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Cornell University, USA
Bounding the price of anarchy, which quantifies the damage to social welfare due to selfish behavior of the participants, has been an important area of research in algorithmic game theory. Classical work on such bounds in repeated games makes the strong ...
Massachusetts Institute of Technology, Cambridge, Massachusetts 02139;
,Cornell University, Ithaca, New York 14850
,Cornell University, Ithaca, New York 14850
We consider the problem of adversarial (nonstochastic) online learning with partial-information feedback, in which, at each round, a decision maker selects an action from a finite set of alternatives. We develop a black-box approach for such problems in ...
Cornell University, Ithaca, NY, USA
,Cornell University, Ithaca, NY, USA
,Cornell University, Ithaca, NY, USA
Ridesharing markets are complex: drivers are strategic, rider demand and driver availability are stochastic, and complex city-scale phenomena like weather induce large scale correlation across space and time. At the same time, past work has focused on a ...
Cornell University, Ithaca, NY, USA
,Cornell University, Ithaca, NY, USA
We consider the problem of selfish agents in discrete-time queuing systems, where competitive queues try to get their packets served. In this model, a queue gets to send a packet each step to one of the servers, which will attempt to serve the oldest ...
Cornell University, Ithaca, NY, USA
,Cornell University, Ithaca, NY, USA
,Cornell University, Ithaca, NY, USA
In light of increasing recent attention to political polarization, understanding how polarization can arise poses an important theoretical question. While more classical models of opinion dynamics seem poorly equipped to study this phenomenon, a recent ...
Department of Mathematics, London School of Economics, London WC2A 2AE, United Kingdom;
,Institute of Computer Science, University of Bonn, 53115 Bonn, Germany;
,Department of Computer Science, Cornell University, Ithaca, New York 14853
Many algorithms that are originally designed without explicitly considering incentive properties are later combined with simple pricing rules and used as mechanisms. A key question is therefore to understand which algorithms, or, more generally, which ...
Google LLC, CA
,Cornell University, Ithaca, NY
We study the problem of a budget limited buyer who wants to buy a set of items, each from a different seller, to maximize her value. The budget feasible mechanism design problem requires the design a mechanism that incentivizes the sellers to truthfully ...
Cornell University, Ithaca, NY, USA
,Cornell University, Ithaca, NY, USA
Bounding the price of anarchy, which quantifies the damage to social welfare due to selfish behavior of the participants, has been an important area of research in algorithmic game theory. In this paper, we study this phenomenon in the context of a game ...
Cornell University, Ithaca, NY, USA
,Cornell University, Ithaca, NY, USA
,Cornell University, Ithaca, NY, USA
In this paper, we study the connections between network structure, opinion dynamics, and an adversary's power to artificially induce disagreements. We approach these questions by extending models of opinion formation in the mathematical social sciences ...
Google, Mountain View, CA, USA
,Cornell University, Ithaca, NY, USA
,Cornell University, Ithaca, NY, USA
Market equilibria of matching markets offer an intuitive and fair solution for matching problems without money with agents who have preferences over the items. Such a matching market can be viewed as a variation of Fisher market, albeit with rather ...
Computer Science Department, Stanford University, Stanford, CA
,Microsoft Research, Cambridge, MA
,Computer Science Department, Cornell University, Ithaca, NY
This survey outlines a general and modular theory for proving approximation guarantees for equilibria of auctions in complex settings. This theory complements traditional economic techniques, which generally focus on exact and optimal solutions and are ...
Editor-in-Chief
Cornell University
,Tsinghua University
,Cornell University
,Cornell University
,Cornell University
We show that learning algorithms satisfying a low approximate regret property experience fast convergence to approximate optimality in a large class of repeated games. Our property, which simply requires that each learner has small regret compared to a (...
ETH Zürich
,Max-Planck-Institut für Informatik
,Cornell University
We show that algorithms that follow the relax-and-round paradigm translate approximation guarantees into Price of Anarchy guarantees, provided that the rounding is oblivious and the relaxation is smooth. We use this meta result to obtain simple, near-...
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