381 citations · 1.4k across the 100 of their papers we have counts for
15 papers · 1 filter
Strategic Apple Tasting
Keegan Harris, Chara Podimata, Zhiwei Steven Wu
Algorithmic decision-making in high-stakes domains often involves assigning decisions to agents with incentives to strategically modify their input to the algorithm. In addition to…
Meta-Learning in Games
Keegan Harris, Ioannis Anagnostides, Gabriele Farina +3
In the literature on game-theoretic equilibrium finding, focus has mainly been on solving a single game in isolation. In practice, however, strategic interactions -- ranging from r…
Game-Theoretic Algorithms for Conditional Moment Matching
Gokul Swamy, Sanjiban Choudhury, J. Andrew Bagnell +1
A variety of problems in econometrics and machine learning, including instrumental variable regression and Bellman residual minimization, can be formulated as satisfying a set of c…
Bayesian Persuasion for Algorithmic Recourse
Keegan Harris, Valerie Chen, Joon Sik Kim +3
When subjected to automated decision-making, decision subjects may strategically modify their observable features in ways they believe will maximize their chances of receiving a fa…
Information Discrepancy in Strategic Learning
Yahav Bechavod, Chara Podimata, Zhiwei Steven Wu +1
We initiate the study of the effects of non-transparency in decision rules on individuals' ability to improve in strategic learning settings. Inspired by real-life settings, such a…
Bayesian Exploration with Heterogeneous Agents
Nicole Immorlica, Jieming Mao, Aleksandrs Slivkins +1
It is common in recommendation systems that users both consume and produce information as they make strategic choices under uncertainty. While a social planner would balance "explo…
The Perils of Exploration under Competition: A Computational Modeling Approach
Guy Aridor, Kevin Liu, Aleksandrs Slivkins +1
We empirically study the interplay between exploration and competition. Systems that learn from interactions with users often engage in exploration: making potentially suboptimal d…
Incentivizing Exploration with Selective Data Disclosure
Nicole Immorlica, Jieming Mao, Aleksandrs Slivkins +1
We propose and design recommendation systems that incentivize efficient exploration. Agents arrive sequentially, choose actions and receive rewards, drawn from fixed but unknown ac…
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…
Competing Bandits: Learning under Competition
Yishay Mansour, Aleksandrs Slivkins, Zhiwei Steven Wu
Most modern systems strive to learn from interactions with users, and many engage in exploration: making potentially suboptimal choices for the sake of acquiring new information. W…
Logarithmic Query Complexity for Approximate Nash Computation in Large Games
Paul W. Goldberg, Francisco J. Marmolejo-Cossío, Zhiwei Steven Wu
We investigate the problem of equilibrium computation for "large" -player games. Large games have a Lipschitz-type property that no single player's utility is greatly affected b…
Bayesian Exploration: Incentivizing Exploration in Bayesian Games
Yishay Mansour, Aleksandrs Slivkins, Vasilis Syrgkanis +1
We consider a ubiquitous scenario in the Internet economy when individual decision-makers (henceforth, agents) both produce and consume information as they make strategic choices i…
Inducing Approximately Optimal Flow Using Truthful Mediators
Ryan Rogers, Aaron Roth, Jonathan Ullman +1
We revisit a classic coordination problem from the perspective of mechanism design: how can we coordinate a social welfare maximizing flow in a network congestion game with selfish…
Approximately Stable, School Optimal, and Student-Truthful Many-to-One Matchings (via Differential Privacy)
Sampath Kannan, Jamie Morgenstern, Aaron Roth +1
We present a mechanism for computing asymptotically stable school optimal matchings, while guaranteeing that it is an asymptotic dominant strategy for every student to report their…
Private Matchings and Allocations
Justin Hsu, Zhiyi Huang, Aaron Roth +2
We consider a private variant of the classical allocation problem: given k goods and n agents with individual, private valuation functions over bundles of goods, how can we partiti…