368 research outputs found
Optimal Algorithm for Bayesian Incentive-Compatible Exploration
We consider a social planner faced with a stream of myopic selfish agents.
The goal of the social planner is to maximize the social welfare, however, it
is limited to using only information asymmetry (regarding previous outcomes)
and cannot use any monetary incentives. The planner recommends actions to
agents, but her recommendations need to be Bayesian Incentive Compatible to be
followed by the agents. Our main result is an optimal algorithm for the
planner, in the case that the actions realizations are deterministic and have
limited support, making significant important progress on this open problem.
Our optimal protocol has two interesting features. First, it always completes
the exploration of a priori more beneficial actions before exploring a priori
less beneficial actions. Second, the randomization in the protocol is
correlated across agents and actions (and not independent at each decision
time).Comment: EC 201
Learning Valuation Distributions from Partial Observation
Auction theory traditionally assumes that bidders' valuation distributions
are known to the auctioneer, such as in the celebrated, revenue-optimal Myerson
auction. However, this theory does not describe how the auctioneer comes to
possess this information. Recently, Cole and Roughgarden [2014] showed that an
approximation based on a finite sample of independent draws from each bidder's
distribution is sufficient to produce a near-optimal auction. In this work, we
consider the problem of learning bidders' valuation distributions from much
weaker forms of observations. Specifically, we consider a setting where there
is a repeated, sealed-bid auction with bidders, but all we observe for each
round is who won, but not how much they bid or paid. We can also participate
(i.e., submit a bid) ourselves, and observe when we win. From this information,
our goal is to (approximately) recover the inherently recoverable part of the
underlying bid distributions. We also consider extensions where different
subsets of bidders participate in each round, and where bidders' valuations
have a common-value component added to their independent private values
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