55 research outputs found
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
Partial Truthfulness in Minimal Peer Prediction Mechanisms with Limited Knowledge
We study minimal single-task peer prediction mechanisms that have limited
knowledge about agents' beliefs. Without knowing what agents' beliefs are or
eliciting additional information, it is not possible to design a truthful
mechanism in a Bayesian-Nash sense. We go beyond truthfulness and explore
equilibrium strategy profiles that are only partially truthful. Using the
results from the multi-armed bandit literature, we give a characterization of
how inefficient these equilibria are comparing to truthful reporting. We
measure the inefficiency of such strategies by counting the number of dishonest
reports that any minimal knowledge-bounded mechanism must have. We show that
the order of this number is , where is the number of
agents, and we provide a peer prediction mechanism that achieves this bound in
expectation
Optimum Statistical Estimation with Strategic Data Sources
We propose an optimum mechanism for providing monetary incentives to the data
sources of a statistical estimator such as linear regression, so that high
quality data is provided at low cost, in the sense that the sum of payments and
estimation error is minimized. The mechanism applies to a broad range of
estimators, including linear and polynomial regression, kernel regression, and,
under some additional assumptions, ridge regression. It also generalizes to
several objectives, including minimizing estimation error subject to budget
constraints. Besides our concrete results for regression problems, we
contribute a mechanism design framework through which to design and analyze
statistical estimators whose examples are supplied by workers with cost for
labeling said examples
Dominantly Truthful Multi-task Peer Prediction with a Constant Number of Tasks
In the setting where participants are asked multiple similar possibly
subjective multi-choice questions (e.g. Do you like Panda Express? Y/N; do you
like Chick-fil-A? Y/N), a series of peer prediction mechanisms are designed to
incentivize honest reports and some of them achieve dominantly truthfulness:
truth-telling is a dominant strategy and strictly dominate other
"non-permutation strategy" with some mild conditions. However, a major issue
hinders the practical usage of those mechanisms: they require the participants
to perform an infinite number of tasks. When the participants perform a finite
number of tasks, these mechanisms only achieve approximated dominant
truthfulness. The existence of a dominantly truthful multi-task peer prediction
mechanism that only requires a finite number of tasks remains to be an open
question that may have a negative result, even with full prior knowledge.
This paper answers this open question by proposing a new mechanism,
Determinant based Mutual Information Mechanism (DMI-Mechanism), that is
dominantly truthful when the number of tasks is at least 2C and the number of
participants is at least 2. C is the number of choices for each question (C=2
for binary-choice questions). In addition to incentivizing honest reports,
DMI-Mechanism can also be transferred into an information evaluation rule that
identifies high-quality information without verification when there are at
least 3 participants. To the best of our knowledge, DMI-Mechanism is the first
dominantly truthful mechanism that works for a finite number of tasks, not to
say a small constant number of tasks.Comment: To appear in SODA2
Civic Crowdfunding for Agents with Negative Valuations and Agents with Asymmetric Beliefs
In the last decade, civic crowdfunding has proved to be effective in
generating funds for the provision of public projects. However, the existing
literature deals only with citizen's with positive valuation and symmetric
belief towards the project's provision. In this work, we present novel
mechanisms which break these two barriers, i.e., mechanisms which incorporate
negative valuation and asymmetric belief, independently. For negative
valuation, we present a methodology for converting existing mechanisms to
mechanisms that incorporate agents with negative valuations. Particularly, we
adapt existing PPR and PPS mechanisms, to present novel PPRN and PPSN
mechanisms which incentivize strategic agents to contribute to the project
based on their true preference. With respect to asymmetric belief, we propose a
reward scheme Belief Based Reward (BBR) based on Robust Bayesian Truth Serum
mechanism. With BBR, we propose a general mechanism for civic crowdfunding
which incorporates asymmetric agents. We leverage PPR and PPS, to present PPRx
and PPSx. We prove that in PPRx and PPSx, agents with greater belief towards
the project's provision contribute more than agents with lesser belief.
Further, we also show that contributions are such that the project is
provisioned at equilibrium.Comment: Accepted as full paper in IJCAI 201
Deep Bayesian Trust : A Dominant and Fair Incentive Mechanism for Crowd
An important class of game-theoretic incentive mechanisms for eliciting
effort from a crowd are the peer based mechanisms, in which workers are paid by
matching their answers with one another. The other classic mechanism is to have
the workers solve some gold standard tasks and pay them according to their
accuracy on gold tasks. This mechanism ensures stronger incentive compatibility
than the peer based mechanisms but assigning gold tasks to all workers becomes
inefficient at large scale. We propose a novel mechanism that assigns gold
tasks to only a few workers and exploits transitivity to derive accuracy of the
rest of the workers from their peers' accuracy. We show that the resulting
mechanism ensures a dominant notion of incentive compatibility and fairness
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