Article records
https://feeds.library.caltech.edu/people/Ligett-K/article.rss
A Caltech Library Repository Feedhttp://www.rssboard.org/rss-specificationpython-feedgenenThu, 30 Nov 2023 18:11:39 +0000Beating the best Nash without regret
https://resolver.caltech.edu/CaltechAUTHORS:20190111-090750274
Authors: Ligett, Katrina; Piliouras, Georgios
Year: 2011
DOI: 10.1145/1978721.1978727
Nash equilibrium analysis has become the de facto solution standard in game theory. This approach, despite its prominent role, has been the subject of much criticism for being too optimistic. Indeed, in general games, natural play need not converge to Nash equilibria. In games with multiple equilibria, it is unclear how players are expected to coordinate; even in games with a unique equilibrium, finding it may involve unreasonable expectations on player communication or computation.https://authors.library.caltech.edu/records/w0e8z-24q16The Power of Fair Pricing Mechanisms
https://resolver.caltech.edu/CaltechAUTHORS:20120319-102934819
Authors: Chung, Christine; Ligett, Katrina; Pruhs, Kirk; Roth, Aron
Year: 2012
DOI: 10.1007/s00453-011-9587-1
We explore the revenue capabilities of truthful, monotone ("fair") allocation and pricing functions for resource-constrained auction mechanisms within a general framework that encompasses unlimited supply auctions, knapsack auctions, and auctions with general non-decreasing convex production cost functions. We study and compare the revenue obtainable in each fair pricing scheme to the profit obtained by the ideal omniscient multi-price auction. We show that for capacitated knapsack auctions, no constant pricing scheme can achieve any approximation to the optimal profit, but proportional pricing is as powerful as general monotone pricing. In addition, for auction settings with arbitrary bounded non-decreasing convex production cost functions, we present a proportional pricing mechanism which achieves a poly-logarithmic approximation. Unlike existing approaches, all of our mechanisms have fair (monotone) prices, and all of our competitive analysis is with respect to the optimal profit extraction.https://authors.library.caltech.edu/records/5ywwh-br579A Learning Theory Approach to Noninteractive Database Privacy
https://resolver.caltech.edu/CaltechAUTHORS:20130618-102022824
Authors: Blum, Avrim; Ligett, Katrina; Roth, Aaron
Year: 2013
DOI: 10.1145/2450142.2450148
In this article, we demonstrate that, ignoring computational constraints, it is possible to release synthetic databases that are useful for accurately answering large classes of queries while preserving differential privacy. Specifically, we give a mechanism that privately releases synthetic data useful for answering a class of queries over a discrete domain with error that grows as a function of the size of the smallest net approximately representing the answers to that class of queries. We show that this in particular implies a mechanism for counting queries that gives error guarantees that grow only with the VC-dimension of the class of queries, which itself grows at most logarithmically with the size of the query class.
We also show that it is not possible to release even simple classes of queries (such as intervals and their generalizations) over continuous domains with worst-case utility guarantees while preserving differential privacy. In response to this, we consider a relaxation of the utility guarantee and give a privacy preserving polynomial time algorithm that for any halfspace query will provide an answer that is accurate for some small perturbation of the query. This algorithm does not release synthetic data, but instead another data structure capable of representing an answer for each query. We also give an efficient algorithm for releasing synthetic data for the class of interval queries and axis-aligned rectangles of constant dimension over discrete domains.https://authors.library.caltech.edu/records/1v9a5-fra16A Tale of Two Metrics: Simultaneous Bounds on Competitiveness and Regret
https://resolver.caltech.edu/CaltechAUTHORS:20160420-130614870
Authors: Andrew, Lachlan; Barman, Siddharth; Ligett, Katrina; Lin, Minghong; Meyerson, Adam; Roytman, Alan; Wierman, Adam
Year: 2013
DOI: 10.1145/2494232.2465533
We consider algorithms for "smoothed online convex optimization" (SOCO) problems, which are a hybrid between
online convex optimization (OCO) and metrical task system
(MTS) problems. Historically, the performance metric
for OCO was regret and that for MTS was competitive ratio
(CR). There are algorithms with either sublinear regret or
constant CR, but no known algorithm achieves both simultaneously. We show that this is a fundamental limitation – no algorithm (deterministic or randomized) can achieve sublinear regret and a constant CR, even when the objective functions are linear and the decision space is one dimensional. However, we present an algorithm that, for the important one dimensional case, provides sublinear regret and a CR that grows arbitrarily slowly.https://authors.library.caltech.edu/records/cbbaw-cqb13Information-sharing in social networks
https://resolver.caltech.edu/CaltechAUTHORS:20140814-132525010
Authors: Kleinberg, Jon; Ligett, Katrina
Year: 2013
DOI: 10.1016/j.geb.2013.10.002
