6 Search Results for "Hartline, Jason"


Document
Equivocal Blends: Prior Independent Lower Bounds

Authors: Jason Hartline and Aleck Johnsen

Published in: LIPIcs, Volume 287, 15th Innovations in Theoretical Computer Science Conference (ITCS 2024)


Abstract
The prior independent framework for algorithm design considers how well an algorithm that does not know the distribution of its inputs approximates the expected performance of the optimal algorithm for this distribution. This paper gives a method that is agnostic to problem setting for proving lower bounds on the prior independent approximation factor of any algorithm. The method constructs a correlated distribution over inputs that can be described both as a distribution over i.i.d. good-for-algorithms distributions and as a distribution over i.i.d. bad-for-algorithms distributions. We call these two descriptions equivocal blends. Prior independent algorithms are upper-bounded by the optimal algorithm for the latter distribution even when the true distribution is the former. Thus, the ratio of the expected performances of the Bayesian optimal algorithms for these two decompositions is a lower bound on the prior independent approximation ratio. We apply this framework to give new lower bounds on canonical prior independent mechanism design problems. For one of these problems, we also exhibit a near-tight upper bound. Towards solutions for general problems, we give distinct descriptions of two large classes of correlated-distribution "solutions" for the technique, depending respectively on an order-statistic separability property and a paired inverse-distribution property. We exhibit that equivocal blends do not generally have a Blackwell ordering, which puts this paper outside of standard information design.

Cite as

Jason Hartline and Aleck Johnsen. Equivocal Blends: Prior Independent Lower Bounds. In 15th Innovations in Theoretical Computer Science Conference (ITCS 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 287, pp. 59:1-59:21, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)


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@InProceedings{hartline_et_al:LIPIcs.ITCS.2024.59,
  author =	{Hartline, Jason and Johnsen, Aleck},
  title =	{{Equivocal Blends: Prior Independent Lower Bounds}},
  booktitle =	{15th Innovations in Theoretical Computer Science Conference (ITCS 2024)},
  pages =	{59:1--59:21},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-309-6},
  ISSN =	{1868-8969},
  year =	{2024},
  volume =	{287},
  editor =	{Guruswami, Venkatesan},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ITCS.2024.59},
  URN =		{urn:nbn:de:0030-drops-195878},
  doi =		{10.4230/LIPIcs.ITCS.2024.59},
  annote =	{Keywords: prior independent algorithms, lower bounds, correlated decompositions, minimax, equivocal blends, mechanism design, blackwell ordering}
}
Document
Screening with Disadvantaged Agents

Authors: Hedyeh Beyhaghi, Modibo K. Camara, Jason Hartline, Aleck Johnsen, and Sheng Long

Published in: LIPIcs, Volume 256, 4th Symposium on Foundations of Responsible Computing (FORC 2023)


Abstract
Motivated by school admissions, this paper studies screening in a population with both advantaged and disadvantaged agents. A school is interested in admitting the most skilled students, but relies on imperfect test scores that reflect both skill and effort. Students are limited by a budget on effort, with disadvantaged students having tighter budgets. This raises a challenge for the principal: among agents with similar test scores, it is difficult to distinguish between students with high skills and students with large budgets. Our main result is an optimal stochastic mechanism that maximizes the gains achieved from admitting "high-skill" students minus the costs incurred from admitting "low-skill" students when considering two skill types and n budget types. Our mechanism makes it possible to give higher probability of admission to a high-skill student than to a low-skill, even when the low-skill student can potentially get higher test-score due to a higher budget. Further, we extend our admission problem to a setting in which students uniformly receive an exogenous subsidy to increase their budget for effort. This extension can only help the school’s admission objective and we show that the optimal mechanism with exogenous subsidies has the same characterization as optimal mechanisms for the original problem.

Cite as

Hedyeh Beyhaghi, Modibo K. Camara, Jason Hartline, Aleck Johnsen, and Sheng Long. Screening with Disadvantaged Agents. In 4th Symposium on Foundations of Responsible Computing (FORC 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 256, pp. 6:1-6:20, Schloss Dagstuhl - Leibniz-Zentrum für Informatik (2023)


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@InProceedings{beyhaghi_et_al:LIPIcs.FORC.2023.6,
  author =	{Beyhaghi, Hedyeh and Camara, Modibo K. and Hartline, Jason and Johnsen, Aleck and Long, Sheng},
  title =	{{Screening with Disadvantaged Agents}},
  booktitle =	{4th Symposium on Foundations of Responsible Computing (FORC 2023)},
  pages =	{6:1--6:20},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-272-3},
  ISSN =	{1868-8969},
  year =	{2023},
  volume =	{256},
  editor =	{Talwar, Kunal},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.FORC.2023.6},
  URN =		{urn:nbn:de:0030-drops-179274},
  doi =		{10.4230/LIPIcs.FORC.2023.6},
  annote =	{Keywords: screening, strategic classification, budgeted mechanism design, fairness, effort-incentives, subsidies, school admission}
}
Document
Fair Grading Algorithms for Randomized Exams

