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  • 1
    Online Resource
    Online Resource
    Elsevier BV ; 2015
    In:  Journal of Combinatorial Theory, Series B Vol. 114 ( 2015-09), p. 170-186
    In: Journal of Combinatorial Theory, Series B, Elsevier BV, Vol. 114 ( 2015-09), p. 170-186
    Type of Medium: Online Resource
    ISSN: 0095-8956
    RVK:
    Language: English
    Publisher: Elsevier BV
    Publication Date: 2015
    detail.hit.zdb_id: 1469158-9
    SSG: 17,1
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  • 2
    Online Resource
    Online Resource
    Association for Computing Machinery (ACM) ; 2022
    In:  ACM SIGMETRICS Performance Evaluation Review Vol. 49, No. 2 ( 2022-01-17), p. 6-8
    In: ACM SIGMETRICS Performance Evaluation Review, Association for Computing Machinery (ACM), Vol. 49, No. 2 ( 2022-01-17), p. 6-8
    Abstract: While users claim to be concerned about privacy, often they do little to protect their privacy in their online actions. One prominent explanation for this "privacy paradox" is that when an individual shares her data, it is not just her privacy that is compromised; the privacy of other individuals with correlated data is also compromised. This information leakage encourages oversharing of data and significantly impacts the incentives of individuals in online platforms. In this extended abstract, we discuss the design of mechanisms for data acquisition in settings with information leakage and verifiable data. We summarize work designing an incentive compatible mechanism that optimizes the worst-case tradeoff between bias and variance of the estimation subject to a budget constraint, where the worst-case is over the unknown correlation between costs and data. Additionally, we characterize the structure of the optimal mechanism in closed form and study monotonicity and non-monotonicity properties of the marketplace.
    Type of Medium: Online Resource
    ISSN: 0163-5999
    Language: English
    Publisher: Association for Computing Machinery (ACM)
    Publication Date: 2022
    detail.hit.zdb_id: 199353-7
    detail.hit.zdb_id: 2089001-1
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  • 3
    Online Resource
    Online Resource
    Institute for Operations Research and the Management Sciences (INFORMS) ; 2022
    In:  Mathematics of Operations Research Vol. 47, No. 4 ( 2022-11), p. 3207-3238
    In: Mathematics of Operations Research, Institute for Operations Research and the Management Sciences (INFORMS), Vol. 47, No. 4 ( 2022-11), p. 3207-3238
    Abstract: We introduce the pipeline intervention problem, defined by a layered directed acyclic graph and a set of stochastic matrices governing transitions between successive layers. The graph is a stylized model for how people from different populations are presented opportunities, eventually leading to some reward. In our model, individuals are born into an initial position (i.e., some node in the first layer of the graph) according to a fixed probability distribution and then stochastically progress through the graph according to the transition matrices until they reach a node in the final layer of the graph; each node in the final layer has a reward associated with it. The pipeline intervention problem asks how to best make costly changes to the transition matrices governing people’s stochastic transitions through the graph subject to a budget constraint. We consider two objectives: social welfare maximization and a fairness-motivated maximin objective that seeks to maximize the value to the population (starting node) with the least expected value. We consider two variants of the maximin objective that turn out to be distinct, depending on whether we demand a deterministic solution or allow randomization. For each objective, we give an efficient approximation algorithm (an additive fully polynomial-time approximation scheme) for constant-width networks. We also tightly characterize the “price of fairness” in our setting: the ratio between the highest achievable social welfare and the social welfare consistent with a maximin optimal solution. Finally, we show that, for polynomial-width networks, even approximating the maximin objective to any constant factor is NP hard even for networks with constant depth. This shows that the restriction on the width in our positive results is essential.
    Type of Medium: Online Resource
    ISSN: 0364-765X , 1526-5471
    Language: English
    Publisher: Institute for Operations Research and the Management Sciences (INFORMS)
    Publication Date: 2022
    detail.hit.zdb_id: 2004273-5
    detail.hit.zdb_id: 195683-8
    SSG: 3,2
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  • 4
    Online Resource
    Online Resource
    Association for Computing Machinery (ACM) ; 2016
    In:  ACM SIGMETRICS Performance Evaluation Review Vol. 44, No. 1 ( 2016-06-30), p. 383-384
    In: ACM SIGMETRICS Performance Evaluation Review, Association for Computing Machinery (ACM), Vol. 44, No. 1 ( 2016-06-30), p. 383-384
    Abstract: This paper studies design challenges faced by a geo-distributed cloud data market: which data to purchase (data purchasing) and where to place/replicate the data (data placement). We show that the joint problem of data purchasing and data placement within a cloud data market is NP-hard in general. However, we give a provably optimal algorithm for the case of a data market made up of a single data center, and then generalize the structure from the single data center setting and propose Datum, a near-optimal, polynomial-time algorithm for a geo-distributed data market.