We present a new model for reasoning about the way information is shared among friends in a social network and the resulting ways in which the social network fragments. Our model formalizes the intuition that revealing personal information in social settings involves a trade-off between the benefits of sharing information with friends, and the risks that additional gossiping will propagate it to someone with whom one is not on friendly terms but who is within oneʼs community. We study the behavior of rational agents in such a situation, and we characterize the existence and computability of stable information-sharing configurations, in which agents do not have an incentive to change the set of partners with whom they share information. We analyze the implications of these stable configurations for social welfare and the resulting fragmentation of the social network.https://authors.library.caltech.edu/records/ddvc9-hgr63Privacy and Data-Based Research
https://resolver.caltech.edu/CaltechAUTHORS:20141204-135134569
Authors: Heffetz, Ori; Ligett, Katrina
Year: 2014
DOI: 10.1257/jep.28.2.75
What can we, as users of microdata, formally guarantee to the individuals (or firms) in our dataset, regarding their privacy? We retell a few stories, well-known in data-privacy circles, of failed anonymization attempts in publicly released datasets. We then provide a mostly informal introduction to several ideas from the literature on differential privacy, an active literature in computer science that studies formal approaches to preserving the privacy of individuals in statistical databases. We apply some of its insights to situations routinely faced by applied economists, emphasizing big-data contexts.https://authors.library.caltech.edu/records/yxygj-5a706Network improvement for equilibrium routing
https://resolver.caltech.edu/CaltechAUTHORS:20150220-135314566
Authors: Bhaskar, Umang; Ligett, Katrina
Year: 2014
DOI: 10.1145/2728732.2728737
Routing games are frequently used to model the behavior of traffic in large networks, such as road networks. In transportation research, the problem of adding capacity to a road network in a cost-effective manner to minimize the total delay at equilibrium is known as the Network Design Problem, and has received considerable attention. However, prior to our work, little was known about guarantees for polynomial-time algorithms for this problem. We obtain tight approximation guarantees for general and series-parallel networks, and present a number of open questions for future work.https://authors.library.caltech.edu/records/g4b4k-ytj48Contention Resolution under Selfishness
https://resolver.caltech.edu/CaltechAUTHORS:20141208-082730675
Authors: Christodoulou, George; Ligett, Katrina; Pyrga, Evangelia
Year: 2014
DOI: 10.1007/s00453-013-9773-4
In many communications settings, such as wired and wireless local-area networks, when multiple users attempt to access a communication channel at the same time, a conflict results and none of the communications are successful. Contention resolution is the study of distributed transmission and retransmission protocols designed to maximize notions of utility such as channel utilization in the face of blocking communications.
An additional issue to be considered in the design of such protocols is that selfish users may have incentive to deviate from the prescribed behavior, if another transmission strategy increases their utility. The work of Fiat et al. (in SODA '07, pp. 179–188, SIAM, Philadelphia 2007) addresses this issue by constructing an asymptotically optimal incentive-compatible protocol. However, their protocol assumes the cost of any single transmission is zero, and the protocol completely collapses under non-zero transmission costs.
In this paper we treat the case of non-zero transmission cost c. We present asymptotically optimal contention resolution protocols that are robust to selfish users, in two different channel feedback models. Our main result is in the Collision Multiplicity Feedback model, where after each time slot, the number of attempted transmissions is returned as feedback to the users. In this setting, we give a protocol that has expected cost Θ(n+clogn) and is in o(1)-equilibrium, where n is the number of users.https://authors.library.caltech.edu/records/6c73h-bpv16Finding any nontrivial coarse correlated equilibrium is hard
https://resolver.caltech.edu/CaltechAUTHORS:20151124-112840356
Authors: Barman, Siddharth; Ligett, Katrina
Year: 2015
DOI: 10.1145/2845926.2845929
One of the most appealing aspects of correlated equilibria and coarse correlated equilibria is that natural dynamics quickly arrive at approximations of such equilibria, even in games with many players. In addition, there exist polynomial-time algorithms that compute exact correlated and coarse correlated equilibria. However, in general these dynamics and algorithms do not provide a guarantee on the quality (say, in terms of social welfare) of the resulting equilibrium. In light of these results, a natural question is how good are the correlated and coarse correlated equilibria---in terms natural objectives such as social welfare or Pareto optimality---that can arise from any efficient algorithm or dynamics.
We address this question, and establish strong negative results. In particular, we show that in multiplayer games that have a succinct representation, it is NP-hard to compute any coarse correlated equilibrium (or approximate coarse correlated equilibrium) with welfare strictly better than the worst possible. The focus on succinct games ensures that the underlying complexity question is interesting; many multiplayer games of interest are in fact succinct. We show that analogous hardness results hold for correlated equilibria, and persist under the egalitarian objective or Pareto optimality.