Authors: Jiale Chen, Jason Hartline, and Onno Zoeter

Published in: LIPIcs, Volume 256, 4th Symposium on Foundations of Responsible Computing (FORC 2023)


Abstract
This paper studies grading algorithms for randomized exams. In a randomized exam, each student is asked a small number of random questions from a large question bank. The predominant grading rule is simple averaging, i.e., calculating grades by averaging scores on the questions each student is asked, which is fair ex-ante, over the randomized questions, but not fair ex-post, on the realized questions. The fair grading problem is to estimate the average grade of each student on the full question bank. The maximum-likelihood estimator for the Bradley-Terry-Luce model on the bipartite student-question graph is shown to be consistent with high probability when the number of questions asked to each student is at least the cubed-logarithm of the number of students. In an empirical study on exam data and in simulations, our algorithm based on the maximum-likelihood estimator significantly outperforms simple averaging in prediction accuracy and ex-post fairness even with a small class and exam size.

Cite as

Jiale Chen, Jason Hartline, and Onno Zoeter. Fair Grading Algorithms for Randomized Exams. In 4th Symposium on Foundations of Responsible Computing (FORC 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 256, pp. 7:1-7:22, Schloss Dagstuhl - Leibniz-Zentrum für Informatik (2023)


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@InProceedings{chen_et_al:LIPIcs.FORC.2023.7,
  author =	{Chen, Jiale and Hartline, Jason and Zoeter, Onno},
  title =	{{Fair Grading Algorithms for Randomized Exams}},
  booktitle =	{4th Symposium on Foundations of Responsible Computing (FORC 2023)},
  pages =	{7:1--7:22},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-272-3},
  ISSN =	{1868-8969},
  year =	{2023},
  volume =	{256},
  editor =	{Talwar, Kunal},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.FORC.2023.7},
  URN =		{urn:nbn:de:0030-drops-179282},
  doi =		{10.4230/LIPIcs.FORC.2023.7},
  annote =	{Keywords: Ex-ante and Ex-post Fairness, Item Response Theory, Algorithmic Fairness in Education}
}
Document
Optimal Deterministic Clock Auctions and Beyond

Authors: Giorgos Christodoulou, Vasilis Gkatzelis, and Daniel Schoepflin

Published in: LIPIcs, Volume 215, 13th Innovations in Theoretical Computer Science Conference (ITCS 2022)


Abstract
We design and analyze deterministic and randomized clock auctions for single-parameter domains with downward-closed feasibility constraints, aiming to maximize the social welfare. Clock auctions have been shown to satisfy a list of compelling incentive properties making them a very practical solution for real-world applications, partly because they require very little reasoning from the participating bidders. However, the first results regarding the worst-case performance of deterministic clock auctions from a welfare maximization perspective indicated that they face obstacles even for a seemingly very simple family of instances, leading to a logarithmic inapproximability result; this inapproximability result is information-theoretic and holds even if the auction has unbounded computational power. In this paper we propose a deterministic clock auction that achieves a logarithmic approximation for any downward-closed set system, using black box access to a solver for the underlying optimization problem. This proves that our clock auction is optimal and that the aforementioned family of instances exactly captures the information limitations of deterministic clock auctions. We then move beyond deterministic auctions and design randomized clock auctions that achieve improved approximation guarantees for a generalization of this family of instances, suggesting that the earlier indications regarding the performance of clock auctions may have been overly pessimistic.

Cite as

Giorgos Christodoulou, Vasilis Gkatzelis, and Daniel Schoepflin. Optimal Deterministic Clock Auctions and Beyond. In 13th Innovations in Theoretical Computer Science Conference (ITCS 2022). Leibniz International Proceedings in Informatics (LIPIcs), Volume 215, pp. 49:1-49:23, Schloss Dagstuhl - Leibniz-Zentrum für Informatik (2022)


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@InProceedings{christodoulou_et_al:LIPIcs.ITCS.2022.49,
  author =	{Christodoulou, Giorgos and Gkatzelis, Vasilis and Schoepflin, Daniel},
  title =	{{Optimal Deterministic Clock Auctions and Beyond}},
  booktitle =	{13th Innovations in Theoretical Computer Science Conference (ITCS 2022)},
  pages =	{49:1--49:23},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-217-4},
  ISSN =	{1868-8969},
  year =	{2022},
  volume =	{215},
  editor =	{Braverman, Mark},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ITCS.2022.49},
  URN =		{urn:nbn:de:0030-drops-156453},
  doi =		{10.4230/LIPIcs.ITCS.2022.49},
  annote =	{Keywords: Auctions, Obvious Strategyproofness, Mechanism Design}
}
Document
Extended Abstract
Non-Quasi-Linear Agents in Quasi-Linear Mechanisms (Extended Abstract)

Authors: Moshe Babaioff, Richard Cole, Jason Hartline, Nicole Immorlica, and Brendan Lucier