    Type of Medium: Online Resource
    ISSN: 0163-5999
    Language: English
    Publisher: Association for Computing Machinery (ACM)
    Publication Date: 2016
    detail.hit.zdb_id: 199353-7
    detail.hit.zdb_id: 2089001-1
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  • 5
    Online Resource
    Online Resource
    Association for Computing Machinery (ACM) ; 2020
    In:  ACM SIGMETRICS Performance Evaluation Review Vol. 48, No. 1 ( 2020-07-08), p. 103-104
    In: ACM SIGMETRICS Performance Evaluation Review, Association for Computing Machinery (ACM), Vol. 48, No. 1 ( 2020-07-08), p. 103-104
    Abstract: 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.
    Type of Medium: Online Resource
    ISSN: 0163-5999
    Language: English
    Publisher: Association for Computing Machinery (ACM)
    Publication Date: 2020
    detail.hit.zdb_id: 199353-7
    detail.hit.zdb_id: 2089001-1
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  • 6
    Online Resource
    Online Resource
    Institute of Electrical and Electronics Engineers (IEEE) ; 2018
    In:  IEEE/ACM Transactions on Networking Vol. 26, No. 2 ( 2018-4), p. 893-905
    In: IEEE/ACM Transactions on Networking, Institute of Electrical and Electronics Engineers (IEEE), Vol. 26, No. 2 ( 2018-4), p. 893-905
    Type of Medium: Online Resource
    ISSN: 1063-6692 , 1558-2566
    Language: Unknown
    Publisher: Institute of Electrical and Electronics Engineers (IEEE)
    Publication Date: 2018
    detail.hit.zdb_id: 2004853-1
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  • 7
    Online Resource
    Online Resource
    Association for the Advancement of Artificial Intelligence (AAAI) ; 2018
    In:  Proceedings of the AAAI Conference on Artificial Intelligence Vol. 32, No. 1 ( 2018-04-25)
    In: Proceedings of the AAAI Conference on Artificial Intelligence, Association for the Advancement of Artificial Intelligence (AAAI), Vol. 32, No. 1 ( 2018-04-25)
    Abstract: We introduce the notion of exploitability in cut-and-choose protocols for repeated cake cutting. If a cut-and-choose protocol is repeated, the cutter can possibly gain information about the chooser from her previous actions, and exploit this information for her own gain, at the expense of the chooser. We define a generalization of cut-and-choose protocols - forced-cut protocols - in which some cuts are made exogenously while others are made by the cutter, and show that there exist non-exploitable forced-cut protocols that use a small number of cuts per day: When the cake has at least as many dimensions as days, we show a protocol that uses a single cut per day. When the cake is 1-dimensional, we show an adaptive non-exploitable protocol that uses 3 cuts per day, and a non-adaptive protocol that uses n cuts per day (where n is the number of days). In contrast, we show that no non-adaptive non-exploitable forced-cut protocol can use a constant number of cuts per day. Finally, we show that if the cake is at least 2-dimensional, there is a non-adaptive non-exploitable protocol that uses 3 cuts per day.
    Type of Medium: Online Resource
    ISSN: 2374-3468 , 2159-5399
    Language: Unknown
    Publisher: Association for the Advancement of Artificial Intelligence (AAAI)
    Publication Date: 2018
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  • 8
    Online Resource
    Online Resource
    Association for Computing Machinery (ACM) ; 2020
    In:  Proceedings of the ACM on Measurement and Analysis of Computing Systems Vol. 4, No. 1 ( 2020-05-27), p. 1-31
    In: Proceedings of the ACM on Measurement and Analysis of Computing Systems, Association for Computing Machinery (ACM), Vol. 4, No. 1 ( 2020-05-27), p. 1-31
    Abstract: 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.
    Type of Medium: Online Resource
    ISSN: 2476-1249
    Language: English
    Publisher: Association for Computing Machinery (ACM)
    Publication Date: 2020
    detail.hit.zdb_id: 2924209-5
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