Passager clandestin (économie)

Résumé

Dans les sciences sociales, le free-rider problem (traduit en français par « 'problème du passager clandestin' ») est un type de défaillance du marché ou d'une organisation qui se produit lorsque ceux qui bénéficient d'une ressource ou d'un service (biens publics tels que routes ou hôpitaux publics, biens de nature communale) ne le paient pas ou le sous-paient. Les passagers clandestins peuvent poser problème car sans payer ou en sous-payant le bien (soit directement par des redevances, des cotisations ou des péages, soit indirectement par des taxes), ils continuent à y accéder ou à l'utiliser. Ainsi, le bien peut être sous-produit, sur-utilisé ou dégradé. Dans la langue française, dans le langage courant et dans les traductions, le terme de « passager clandestin » ne permettant pas de faire toujours clairement référence au concept de free rider, on trouve souvent d'autres expressions pour exprimer ce concept, selon le contexte : problème des bénéficiaires à titre gratuit ou du béné

Source officielle

https://fr.wikipedia.org/wiki/Passager_clandestin_(%C3%A9conomie)

À propos de ce résultat

Cette page est générée automatiquement et peut contenir des informations qui ne sont pas correctes, complètes, à jour ou pertinentes par rapport à votre recherche. Il en va de même pour toutes les autres pages de ce site. Veillez à vérifier les informations auprès des sources officielles de l'EPFL.

Publications associées (5)

Publications associées

Chargement

Personnes associées

Chargement

Unités associées

Chargement

Concepts associés (15)

Concepts associés

Chargement

Cours associés (5)

Cours associés

Chargement

Séances de cours associées (6)

Afficher plus

Séances de cours associées

Chargement

Monetary incentives, such as subsidies or bonuses, are often considered as a way to foster contributions to public goods in society and firms. This paper investigates experimentally the effect of private contribution incentives in the presence of a norm enforcement mechanism. Norm enforcement through peer punishment has been shown to be effective in raising contributions by itself. We test whether and how (centrally provided) private incentives interact with (decentralized) punishment, both of which affect subjects’ monetary payoffs. The results of our experiment show that private incentives for contributors can reduce the effectiveness of the norm enforcement mechanism: Free riders are punished less harshly in the treatment with incentives, and as a consequence, average contributions to the public good are no higher than without incentives. This finding ties to and extends previous research on settings in which monetary incentives may fail to have the desired effect.

2010

Chargement

On Tracking Freeriders in Gossip Protocols

Rachid Guerraoui, Kévin Clément Huguenin, Anne-Marie Kermarrec, Maxime Monod

Peer-to-peer content dissemination applications suffer immensely from freeriders, i.e., nodes that do not provide their fair share. The Tit-for-Tat (TfT) incentives have received much attention as they help make such systems more robust against freeriding. However, these rely on an asymmetric component, namely opportunistic pushes, that let peers receive content without sending anything in return. Opportunistic push constitutes the Achilles’ heel of TfT-based protocols as illustrated by the fact that all known attacks against them exploit it. This problem becomes even more serious when used by colluding freeriders. In this paper, we discuss the possibility of using accountability to secure gossip-based dissemination protocols based on asymmetric exchanges. The fact that gossip protocols are dynamic and randomized makes our approach robust against collusion and alleviates the need for cryptography. We present the challenges raised by an auditing approach and give insights into how to build a freerider-tracking protocol for gossip-based content dissemination.

2009

Chargement

, , ,

Gossip-based protocols have proven very efficient for disseminating high-bandwidth content such as video streams in a peer-to-peer fashion. However, for the protocols to work, nodes are required to collaborate by devoting a fraction of their upload bandwidth, a scarce resource for some of them, to forward the content they receive to other nodes. Consequently, such protocols suffer from freeriding, a common phenomenon on the Internet, which consists in selfishly benefiting from the system without contributing its fair share. Due to the dynamic nature and the inherent randomness of gossip protocols and to the high scalability requirements of video streaming systems, detecting freeriders is a difficult challenge. This paper presents LiFTinG, the first protocol for detecting freeriders, including colluding ones, in gossip-based content dissemination systems with asymmetric data exchanges. In addition, LiFTinG is still able to detect freeriders when network coding, a widely used technique to improve the efficiency of content dissemination, is used. LiFTinG relies on nodes to track abnormal behavior by cross-checking the history of their previous interactions and exploits the fact that nodes pick neighbors at random to prevent colluding nodes from mutually covering up their bad actions. We present a methodology for setting the parameters of LiFTinG to their optimal value, based on a theoretical analysis and we quantify theoretically the performance of LiFTinG. We derive, based on simulations, the optimal strategy of freeriders by taking into account, through a utility function, the benefit of freeriding and the probability of being detected. In addition to these simulations, we report on the deployment of LiFTinG on PlanetLab. In a 300-node system, where a stream of 674 kbps is broadcasted, LiFTinG incurs a maximum overhead of only 8% and provides good detection results: For instance, with 10% of freeriders decreasing their contribution by up to 30%, LiFTinG detects 86% of the freeriders after only 30 s and wrongfully expels only a few honest nodes (most of them actually being buggy). (c) 2014 Elsevier B.V. All rights reserved.

Elsevier2014

Chargement

, , ,

Elsevier2014