Radu Jurca DIP internal workshop, EPFL, 2005 Trust, Reputation
and Incentives in Online Environments Radu Jurca, Boi Faltings
[email protected]; [email protected] Artificial Intelligence
Laboratory Ecole Polytechnique Fdrale de Lausanne
liawww.epfl.ch/People/jurca
Slide 2
Radu Jurca Slide 2 DIP internal workshop, EPFL, 2005 Overview
Trust Reputation Mechanisms Signaling RM Sanctioning RM Other
Approaches Incentive Compatibility for probabilistic behavior for
strategic behavior Conclusions and Future Work
Slide 3
Radu Jurca Slide 3 DIP internal workshop, EPFL, 2005 On Trust
plays an essential role in our society subjective decision to rely
on somebody or something depends on: truster, trustee, and context
symmetric relation symmetry can be broken by interaction
protocols
Radu Jurca Slide 5 DIP internal workshop, EPFL, 2005 On
Reputation information support for trusting decisions in online
systems, often assumed to be objective information information
about past behavior Form of feedback? Feedback aggregation?
Slide 6
Radu Jurca Slide 6 DIP internal workshop, EPFL, 2005 Role of
Reputation informative (signaling) aggregated statistics overview
of capabilities collective intelligence saves search time
sanctioning incentives for honest behavior cheating becomes
economically unattractive
Slide 7
Radu Jurca Slide 7 DIP internal workshop, EPFL, 2005 Models of
Behavior (for Trustee) Probabilistic behavior fixed probabilities
no strategies SIGNALING Reputation Mechanisms Opportunistic
behavior economic model utility maximizing behavior SANCTIONING
Reputation Mechanism
Slide 8
Radu Jurca Slide 8 DIP internal workshop, EPFL, 2005 Existing
Reputation Mechanisms Trustee Behavior ProbabilisticOpportunistic
Type of Reputation Mechanism SignalingSanctioning Tools Social
Networks Probabilistic Estimation Game Theory Examples... ?
Slide 9
Radu Jurca Slide 9 DIP internal workshop, EPFL, 2005 Challenges
Correctness Obtain honest reports from RATIONAL trusters resistance
to collusion scalable robust against manipulation and failure
reliable
Slide 10
Radu Jurca Slide 10 DIP internal workshop, EPFL, 2005 Overview
You have seen: what is trust, reputation types of Reputation
Mechanisms You will see: Signaling Reputation Mechanisms
Sanctioning Reputation Mechanisms Other Approaches Incentive
Compatible RMs
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Radu Jurca DIP internal workshop, EPFL, 2005 Signaling
Reputation Mechanisms
Slide 12
Radu Jurca Slide 12 DIP internal workshop, EPFL, 2005 Social
Networks and Probabilistic Estimates T(G|O) = f( T(G|B),T(B|O),
T(G|R),T(R|O) ) Type B: C:10% D:90% Type R: C:90% D:10% Reputation
Mechanism Seller Group of buyers Reports ? B or R ? O R G B
Slide 13
Radu Jurca DIP internal workshop, EPFL, 2005 Sanctioning
Reputation Mechanisms
Slide 14
Radu Jurca Slide 14 DIP internal workshop, EPFL, 2005
Reputation Mechanism Design Reputation Mechanism Trusting Agent
Trusted Agent Mechanism Designer Value of Reputation Semantics of
Reputation & Protocol Implementation Reputation Information
& Reputation Reports Reputation Information & Reputation
Reports RULES Trust Decision Maximize the gain of the Trusting
Agent given the available data (i.e. the REPUTATION): - scale the
value of the transaction - decide whether or not to trade
Reputation has a direct influence on future gains. Value of R: How
much more can a Trusted Agent gain by starting with reputation R?
Value for a negative or positive feedback report! EFFECTIVE: - the
gain obtained from cheating is smaller than the value of a negative
report INCENTIVE COMPATIBLE COLLUSION RESISTENCE SCALABLE
RELIABLE
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Radu Jurca Slide 15 DIP internal workshop, EPFL, 2005
Dellarocas 2004 one seller, the same good, multiple buyers seller
can cooperate or defect binary feedback aggregated into reputation
the selling price depends on the reputation the mechanism has a
Nash equilibrium such that for every acceptable value of the
reputation, there is a unique probability that the seller will
cooperate
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Radu Jurca Slide 16 DIP internal workshop, EPFL, 2005 Pros and
Cons clear reputation semantics designer can choose the desired
outcome complicated to analyze human rationality?
