24,930 research outputs found
A Robust Bayesian Truth Serum for Small Populations
Peer prediction mechanisms allow the truthful elicitation of private signals (e.g., experiences, or opinions) in regard to a true world state when this ground truth is unobservable. The original peer prediction method is incentive compatible for any number of agents n >= 2, but relies on a common prior, shared by all agents and the mechanism. The Bayesian Truth Serum (BTS) relaxes this assumption. While BTS still assumes that agents share a common prior, this prior need not be known to the mechanism. However, BTS is only incentive compatible for a large enough number of agents, and the particular number of agents required is uncertain because it depends on this private prior. In this paper, we present a robust BTS for the elicitation of binary information which is incentive compatible for every n >= 3, taking advantage of a particularity of the quadratic scoring rule. The robust BTS is the first peer prediction mechanism to provide strict incentive compatibility for every n >= 3 without relying on knowledge of the common prior. Moreover, and in contrast to the original BTS, our mechanism is numerically robust and ex post individually rational.Engineering and Applied Science
Information Gathering with Peers: Submodular Optimization with Peer-Prediction Constraints
We study a problem of optimal information gathering from multiple data
providers that need to be incentivized to provide accurate information. This
problem arises in many real world applications that rely on crowdsourced data
sets, but where the process of obtaining data is costly. A notable example of
such a scenario is crowd sensing. To this end, we formulate the problem of
optimal information gathering as maximization of a submodular function under a
budget constraint, where the budget represents the total expected payment to
data providers. Contrary to the existing approaches, we base our payments on
incentives for accuracy and truthfulness, in particular, {\em peer-prediction}
methods that score each of the selected data providers against its best peer,
while ensuring that the minimum expected payment is above a given threshold. We
first show that the problem at hand is hard to approximate within a constant
factor that is not dependent on the properties of the payment function.
However, for given topological and analytical properties of the instance, we
construct two greedy algorithms, respectively called PPCGreedy and
PPCGreedyIter, and establish theoretical bounds on their performance w.r.t. the
optimal solution. Finally, we evaluate our methods using a realistic crowd
sensing testbed.Comment: Longer version of AAAI'18 pape
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