To complement the hardness results, we develop an algorithmic framework that identifies settings in which we can efficiently compute an approximate correlated equilibrium with near-optimal welfare. We use this framework to develop an efficient algorithm for computing an approximate correlated equilibrium with near-optimal welfare in aggregative games.https://authors.library.caltech.edu/records/z8st3-8jp90Adaptive Learning with Robust Generalization Guarantees
https://resolver.caltech.edu/CaltechAUTHORS:20190627-154059380
Authors: Cummings, Rachel; Ligett, Katrina; Nissim, Kobbi; Roth, Aaron; Wu, Zhiwei Steven
Year: 2016
DOI: 10.48550/arXiv.1602.07726
The traditional notion of generalization—i.e., learning a hypothesis whose empirical error is close to its true error—is surprisingly brittle. As has recently been noted [Dwork et al. 2015], even if several algorithms have this guarantee in isolation, the guarantee need not hold if the algorithms are composed adaptively. In this paper, we study three notions of generalization—increasing in strength—that are \emphrobust to postprocessing and amenable to adaptive composition, and examine the relationships between them. We call the weakest such notion \emphRobust Generalization. A second, intermediate, notion is the stability guarantee known as \emphdifferential privacy. The strongest guarantee we consider we call \emphPerfect Generalization. We prove that every hypothesis class that is PAC learnable is also PAC learnable in a robustly generalizing fashion, with almost the same sample complexity. It was previously known that differentially private algorithms satisfy robust generalization. In this paper, we show that robust generalization is a strictly weaker concept, and that there is a learning task that can be carried out subject to robust generalization guarantees, yet cannot be carried out subject to differential privacy. We also show that perfect generalization is a strictly stronger guarantee than differential privacy, but that, nevertheless, many learning tasks can be carried out subject to the guarantees of perfect generalization.https://authors.library.caltech.edu/records/cha6p-hme85Commitment in first-price auctions
https://resolver.caltech.edu/CaltechAUTHORS:20180816-074937577
Authors: Xu, Yunjian; Ligett, Katrina
Year: 2018
DOI: 10.1007/s00199-017-1069-5
We study a variation of the single-item sealed-bid first-price auction wherein one bidder (the leader) publicly commits to a (possibly mixed) strategy before the others submit their bids. For the case wherein all bidders' valuations are commonly known, we fully characterize the committed mixed strategy that is optimal for the leader and find that both the leader and the follower with the highest valuation strictly benefit from the commitment, so long as the leader's valuation is strictly higher than the second highest valuation of the followers. We further show that compared with the simultaneous first-price auction, the leader's optimal commitment yields the same net utility benefit to both of these bidders. As a result, the two highest valued bidders' incentives are aligned, facilitating coordination and implementation of the commitment. Finally, we provide characterization of the leader's optimal commitment in a Bayesian setting with two bidders, leveraging the methodology developed for the complete-information setting.https://authors.library.caltech.edu/records/d49gb-pjj92Special Issue on the Economics of Security and Privacy: Guest Editors' Introduction
https://resolver.caltech.edu/CaltechAUTHORS:20190110-110039117
Authors: Böhme, Rainer; Clayton, Richard; Grossklags, Jens; Ligett, Katrina; Loiseau, Patrick; Schwartz, Galina
Year: 2018
DOI: 10.1145/3216902
This editorial introduces the special issue on the economics of security and privacy.
The global adoption of the Internet has transformed economies and societies. However, Internet
technologies have also resulted in heightened societal concerns about information security and
privacy. Insufficient safeguards—actual or perceived—have become a barrier to certain economic
activity, and a source of downside risk to growth and sustainability, with possible systemic impact.
Scholars have long realized that choices pertaining to security and privacy affect the world in
ways that are not captured within the narrow modeling of engineering systems. In essence, these
choices are strategic decisions. Thus, the analysis that is performed should incorporate the models
and methods developed in economics and, where applicable, in the behavioral sciences.https://authors.library.caltech.edu/records/j1103-95r12Beyond myopic best response (in Cournot competition)
https://resolver.caltech.edu/CaltechAUTHORS:20190312-141425964
Authors: Fiat, Amos; Koutsoupias, Elias; Ligett, Katrina; Mansour, Yishay; Olonetsky, Svetlana
Year: 2019
DOI: 10.1016/j.geb.2013.12.006
The Nash equilibrium as a prediction myopically ignores the possibility that deviating from the equilibrium could lead to an avalanche of beneficial changes by other agents.
