2017
DOI: 10.1109/tac.2016.2619674
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The Robustness of Marginal-Cost Taxes in Affine Congestion Games

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Cited by 28 publications
(15 citation statements)
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“…Proof of Proposition 4.1: The first part of the proposition comes from [35]. We thus find the nominally equivalent subsidy mechanism and find the associated price of anarchy bound.…”
Section: Appendixmentioning
confidence: 83%
See 1 more Smart Citation
“…Proof of Proposition 4.1: The first part of the proposition comes from [35]. We thus find the nominally equivalent subsidy mechanism and find the associated price of anarchy bound.…”
Section: Appendixmentioning
confidence: 83%
“…We assign taxes using the optimal scaled marginal cost toll with player heterogeneity, T smc (af + b) := ( √ S L S U ) −1 af . This tolling mechanism was first introduced in [35], and was shown to minimize the price of anarchy in parallel affine congestion games with sensitivity distributions in S bounded by S L and S U . In Proposition 4.1, we give price of anarchy bounds on the optimal scaled marginal cost toll as well as a nominally equivalent subsidy T nes .…”
Section: B Robustness Of Incentives In Affine Congestion Gamesmentioning
confidence: 99%
“…Recent research has identified several new network-agnostic taxation mechanisms, which are all in some sense generalizations of T mc : in restricted settings, scaled marginal-cost tolls can be non-perverse [21] while guaranteeing improvements; furthermore, there exists a universal taxation mechanism based on T mc which optimizes routing using large tolls [4]. Dynamic network-agnostic tolls converging to T mc are studied in [22].…”
Section: Related Workmentioning
confidence: 99%
“…As we use financial incentives to influence this behavior, our formulation is related to work in tolling, some of which consider users with different sensitivities to tolls [16], [17]. [18] considers a congestion game framework and derives tolls which drive users to choose socially optimal strategies for a broad class of user strategy update dynamics.…”
Section: Introductionmentioning
confidence: 99%