness, i.e., the availability of supply-side inventory. Using data from a natural experiment on a major B2B auction platform specializing in the $424 billion secondary...

Yonatan Gur, Gregory Macnamara, Daniela Saban
2020
In many marketplaces that facilitate trade with the objective of maximizing consumer surplus, prices are set by revenue-maximizing sellers but platforms can influence prices through (i) price-dependent promotion policies that can increase demand for a product...
J. Choi, Daniela Saban, Gabriel Weintraub
2020
Yonatan Gur, Gregory Macnamara, Daniela Saban
2020

We study the design of sequential procurement strategies that integrate stochastic and strategic information. We consider a buyer who repeatedly demands a certain good and is unable to commit to long-term contracts. In each time...

Yonatan Gur, Ahmadreza Momeni, Stefan Wager
2020

We study a non-parametric multi-armed bandit problem with stochastic covariates, where a key complexity driver is the smoothness of payoff functions with respect to covariates. Previous studies have focused on deriving minimax-optimal algorithms in cases...

Yonatan Gur, Dan A. Iancu, Xavier Warnes
2020

Centralized planning systems routinely allocate tasks to workers or service providers in order to generate the maximum possible value. These allocations can also critically influence the service providers’ well-being, and thus the planning systems are...

Charles M. C. Lee, Eric C. So, Charles C. Y. Wang
December 30, 2019

We introduce a parsimonious framework for choosing among alternative expected-return proxies (ERPs) when estimating treatment effects. By comparing ERPs’ measurement-error variances in the cross-section and time series, we provide new evidence on the relative performance...

Paulo Somaini
December 12, 2019

This paper provides a positive identification result for first-price procurement models with asymmetric bidders, statistically dependent private signals,
and interdependent costs. When bidders are risk neutral, the model’s payoff-relevant primitives are: (i) the joint...