We consider a non-myopic version of Cournot competition, where each firm selects either profit maximization (as in the classical model) or revenue maximization (by masquerading as a firm with zero production costs). We consider many non-identical firms with linear demand functions and show existence of pure Nash equilibria, that simple dynamics will produce such an equilibrium, and that some natural dynamics converge within linear time.
Furthermore, we compare the outcome of the non-myopic Cournot competition with that of the standard Cournot competition. Prices in the non-myopic game are lower and the firms, in total, produce more and have a lower aggregate utility.
We also briefly consider a non-myopic version of Bertrand competition, and find that prices increase relative to the classical model.https://authors.library.caltech.edu/records/q7nc7-rwn21Achieving target equilibria in network routing games without knowing the latency functions
https://resolver.caltech.edu/CaltechAUTHORS:20180622-082816994
Authors: Bhaskar, Umang; Ligett, Katrina; Schulman, Leonard J.; Swamy, Chaitanya
Year: 2019
DOI: 10.1016/j.geb.2018.02.009
The analysis of network routing games typically assumes precise, detailed information about the latency functions. Such information may, however, be unavailable or difficult to obtain. Moreover, one is often primarily interested in enforcing a desired target flow as an equilibrium. We ask whether one can achieve target flows as equilibria without knowing the underlying latency functions. We give a crisp positive answer to this question. We show that one can efficiently compute edge tolls that induce a given target multicommodity flow in a nonatomic routing game using a polynomial number of queries to an oracle that takes tolls as input and outputs the resulting equilibrium flow. This result is obtained via a novel application of the ellipsoid method, and extends to various other settings. We obtain improved query-complexity bounds for series-parallel networks, and single-commodity routing games with linear latency functions. Our techniques provide new insights into network routing games.https://authors.library.caltech.edu/records/zyfqc-8bf55Third-Party Data Providers Ruin Simple Mechanisms
https://resolver.caltech.edu/CaltechAUTHORS:20190626-155536214
Authors: Cai, Yang; Echenique, Federico; Fu, Hu; Ligett, Katrina; Wierman, Adam; Ziani, Juba
Year: 2020
DOI: 10.1145/3379478
Motivated by the growing prominence of third-party data providers in online marketplaces, this paper studies the impact of the presence of third-party data providers on mechanism design. When no data provider is present, it has been shown that simple mechanisms are "good enough'' -- they can achieve a constant fraction of the revenue of optimal mechanisms. The results in this paper demonstrate that this is no longer true in the presence of a third-party data provider who can provide the bidder with a signal that is correlated with the item type. Specifically, even with a single seller, a single bidder, and a single item of uncertain type for sale, the strategies of pricing each item-type separately (the analog of item pricing for multi-item auctions) and bundling all item-types under a single price (the analog of grand bundling) can both simultaneously be a logarithmic factor worse than the optimal revenue. Further, in the presence of a data provider, item-type partitioning mechanisms---a more general class of mechanisms which divide item-types into disjoint groups and offer prices for each group---still cannot achieve within a $łog łog$ factor of the optimal revenue. Thus, our results highlight that the presence of a data-provider forces the use of more complicated mechanisms in order to achieve a constant fraction of the optimal revenue.https://authors.library.caltech.edu/records/r67cj-d4544Third-Party Data Providers Ruin Simple Mechanisms
https://resolver.caltech.edu/CaltechAUTHORS:20200709-084932341
Authors: Cai, Yang; Echenique, Federico; Fu, Hu; Ligett, Katrina; Wierman, Adam; Ziani, Juba
Year: 2020
DOI: 10.1145/3410048.3410108
Motivated by the growing prominence of third-party data providers in online marketplaces, this paper studies the impact of the presence of third-party data providers on mechanism design. When no data provider is present, it has been shown that simple mechanisms are "good enough" -they can achieve a constant fraction of the revenue of optimal mechanisms. The results in this paper demonstrate that this is no longer true in the presence of a third-party data provider who can provide the bidder with a signal that is correlated with the item type. Specifically, even with a single seller, a single bidder, and a single item of uncertain type for sale, the strategies of pricing each item-type separately (the analog of item pricing for multiitem auctions) and bundling all item-types under a single price (the analog of grand bundling) can both simultaneously be a logarithmic factor worse than the optimal revenue. Further, in the presence of a data provider, item-type partitioning mechanisms-a more general class of mechanisms which divide item-types into disjoint groups and offer prices for each group-still cannot achieve within a log log factor of the optimal revenue. Thus, our results highlight that the presence of a data-provider forces the use of more complicated mechanisms in order to achieve a constant fraction of the optimal revenue.https://authors.library.caltech.edu/records/hdp6f-c2s32