25 citations · 69 across the 11 of their papers we have counts for
14 papers
The Centralizing Effects of Private Order Flow on Proposer-Builder Separation
Tivas Gupta, Mallesh M Pai, Max Resnick
The current Proposer-Builder Separation (PBS) equilibrium has several builders with different backgrounds winning blocks consistently. This paper considers how that equilibrium wil…
Censorship Resistance in On-Chain Auctions
Elijah Fox, Mallesh Pai, Max Resnick
Modern blockchains guarantee that submitted transactions will be included eventually; a property formally known as liveness. But financial activity requires transactions to be incl…
Online Minimax Multiobjective Optimization: Multicalibeating and Other Applications
Daniel Lee, Georgy Noarov, Mallesh Pai +1
We introduce a simple but general online learning framework in which a learner plays against an adversary in a vector-valued game that changes every round. Even though the learner'…
The Wisdom of the Crowd and Higher-Order Beliefs
Yi-Chun Chen, Manuel Mueller-Frank, Mallesh M Pai
We propose a new simple procedure called Population-Mean-Based Aggregation (PMBA) that enables a principal to "aggregate" information about an unknown state of the world from agent…
Online Multivalid Learning: Means, Moments, and Prediction Intervals
Varun Gupta, Christopher Jung, Georgy Noarov +2
We present a general, efficient technique for providing contextual predictions that are "multivalid" in various senses, against an online sequence of adversarially chosen examples…
Moment Multicalibration for Uncertainty Estimation
Christopher Jung, Changhwa Lee, Mallesh M. Pai +2
We show how to achieve the notion of "multicalibration" from Hébert-Johnson et al. [2018] not just for means, but also for variances and other higher moments. Informally, it means…
Fair Prediction with Endogenous Behavior
Christopher Jung, Sampath Kannan, Changhwa Lee +3
There is increasing regulatory interest in whether machine learning algorithms deployed in consequential domains (e.g. in criminal justice) treat different demographic groups "fair…
Competing Models
Jose Luis Montiel Olea, Pietro Ortoleva, Mallesh M Pai +1
Different agents need to make a prediction. They observe identical data, but have different models: they predict using different explanatory variables. We study which agent believe…
Fairness Incentives for Myopic Agents
Sampath Kannan, Michael Kearns, Jamie Morgenstern +4
We consider settings in which we wish to incentivize myopic agents (such as Airbnb landlords, who may emphasize short-term profits and property safety) to treat arriving clients fa…
Robust Mediators in Large Games
Michael Kearns, Mallesh M. Pai, Ryan Rogers +2
A mediator is a mechanism that can only suggest actions to players, as a function of all agents' reported types, in a given game of incomplete information. We study what is achieva…
The Strange Case of Privacy in Equilibrium Models
Rachel Cummings, Katrina Ligett, Mallesh M. Pai +1
We study how privacy technologies affect user and advertiser behavior in a simple economic model of targeted advertising. In our model, a consumer first decides whether or not to b…
An Anti-Folk Theorem for Large Repeated Games with Imperfect Monitoring
Mallesh M. Pai, Aaron Roth, Jonathan Ullman
We study infinitely repeated games in settings of imperfect monitoring. We first prove a family of theorems that show that when the signals observed by the players satisfy a condit…
Privacy and Mechanism Design
Mallesh Pai, Aaron Roth
This paper is a survey of recent work at the intersection of mechanism design and privacy. The connection is a natural one, but its study has been jump-started in recent years by t…
Mechanism Design in Large Games: Incentives and Privacy
Michael Kearns, Mallesh M. Pai, Aaron Roth +1
We study the problem of implementing equilibria of complete information games in settings of incomplete information, and address this problem using "recommender mechanisms." A reco…