Uncertainty in Artificial Intelligence
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Sequential Information Elicitation in Multi-Agent Systems
Rann Smorodinsky, Moshe Tennenholtz
We introduce the study of sequential infor-mation elicitation in strategic multi-agent systems. In an information elicitation setup a center attempts to compute the value of a function based on private information (a-k-a secrets) accessible to a set of agents. We consider the classical multi-party computa-tion setup where each agent is interested in knowing the result of the function. However, in our setting each agent is strategic,and since acquiring information is costly, an agent may be tempted not spending the efforts of obtaining the information, free-riding on other agents' computations. A mechanism which elicits agents' secrets and performs the desired computation defines a game. A mech-anism is 'appropriate' if there exists an equi-librium in which it is able to elicit (sufficiently many) agents' secrets and perform the computation, for all possible secret vectors.We characterize a general efficient procedure for determining an appropriate mechanism, if such mechanism exists. Moreover, we also address the existence problem, providing a polynomial algorithm for verifying the existence of an appropriate mechanism.
Keywords: null
Pages: 528-535
PS Link:
PDF Link: /papers/04/p528-smorodinsky.pdf
AUTHOR = "Rann Smorodinsky and Moshe Tennenholtz",
TITLE = "Sequential Information Elicitation in Multi-Agent Systems",
BOOKTITLE = "Proceedings of the Twentieth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-04)",
ADDRESS = "Arlington, Virginia",
YEAR = "2004",
PAGES = "528--535"

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