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Radu Jurca Slide 17 DIP internal workshop, EPFL, 2005 Different
Approaches instead of having truthful reputation reports, make the
trustee truthfully confess his intended quality of service or
trustworthiness
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Radu Jurca Slide 18 DIP internal workshop, EPFL, 2005
Dellarocas 2003: Goodwill Hunting One seller sells goods of
different quality levels Seller has goodwill Goodwill is updated by
the center The center modifies the transaction prices in order to
compensate the goodwill the mechanism provides week incentives for
the seller to announce the true qualities
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Radu Jurca Slide 19 DIP internal workshop, EPFL, 2005 Braynov
and Sandholm 2001 the seller announces his trustworthiness and the
buyer than sets the quantity of the transaction there exists a
function q(t) such that the seller truthfully announces his
trustworthiness marginal cost function of the seller needs to be
known, and has to satisfy certain properties
Slide 20
Radu Jurca DIP internal workshop, EPFL, 2005 Incentive
Compatibility
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Radu Jurca Slide 21 DIP internal workshop, EPFL, 2005 Incentive
Compatibility reputation should be shared Reputation = Information
=> it is not for free no verification authorities or TTPs. The
only source of information = feedback from other agents Reputation
Mechanisms have to be Incentive Compatible rational agents will
truthfully share the reputation information
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Radu Jurca Slide 22 DIP internal workshop, EPFL, 2005 Incentive
Compatibility generating feedback is easy! pay for a report
generating TRUE feedback is difficult! different solutions for
different classes of behavior (of the Trusted Agent) Probabilistic
Behavior Opportunistic Behavior
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Radu Jurca Slide 23 DIP internal workshop, EPFL, 2005
Probabilistic Behavior strong correlation between present and
future behavior. incentive compatibility is based on side payments
side payments depend on future, unknown reports it is possible to
design payment rules which make it in the best interest of the
agent to report the truth. (Miller, Resnick, Zeckhauser:2003)
(Jurca,Faltings:2003)
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Radu Jurca Slide 24 DIP internal workshop, EPFL, 2005 Miller,
Resnick, Zeckhauser:2003 side payments computed by scoring rules
scoring rules elicit correct estimates can be used for reputation
mechanisms when the trustee has one among a countable set of types
E.g. logarithmic scoring rule Pay_i = log(Pr[report_j |
announcement_i])
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Radu Jurca Slide 25 DIP internal workshop, EPFL, 2005 Miller,
Resnick, Zeckhauser:2003 (2) GB +0.90.2 -0.10.8 Types Feedback
Priors: GB+- 0.5 0.550.45 0.820.180.770.23 Posteriors if updated
with +: 0.110.890.280.72 Posteriors if updated with -: E[Pay] =
Pr[+|+]*log(Pr[+|+]) + Pr[-|+]*log(Pr[-|+]) = -0.53 Truster
perceives + and announces +: E[Pay] = Pr[+|+]*log(Pr[+|-]) +
Pr[-|+]*log(Pr[-|-]) = -1.05 Truster perceives + and announces
-:
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Radu Jurca Slide 26 DIP internal workshop, EPFL, 2005 Miller,
Resnick, Zeckhauser:2003 (3) can be made budget balanced can
account for reporting costs precise semantics for reputation prior
probabilities need to be common knowledge.
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Radu Jurca Slide 27 DIP internal workshop, EPFL, 2005 Jurca and
Faltings 2003 R-Agents buy and sell reputation information Simple
Payment Rule: pay a report about B only if the next report about B
has the same value Cryptographic measures make the mechanism
reliable and safe.
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Radu Jurca Slide 28 DIP internal workshop, EPFL, 2005 Jurca and
Faltings 2003 (2)
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Radu Jurca Slide 29 DIP internal workshop, EPFL, 2005 Jurca and
Faltings 2003 (3) no a priori common knowledge required mechanism
is trustworthy reputation is tied to identity JADE implementation
exists limited set of acceptable behavior models
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Radu Jurca Slide 30 DIP internal workshop, EPFL, 2005
Opportunistic Behavior agents have the freedom to strategically
chose their actions in every transaction EXAMPLE: a seller can work
hard in the beginning, but then cheats from time to time a buyer
who gets cheated does not have the incentive to tell the truth
because the next report will most likely be positive future reports
are not correlated with the present one => no payment rule can
guarantee incentive compatibility
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Radu Jurca Slide 31 DIP internal workshop, EPFL, 2005
Opportunistic Behavior (2) it is possible to have an IC mechanisms
when reporting agents have a repeated presence in the market
INTUITION: Sellers will not cheat on truthful reporters because it
is not economical. A buyer has the incentive to develop a
reputation for reporting the truth because she will benefit from
cooperative trade in the future. EXAMPLE: a simplified setting in
which one hotel can provide or not the promised accommodation
quality the occupancy of the hotel depends on its reputation
clients can come back to the same hotel
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Radu Jurca Slide 32 DIP internal workshop, EPFL, 2005 CONFESS
An IC Mechanism both agents submit feedback DECISION: if Hotel
admits having cheated record R- if both agents report positive
feedback, record R+ if Hotel reports +, and Client reports -,
record R- and fine both agents (Jurca,Faltings:2004) prove that the
mechanism is incentive-compatible, and derive an upper bound on the
percentage of false reports recorded Hotel Client Negative Report +
+ - - Negative Report Both agents fined Positive Report
Slide 33
Radu Jurca Slide 33 DIP internal workshop, EPFL, 2005 Future
Work collusion resistance reputation mechanisms for opportunistic
agents who have different characteristics study the influence of
mistakes and irrational behavior robustness and scalability in
fully decentralized markets (e.g. P2P environments)
