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https://dx.doi.org/10.4230/dag...
Article . 2005
License: CC BY
Data sources: Datacite
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Overcoming Free Riding in Multi-Party Computations

Authors: Smordinsky, Rann; Tennenholtz, Moshe;

Overcoming Free Riding in Multi-Party Computations

Abstract

This paper addresses the question of multi party computation in a model with asymmetric information. Each agent has a private value (secret), but in contrast to standard models, the agent incurs a cost when retrieving the secret. There is a social choice function the agents would like to compute and implement. All agents would like to perform a joint computation, which input is their vector of secrets. However, agents would like to free-ride on others contribution. A mechanism which elicits players secrets and performs the desired computation defines a game. A mechanism is `appropriate if it (weakly) implements the social choice function for all secret vectors. namely, if there exists an equilibrium in which it is able to elicit (sufficiently many) agents secrets and perform the computation, for all possible secret vectors. We show that `appropriate mechanisms approach agents sequentially and that they have low communication complexity.

Country
Germany
Keywords

330, compact representation of games, congestion games, action-graph gamescomputational markets, bidding strategiesNegotiatio, auctions, action-graph gamescomputational markets; auctions; bidding strategiesNegotiatio, local-effect games, 004

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
Green