## Working Papers

**Learning from Viral Content** (with Krishna Dasaratha)

[abstract] [download pdf] [arXiv]

**Private Private Information** (with Fedor Sandomirskiy and Omer Tamuz)

Revise and resubmit at the *Journal of Political Economy*.

Presented at *ACM EC’22*.

[abstract] [download pdf] [slides] [talk] [arXiv]

*private*private information structure delivers information about an unknown state while preserving privacy: An agent’s signal contains information about the state but remains independent of others’ sensitive or private information. We study how informative such structures can be, and characterize those that are optimal in the sense that they cannot be made more informative without violating privacy. We connect our results to fairness in recommendation systems and explore a number of further applications.

**Screening p-Hackers: Dissemination Noise as Bait** (with Federico Echenique)

Presented at

*ACM EC’22*.

[abstract] [download pdf] [arXiv]

*p*-hacked findings: spurious explanations produced by fitting many statistical models (data mining). Noise creates “baits” that affect two types of researchers differently. Uninformed

*p*-hackers, who are fully ignorant of the true mechanism and engage in data mining, often fall for baits. Informed researchers, who start with an ex-ante hypothesis, are minimally affected. We characterize the optimal noise level and highlight the relevant trade-offs. Dissemination noise is a tool that statistical agencies currently use to protect privacy. We argue this existing practice can be repurposed to screen

*p*-hackers and thus improve research credibility.

**Evolutionarily Stable (Mis)specifications: Theory and Applications** (with Jonathan Libgober)

Presented at *ACM EC’21*.

[abstract] [download pdf] [arXiv]

*learning channel*: the endogeneity of perceived best replies due to inference. We characterize when a rational society is only vulnerable to invasion by some misspecification through the learning channel. The learning channel leads to new stability phenomena, and can confer an evolutionary advantage to otherwise detrimental biases in economically relevant applications.

**Aggregative Efficiency of Bayesian Learning in Networks** (with Krishna Dasaratha)

Revise and resubmit at *Econometrica*.

Best Paper Award at *ACM EC’21*.

[abstract] [download pdf] [slides] [EC'21 talk] [arXiv]

*confound*information, and even a small amount of confounding can lead to much lower accuracy. In a class of networks where agents move in generations and observe the previous generation, we quantify the information loss with an

*aggregative efficiency*index. Aggregative efficiency is a simple function of network parameters: increasing in observations and decreasing in confounding. Later generations contribute little additional information, even with arbitrarily large generations.

**Dynamic Information Preference and Communication with Diminishing Sensitivity Over News** (with Jetlir Duraj)

Accepted subject to a revision at *Theoretical Economics*.

[abstract] [download pdf] [slides] [arXiv]

## Published Papers

**Observability, Dominance, and Induction in Learning Models** (with Daniel Clark and Drew Fudenberg)

*Journal of Economic Theory* 206:105569, December 2022.

[abstract] [download pdf] [publisher’s DOI] [arXiv]

*terminal node partitions*so that two games are

*information equivalent*, i.e., the players receive the same feedback about others’ strategies.

**Mislearning from Censored Data: The Gambler’s Fallacy and Other Correlational Mistakes in Optimal-Stopping
Problems**

*Theoretical Economics* 17(3):1269–1312, July 2022.

[abstract] [download pdf] [publisher’s DOI] [arXiv]

**Network Structure and Social Learning** (with Krishna Dasaratha)

*ACM SIGecom Exchanges* 19(2):62–67, November 2021.

[abstract] [download pdf] [publisher’s DOI]

**An Experiment on Network Density and Sequential Learning** (with Krishna Dasaratha)

*Games and Economic Behavior* 128:182-192, July 2021. Presented at *ACM EC’20*.

[abstract] [download pdf] [slides] [publisher’s DOI] [pre-registration] [data and code] [EC’20 talk] [arXiv]

**Player-Compatible Learning and Player-Compatible Equilibrium** (with Drew Fudenberg)

*Journal of Economic Theory* 194:105238, June 2021.

[abstract] [download
pdf] [online
appendix] [publisher’s DOI] [arXiv]

*Player-Compatible Equilibrium*(PCE) imposes cross-player restrictions on the magnitudes of the players’ “trembles” onto different strategies. These restrictions capture the idea that trembles correspond to deliberate experiments by agents who are unsure of the prevailing distribution of play. PCE selects intuitive equilibria in a number of examples where trembling-hand perfect equilibrium (Selten, 1975) and proper equilibrium (Myerson, 1978) have no bite. We show that rational learning and weighted fictitious play imply our compatibility restrictions in a steady-state setting.

**Network Structure and Naive Sequential Learning** (with Krishna Dasaratha)

*Theoretical Economics* 15(2):415–444, May 2020.

[abstract] [download pdf]
[publisher’s DOI] [arXiv]

**Payoff Information and Learning in Signaling Games** (with Drew Fudenberg)

*Games and Economic Behavior* 120:96-120, March 2020.

[abstract] [download
pdf] [publisher’s DOI] [arXiv]

*rationality-compatible equilibria*(

*RCE*), and bounded below by

*uniform RCE*. RCE refine the Intuitive Criterion (Cho and Kreps, 1987) and include all divine equilibria (Banks and Sobel, 1987). Uniform RCE sometimes but not always exists, and implies universally divine equilibrium.

**Learning and Type Compatibility in Signaling Games** (with Drew Fudenberg)

*Econometrica* 86(4):1215-1255, July 2018.

[abstract] [download
pdf] [online appendix] [publisher’s DOI] [arXiv]

**Bayesian Posteriors for Arbitrarily Rare Events** (with Drew Fudenberg and Lorens Imhof)

*Proceedings of the National Academy of Sciences* 114(19):4925-4929, May 2017.

[abstract] [download pdf] [publisher’s DOI] [arXiv]

**Differentially Private and Incentive Compatible Recommendation System for the
Adoption of Network Goods** (with Xiaosheng
Mu)

*Proceedings of the 15th ACM Conference on Economics and Computation
(ACM EC’14)*:949-966, June 2014.

[abstract] [download pdf] [slides]
[publisher’s DOI]

## Current Classes

I'm not currently teaching.

## Previous Classes

**Game Theory and Applications** (ECON 6110, previously known as ECON 682)

An introduction to game theory aimed at graduate students in SAS/SEAS/Wharton.

I will teach this class again in Spring 2024.

[syllabus] [teaching evaluations]

**Topics in Advanced Microeconomic Theory** (ECON 8000, previously known as ECON 712)

A topics class for Ph.D. students in Economics, featuring a few of my favorite things: learning in games, learning in networks, and learning with psychological agents.

I will teach this class again in Spring 2024.

[syllabus] [teaching evaluations]