Jens Witkowski – Publications
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2012
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Jens Witkowski and David C. Parkes.
A Bayesian Truth Serum for Small Populations.
Technical Report No. 267,
Institut für Informatik, Universität Freiburg, 2012.
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Peer prediction methods 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
finite number of agents n ≥ 2 but critically relies on a common
prior, shared by all agents and the center. The Bayesian Truth
Serum (BTS) relaxes this assumption. While it still assumes that
the agents share a common prior, this prior need not be known by
the center. However, BTS is proven to be incentive compatible
only for a large enough number of agents, and this number
depends on the prior and is thus unknown to the mechanism. In
this paper, we present a robust BTS for the elicitation of
binary information which is incentive compatible for any n ≥ 3,
taking advantage of a particularity of the quadratic scoring
rule. Our mechanism is the first peer prediction method that
does not rely on knowledge of the common prior to provide strict
incentive compatibility for any n ≥ 3. Moreover, and in contrast
to the original BTS, our mechanism is numerically robust and ex
post individually rational.
2011
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Jens Witkowski, Sven Seuken and David C. Parkes.
Incentive-Compatible Escrow Mechanisms.
In
Proceedings of the 25th AAAI Conference on Artificial Intelligence (AAAI 2011).
2011.
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The most prominent way to establish trust between buyers and sellers on online auction sites are reputation mechanisms. Two drawbacks of this approach are the reliance on the seller
being long-lived and the susceptibility to whitewashing. In this paper, we introduce so-called escrow mechanisms that avoid these problems by installing a trusted intermediary
which forwards the payment to the seller only if the buyer acknowledges that the good arrived in the promised condition.
We address the incentive issues that arise and design an escrow mechanism that is incentive compatible, efficient, interim individually rational and ex ante budget-balanced. In
contrast to previous work on trust and reputation, our approach does not rely on knowing the sellers' cost functions or the distribution of buyer valuations.
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Jens Witkowski.
Incentive-Compatible Trust Mechanisms (Extended Abstract).
In
Proceedings of the 25th AAAI Conference on Artificial Intelligence (AAAI 2011).
2011.
16th AAAI/SIGART Doctoral Consortium.
(PDF)
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Jens Witkowski.
Trust Mechanisms for Online Systems (Extended Abstract).
In
Proceedings of the 22nd International Joint Conference
on Artificial Intelligence (IJCAI 2011).
2011.
IJCAI 2011 Doctoral Consortium.
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Jens Witkowski and David C. Parkes.
Peer Prediction with Private Beliefs.
In
Proceedings of the 1st Workshop on Social Computing and User Generated Content (SC 2011).
2011.
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Reputation mechanisms at online opinion forums, such as Amazon
Reviews, elicit ratings from their users about the experiences with
products of unknown quality and critically rely on these ratings
being truthful. The peer prediction method by Miller, Resnick and
Zeckhauser is arguably the most prominent truthful feedback
mechanism in the literature. An obstacle with regard to its
application are the strong common knowledge assumptions. Especially
the commonly held prior belief about a product's quality, although
prevailing in economic theory, is too strict for this setting. Two
issues stand out in particular: first, that different buyers hold
different beliefs and, second, that the buyers' beliefs are often
unknown to the mechanism. In this paper, we develop an
incentive-compatible peer prediction mechanism for these reputation
settings where the buyers have private beliefs about the product's
inherent quality and the likelihood of a positive experience given a
particular quality. We show how to exploit the temporal structure
and truthfully elicit two reports: one before and one after the
buyer's experience with the product. The key idea is to infer the
experience from the direction of the belief change and to use this
direction as the event that another buyer is asked to predict.
2010
2009
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Jens Witkowski.
Eliciting Honest Reputation Feedback in a Markov Setting.
In
Proceedings of the 21th International Joint Conference
on Artificial Intelligence (IJCAI 2009).
2009.
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Recently, online reputation mechanisms have been proposed that
reward agents for honest feedback about products and services
with fixed quality. Many real-world settings, however, are
inherently dynamic. As an example, consider a web service that
wishes to publish the expected download speed of a file
mirrored on different server sites. In contrast to the models
of Miller, Resnick and Zeckhauser and of Jurca and Faltings,
the quality of the service (e.g., a server's available
bandwidth) changes over time and future agents are solely
interested in the present quality levels. We show that
hidden Markov models (HMM) provide natural generalizations of
these static models and design a payment scheme that elicits
honest reports from the agents after they have experienced the
quality of the service.
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Jens Witkowski.
Truthful Feedback for Reputation Mechanisms.
Diploma thesis,
Albert-Ludwigs-Universität,
Freiburg, Germany 2009.
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Reputation mechanisms such as those employed by Amazon and
eBay offer an effective way to prevent market failure in
online economies. However, most of these mechanisms assume
that the privately monitored transaction outcomes are honestly
reported. This clearly is a simplification since buyers may
have incentives to misreport. While it has been shown that the
truthful elicitation of these outcomes is feasible in settings
with pure adverse selection, i.e. with a purely
stochastic seller, we study whether honest feedback can
be elicited in settings with moral hazard, i.e. with a
strategic seller. For a pure moral hazard setting
motivated by the one at eBay, we find that there is no
feedback mechanism that makes honest reporting a best response
to truthful play by all other players. For a combined setting
with both adverse selection and moral hazard, however, we
retrieve a positive result and construct a payment scheme that
can be used as a "feedback plug-in" for reputation mechanisms.
2008
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Jens Witkowski.
Eliciting honest reputation feedback in a Markov setting.
Student project,
Albert-Ludwigs-Universität,
Freiburg, Germany 2008.
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