Download presentation

Presentation is loading. Please wait.

Published byFernando Maule Modified over 2 years ago

1
Enforcing Truthful-Rating Equilibria in Electronic Marketplaces T. G. Papaioannou and G. D. Stamoulis Networks Economics & Services Lab, Dept.of Informatics, Athens Univ. of Economics & Business (AUEB) http://nes.aueb.gr “ Towards a Science of Networks” Workshop Sounion, Greece – September 1, 2006 Work partly funded by EuroNGI Network of Excellence (IST-2003-507613)

2
2 Overview Problem definition and related work The basic model and its analysis fixed punishments The extended model and its analysis reputation-based punishments Conclusions – Recent and Future Work

3
3 Reputation in Peer-to-Peer Systems Reputation reveals hidden information about peers Is only effective with reputation-based policies [1] “Provider Selection” and “Contention Resolution” policies But, reputation is vulnerable to false/malicious ratings Collect ratings on each transaction from both parties and punish both in case of disagreement [2] At least one of them is lying Punishment is not monetary bans participation for some time [1] “Reputation-based policies that provide the right incentives in peer-to-peer environments”, Computer Networks, vol. 50, no. 4, March 2006. [2] “An Incentives' Mechanism Promoting Truthful Feedback in Peer-to-Peer Systems”, GP2PC’05.

4
4 The New Context Reputation is now studied in electronic marketplaces where participants act both as providers and as clients competitively, so as to maximize their market share Applications: exchanging of vinyl records among collectors, software modules among programmers, news among agencies, etc. Provider selection is based on reputation Malicious rating offers future competitive advantage

5
5 Objective & Contribution Objective: Provide incentives for truthful ratings by participants in the new context Contribution: Analyzed the dynamics of fixed monetary punishments Derived necessary conditions for stable truthful rating equilibrium Customized punishments w.r.t. reputation to limit social unfairness

6
6 Related Work: Monetary-Penalty Approaches Miller, Resnick, Zeckhauser: Truthful rating is a Nash equilibrium for clients if certain penalties are imposed to them upon evidence of lying Jurca, Faltings: Side-payments imposed upon evidence of lying. Clients are truthful and do not act as providers Dellarocas: Penalty to provider to compensate payoff gains from offering lower quality than promised. Nash equilibrium for truthful clients

7
7 Innovation in this Research Dual role of participants Reputation-based competition and its impact on incentives for reporting truthful ratings Stability analysis of truthful-rating Nash equilibrium enforced by each mechanism Tailored reputation-based punishments

8
8 The Basic Model

9
9 Providers and Clients E-marketplace with N participants N is either fixed or mean number of participants with geometrically distributed lifetimes Time is partitioned in rounds During a round: Each participant acts: –either as a provider with probability q –or as a client with probability 1-q One client is selected at random to be served

10
10 Selecting a Provider Reputation-based policy: A clients selects the provider to associate with in a probabilistically fair manner w.r.t. the reputation of the latter A provider i is selected during a round with probability proportional to his rank Each participant has a probability a i to provide a service instance successfully, with satisfactory quality a i is private information, estimated by reputation –e.g. percentage of past successful provisions

11
11 Costs, Payments, Ratings A successfully provided service instance: Offers fixed utility u to the client Demands costly effort v by the provider Costs b to the client A pre-payment p·b is paid for each service to balance the risks Both transacted parties submit a binary rating of the service provided Upon agreement, the client pays (1-p)·b and the vote is registered for updating the provider’s reputation Upon disagreement a fixed punishment c is imposed to both

12
12 Single-Transaction Game Two sub-games depending on the outcome of the service provision: success or failure Set of Reporting strategies: S = {Witness, Lie, Duck} Impact of agreed rating to future payoffs of participants A positive rating results in payoff impact w p > 0 for the provider and -w c < 0 for the client A negative rating results in payoff impact -w p 0 for the client Payoff impacts are taken independent of current reputation ~ ~

13
u-b-w c b-v+w p u-p·b-c p·b-v-c u-p·b-c p·b-v-c u-b-c b-v-c u-p·b+w c ’ p·b-v-w p ’ u-p·b-c p·b-v-c u-b-c b-v-c u-p·b-c p·b-v-c u-p·b p·b-v -p·b+w c ’ p·b-w p ’ -p·b-c p·b-c -p·b-c p·b-c -p·b-c p·b-c -p·b-w c p·b+w p -p·b-c p·b-c -p·b-c p·b-c -p·b-c p·b-c -p·b p·b client provider success failure aiai 1-a i Witness Lie Duck Witness Lie Duck ~ ~ ~ ~

