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200 papers

Auctions in which agents' payoffs are random variables have received increased attention in recent years. In particular, recent work in algorithmic mechanism design has produced mechanisms employing internal randomization, partly in…

Computer Science and Game Theory · Computer Science 2012-06-15 Shaddin Dughmi , Yuval Peres

We consider a model where an agent has a repeated decision to make and wishes to maximize their total payoff. Payoffs are influenced by an action taken by the agent, but also an unknown state of the world that evolves over time. Before…

Computer Science and Game Theory · Computer Science 2021-01-20 Nicole Immorlica , Ian Kash , Brendan Lucier

Auditing fairness of decision-makers is now in high demand. To respond to this social demand, several fairness auditing tools have been developed. The focus of this study is to raise an awareness of the risk of malicious decision-makers who…

Machine Learning · Statistics 2019-12-02 Kazuto Fukuchi , Satoshi Hara , Takanori Maehara

This manuscript studies a general approach to construct confidence sets for the solution of stochastic optimization, rendering empirical risk minimization as special cases. Statistical inference for stochastic optimization poses significant…

Statistics Theory · Mathematics 2026-05-22 Kenta Takatsu , Arun Kumar Kuchibhotla

Common sense suggests that when individuals explain why they believe something, we can arrive at more accurate conclusions than when they simply state what they believe. Yet, there is no known mechanism that provides incentives to elicit…

Computer Science and Game Theory · Computer Science 2025-02-20 Siddarth Srinivasan , Ezra Karger , Michiel Bakker , Yiling Chen

We study the problem of fitting the high dimensional sparse linear regression model with sub-Gaussian covariates and responses, where the data are provided by strategic or self-interested agents (individuals) who prioritize their privacy of…

Computer Science and Game Theory · Computer Science 2024-10-18 Liyang Zhu , Amina Manseur , Meng Ding , Jinyan Liu , Jinhui Xu , Di Wang

We consider the problem of deciding on sampling strategy, in particular sampling design. We propose a risk measure, whose minimizing value guides the choice. The method makes use of a superpopulation model and takes into account uncertainty…

Methodology · Statistics 2020-07-06 Edgar Bueno , Dan Hedlin

We consider a multi-round auction setting motivated by pay-per-click auctions for Internet advertising. In each round the auctioneer selects an advertiser and shows her ad, which is then either clicked or not. An advertiser derives value…

Data Structures and Algorithms · Computer Science 2013-06-05 Moshe Babaioff , Yogeshwer Sharma , Aleksandrs Slivkins

Unbiased and consistent variance estimators generally do not exist for design-based treatment effect estimators because experimenters never observe more than one potential outcome for any unit. The problem is exacerbated by interference and…

Methodology · Statistics 2024-07-04 Christopher Harshaw , Joel A. Middleton , Fredrik Sävje

When deciding how to act under uncertainty, agents may choose to act to reduce uncertainty or they may act despite that uncertainty. In communicative settings, an important way of reducing uncertainty is by asking clarification questions…

Computation and Language · Computer Science 2026-05-26 Polina Tsvilodub , Karl Mulligan , Todd Snider , Robert D. Hawkins , Michael Franke

Proper scoring rules elicit truth-telling when making predictions, or otherwise revealing information. However, when multiple predictions are made of the same event, telling the truth is in general no longer optimal, as agents are motivated…

Computer Science and Game Theory · Computer Science 2017-07-04 Amir Ban

Obviously strategyproof (OSP) mechanisms maintain the incentive compatibility of agents that are not fully rational. They have been object of a number of studies since their recent definition. A research agenda, initiated in…

Computer Science and Game Theory · Computer Science 2018-04-30 Diodato Ferraioli , Carmine Ventre

In this paper, we propose standard statistical tools as a solution to commonly highlighted problems in the explainability literature. Indeed, leveraging statistical estimators allows for a proper definition of explanations, enabling…

Machine Learning · Statistics 2024-05-01 Valentina Ghidini

Human agents are increasingly serving as data sources in the context of dynamical systems. Unlike traditional sensors, humans may manipulate or omit data for selfish reasons. Therefore, this paper studies the influence of effort-averse…

Systems and Control · Electrical Eng. & Systems 2020-04-02 Yuelin Zhao , Roy Dong

We study truthful mechanisms for matching and related problems in a partial information setting, where the agents' true utilities are hidden, and the algorithm only has access to ordinal preference information. Our model is motivated by the…

Computer Science and Game Theory · Computer Science 2016-10-20 Elliot Anshelevich , Shreyas Sekar

Mechanism design is addressed in the context of fair allocations of indivisible goods with monetary compensation. Motivated by a real-world social choice problem, mechanisms with verification are considered in a setting where (i) agents'…

Computer Science and Game Theory · Computer Science 2012-09-18 Gianluigi Greco , Francesco Scarcello

We consider a social planner faced with a stream of myopic selfish agents. The goal of the social planner is to maximize the social welfare, however, it is limited to using only information asymmetry (regarding previous outcomes) and cannot…

Computer Science and Game Theory · Computer Science 2019-05-15 Lee Cohen , Yishay Mansour

The acquisition of survey responses is a crucial component in conducting research aimed at comprehending public opinion. However, survey data collection can be arduous, time-consuming, and expensive, with no assurance of an adequate…

Computers and Society · Computer Science 2023-05-30 Alireza Amirshahi , Nicolas Kirsch , Jonathan Reymond , Saleh Baghersalimi

We address a practical problem ubiquitous in modern marketing campaigns, in which a central agent tries to learn a policy for allocating strategic financial incentives to customers and observes only bandit feedback. In contrast to…

Machine Learning · Statistics 2019-11-12 Romain Lopez , Chenchen Li , Xiang Yan , Junwu Xiong , Michael I. Jordan , Yuan Qi , Le Song

We study a truthful two-facility location problem in which a set of agents have private positions on the line of real numbers and known approval preferences over two different facilities. Given the locations of the two facilities, the cost…

Computer Science and Game Theory · Computer Science 2024-01-10 Panagiotis Kanellopoulos , Alexandros A. Voudouris , Rongsen Zhang