4 research outputs found
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
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