Working Papers

Rational Inattention and Endogenous Volatility: A Large Deviation Approach (with Takashi Ui) [30-min Slides]

• Does rational inattention create endogenous volatility in large economies (#agents → ∞)?

• This paper subsumes a part of my JMP.

Recursive Rational Inattention Is Entropic (to be joint work with Henrique De Oliveira) [Slides]

• What kind of information cost makes the value function recursive?

Rational Inattention in Games [JMP version, JMP slides]

• Which equilibrium is robust to a small misspecification of players' cost of information acquisition?

• This paper is subsumed by the following two papers:

  • • Robustness of equilibria to endogenous information acquisition.

    • "Physics" approach to economic models (to be joint work with Takashi Ui).

Bargaining and Information Acquisition (with Kalyan Chatterjee and Miaomiao Dong)

• Does a buyer with cheap access to information get a surplus from a seller with full bargaining power?

Communication, Censorship, and Coordination

• Does communication with or without censorship help players coordinate?

Enforcement against Organized Crime Increases Organized Fraud (with Takuma Kamada)

• Did enforcement on the yakuza (criminal organizations in Japan) increase organized crime?

Publications

Multi-Agent Persuasion: Leveraging Strategic Uncertainty

• International Economic Review (2022)

• Can a principal persuade multiple agents to take particular actions by manipulating their higher-order beliefs?

Third-Party Policing Approaches against Organized Crime (with Takuma Kamada)

• Journal of Quantitative Criminology (2021)

• Did third-party policing against the yakuza (criminal organizations in Japan) weaken them?

Repeated Coordination with Private Learning (with Pathikrit Basu, Kalyan Chatterjee, and Omer Tamuz) [Slides]

• Journal of Economic Theory (2020)

• Does common learning occur in a repeated coordination game with observable actions? Can players eventually coordinate?

Short Notes

A Note on the Derivative of a Spectral Norm

• On the gradient formula of a spectral norm (often used to train neural networks)