Published in: LIPIcs, Volume 185, 12th Innovations in Theoretical Computer Science Conference (ITCS 2021)


Abstract
Mechanisms with money are commonly designed under the assumption that agents are quasi-linear, meaning they have linear disutility for spending money. We study the implications when agents with non-linear (specifically, convex) disutility for payments participate in mechanisms designed for quasi-linear agents. We first show that any mechanism that is truthful for quasi-linear buyers has a simple best response function for buyers with non-linear disutility from payments, in which each bidder simply scales down her value for each potential outcome by a fixed factor, equal to her target return on investment (ROI). We call such a strategy ROI-optimal. We prove the existence of a Nash equilibrium in which agents use ROI-optimal strategies for a general class of allocation problems. Motivated by online marketplaces, we then focus on simultaneous second-price auctions for additive bidders and show that all ROI-optimal equilibria in this setting achieve constant-factor approximations to suitable welfare and revenue benchmarks.

Cite as

Moshe Babaioff, Richard Cole, Jason Hartline, Nicole Immorlica, and Brendan Lucier. Non-Quasi-Linear Agents in Quasi-Linear Mechanisms (Extended Abstract). In 12th Innovations in Theoretical Computer Science Conference (ITCS 2021). Leibniz International Proceedings in Informatics (LIPIcs), Volume 185, p. 84:1, Schloss Dagstuhl - Leibniz-Zentrum für Informatik (2021)


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@InProceedings{babaioff_et_al:LIPIcs.ITCS.2021.84,
  author =	{Babaioff, Moshe and Cole, Richard and Hartline, Jason and Immorlica, Nicole and Lucier, Brendan},
  title =	{{Non-Quasi-Linear Agents in Quasi-Linear Mechanisms}},
  booktitle =	{12th Innovations in Theoretical Computer Science Conference (ITCS 2021)},
  pages =	{84:1--84:1},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-177-1},
  ISSN =	{1868-8969},
  year =	{2021},
  volume =	{185},
  editor =	{Lee, James R.},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ITCS.2021.84},
  URN =		{urn:nbn:de:0030-drops-136230},
  doi =		{10.4230/LIPIcs.ITCS.2021.84},
  annote =	{Keywords: Return on investment, Non-quasi-linear agents, Transferable Welfare, Simultaneous Second-Price Auctions}
}
Document
Welfare Maximization with Friends-of-Friends Network Externalities

Authors: Sayan Bhattacharya, Wolfgang Dvorák, Monika Henzinger, and Martin Starnberger

Published in: LIPIcs, Volume 30, 32nd International Symposium on Theoretical Aspects of Computer Science (STACS 2015)


Abstract
Online social networks allow the collection of large amounts of data about the influence between users connected by a friendship-like relationship. When distributing items among agents forming a social network, this information allows us to exploit network externalities that each agent receives from his neighbors that get the same item. In this paper we consider Friends-of-Friends (2-hop) network externalities, i.e., externalities that not only depend on the neighbors that get the same item but also on neighbors of neighbors. For these externalities we study a setting where multiple different items are assigned to unit-demand agents. Specifically, we study the problem of welfare maximization under different types of externality functions. Let n be the number of agents and m be the number of items. Our contributions are the following: (1) We show that welfare maximization is APX-hard; we show that even for step functions with 2-hop (and also with 1-hop) externalities it is NP-hard to approximate social welfare better than (1-1/e). (2) On the positive side we present (i) an O(sqrt n)-approximation algorithm for general concave externality functions, (ii) an O(\log m)-approximation algorithm for linear externality functions, and (iii) an (1-1/e)\frac{1}{6}-approximation algorithm for 2-hop step function externalities. We also improve the result from [6] for 1-hop step function externalities by giving a (1-1/e)/2-approximation algorithm.

Cite as

Sayan Bhattacharya, Wolfgang Dvorák, Monika Henzinger, and Martin Starnberger. Welfare Maximization with Friends-of-Friends Network Externalities. In 32nd International Symposium on Theoretical Aspects of Computer Science (STACS 2015). Leibniz International Proceedings in Informatics (LIPIcs), Volume 30, pp. 90-102, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2015)


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@InProceedings{bhattacharya_et_al:LIPIcs.STACS.2015.90,
  author =	{Bhattacharya, Sayan and Dvor\'{a}k, Wolfgang and Henzinger, Monika and Starnberger, Martin},
  title =	{{Welfare Maximization with Friends-of-Friends Network Externalities}},
  booktitle =	{32nd International Symposium on Theoretical Aspects of Computer Science (STACS 2015)},
  pages =	{90--102},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-939897-78-1},
  ISSN =	{1868-8969},
  year =	{2015},
  volume =	{30},
  editor =	{Mayr, Ernst W. and Ollinger, Nicolas},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.STACS.2015.90},
  URN =		{urn:nbn:de:0030-drops-49066},
  doi =		{10.4230/LIPIcs.STACS.2015.90},
  annote =	{Keywords: network externalities, welfare maximization, approximation algorithms}
}
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