14
14 Truthful-Rating Equilibrium under the Basic Model

15
15 Truthful Nash Equilibrium Derive conditions for disagreement punishment c so that truthful rating is a Nash equilibrium in both sub- games Disagreement may rationally arise only in two cases Success: provider Witness and client Lie or Duck Failure: client Witness and provider Lie or Duck Witness is best response to itself when c > (1-p)·b+w c and c > w p Does this equilibrium indeed arise ? Is it stable ? ~

16
16 Evolutionary Stability Evolutionary Stable Strategy (ESS): 1. Nash equilibrium 2. Is a better reply to any mutant strategy than the latter is to itself Strict Nash equilibrium of the asymmetric game ESS of its symmetric version Evolutionary dynamics for strategy s with payoff π s played by a population fraction x s :

17
17 Stable Truthful Rating (x 1, x 2, x 3 ) are the population fractions of providers that play Witness, Lie, Duck in the success sub-game (y 1, y 2, y 3 ) are these fractions for clients Basin of attraction of truthful-rating equilibrium: Region for population mix that ultimately leads all participants to play Witness (x 1, x 2, x 3 ) = (1,0,0) and (y 1, y 2, y 3 ) = (1,0,0)

18
18 The Basin of Attraction of Truthful Rating Success sub-game X Y

19
19 The Basin of Attraction of Truthful Rating Failure sub-game X’ Y ’

20
20 The Extended Model

21
21 Differences from Basic Model Disagreement punishment is not fixed Depends on the transacted participant’s: –Rank, i.e. reputation –Role which are public information Payoff impacts of a vote w p, -w c, w p, -w c are not taken fixed Calculated explicitly ~~

22
22 Innovative Reputation Metric Beta reputation metric: Results to time-dependent impact of a single vote to rank values of transacted parties Solution: An innovative reputation metric Still provides the right incentives Now, rank impacts are not time-dependent; e.g.

23
23 Reputation-based Punishments Derived conditions for disagreement punishments that enforce the truthful-rating equilibrium Outline of Proof Necessary and sufficient condition: A single-round deviation from truthful rating at round t is not beneficial.

24
24 Some Algebra Expected payoff for a participant i at round t : After a non-truthful rating at round t, future ranks of participant i are Calculated explicitly, separately for provider and for client ranksuccess probability mean success probability payoff as client payoff as provider

25
25 The Conditions on Punishments Provider’s punishment: Client’s punishment: Since N is large, f p is approximated by a simple formula This is bounded from above and below

26
26 Numerical Example N=50, q=0.4, p=0.2, b=2, u=2.5, v=0.5, =0.6, =0.9 R Disagreement Payoff Impact w p (R), w c (R) Provider Client Punishment for Disagreement R ~

27
27 Social Benefit Estimation

28
28 Social Loss Disagreement punishment is unfairly induced to one of the transacted participants social loss When punishment is fixed, c > q w p + (1-q)[(1-p)b+w c ] The maximum payoff impacts w p, w c have to be assumed Thus, unfairness is induced for all the non-highest ranked participants greater social loss Reputation-based punishments eliminate this unfairness! ~ ~

29
29 Numerical Example Normal distribution of ranks with (μ, σ)=(1, 0.5) Social benefit: Ratio of average reputation-based punishment over fixed punishment for various mean-reputation values of the population Population’s mean-reputation Reputation-based Punishment Fixed Punishment

30
30 Concluding Remarks

31
31 Summary of our Contribution Proposed a simple mechanism that provides incentives for truthful rating in an interesting context of an e-marketplace Reputation-based competition Dual role of participants Derived conditions on the effectiveness of such a mechanism with fixed punishments Stability analysis of truthful-rating Nash equilibrium Derived tailored reputation-based punishments Significant reduction of social loss

32
32 Recent and Future Work Employ different fixed punishments for provider and client Consider the success probability of each participant as a strategic parameter, rather than as being fixed Derive upper bound for the social loss reduction with reputation-based disagreement punishments Explore stability conditions for truthful equilibrium with reputation-based disagreement punishments

Similar presentations

OK

Learning dynamics,genetic algorithms,and corporate takeovers Thomas H. Noe,Lynn Pi.

Learning dynamics,genetic algorithms,and corporate takeovers Thomas H. Noe,Lynn Pi.

© 2017 SlidePlayer.com Inc.

All rights reserved.

Ads by Google

Ppt on natural and artificial satellites pictures Ppt on hostel management system in c Ppt on time management training Ppt on ram and rom hyper Ppt on articles of association definition Ppt on forest in india Ppt on credit policy in banks Ppt on natural disasters for class 9 Ppt on power system topics Ppt on different mode of transport