incentive y. liu anincentivemechanismdesignedfor e ... · incentive mechanism y. liu introduction...

Incentive

Mechanism

Y. Liu

Introduction

Model

Analysis

Initialization

Simulation

Summary

An Incentive Mechanism Designed forE-Marketplaces with Limited Inventory

Yuan Liu, Jie zhang

School of Computer Engineering

Nanyang Technological University

August 03, 2013

Y. Liu, SCE, NTU BCSI2013 Incentive Mechanism page 1 of 23

Incentive

Mechanism

Y. Liu

Introduction

Model

Analysis

Initialization

Simulation

Summary

Overview

1 Background and motivation

2 Our incentive mechanism

3 System analysis

4 Initialization

5 Experimental validation

6 Conclusions


Incentive

Mechanism

Y. Liu

Introduction

Model

Analysis

Initialization

Simulation

Summary

Introduction

Reputation system

Modeling sellers’ honesty based on buyers’ ratings

Problems:

Unfair rating problemRe-entry problem

Existing Mechanisms to Address the Unfair Rating Problem

Side-payment mechanism [Jurca et.al]

Reward maximal side payment for truthful ratings

Trust-based approach [Zhang et.al]

Honest buyers can propagate seller reputation moreeffectively, then be offered with lower prices

Trust-revelation [Sviatoslav et.al]

Truthfully revelation seller trustworthiness can bring sellersmaximal utility


Incentive

Mechanism

Y. Liu

Introduction

Model

Analysis

Initialization

Simulation

Summary

E-marketplaces with Limited Inventory

One common assumption: there is unlimited products providedby sellers, i.e. no competition between buyers

E-marketplaces with Limited Inventory (EMLI)

Definition: Given a set of sellers S each of whom providesthe same product and a set of buyers B each of whomdemands one piece of the products. An e-marketplacesatisfying the condition, |S| < |B|, is called ane-marketplace with limited inventory.

Examples:

Second-hand textbooksHotel booking in peak seasonsDoctor booking system

New challenges in promoting buyer and seller honesty


Incentive

Mechanism

Y. Liu

Introduction

Model

Analysis

Initialization

Simulation

Summary

New Challenges

Buyers have incentives to be dishonest

‘positive’ rating for a good seller ×‘negative’ rating for a good seller

√

‘negative’ rating for a bad seller ×‘positive’ rating for a bad seller

√

Sellers have incentives to be dishonest

Honest delivery → increased cost ×Dishonest delivery → decreased cost + sold out

√


Incentive

Mechanism

Y. Liu

Introduction

Model

Analysis

Initialization

Simulation

Summary

The Objective of This Paper

We have done

Design an incentivemechanism to promotebuyer and seller honestyfor the e-marketplaceswith limited inventory

Modeling buyer honestyModeling seller honestyPricing algorithmAllocation algorithm

Initialization the system

Initial honesty valuesMembership fees

Important Achievements

Buyers have incentives tobe honest

Sellers have incentives tobe honest

Buyers and sellers have noincentive to re-enter

Experimental validation

Static simulationDynamic SimulationRe-entry simulationComparison Results


Incentive

Mechanism

Y. Liu

Introduction

Model

Analysis

Initialization

Simulation

Summary

Our Incentive Mechanism

The components of the incentive mechanism

Modeling

Buyer Honesty

Modeling

Seller Honesty

Allocation

Algorithm

Pricing

Algorithm

Buyer Honesty

Promoted

Seller Honesty

Promoted

SellerReputation

BuyerRatings

Transaction

BuyerHonesty

+

Buyer ScoreSeller

Reputation

Figure: The work flow of our incentive mechanismY. Liu, SCE, NTU BCSI2013 Incentive Mechanism page 7 of 23

Incentive

Mechanism

Y. Liu

Introduction

Model

Analysis

Initialization

Simulation

Summary

We analyze the system in three aspects:

Individual rationality

Incentive compatibility

Social welfare


Incentive

Mechanism

Y. Liu

Introduction

Model

Analysis

Initialization

Simulation

Summary

Individual Rationality

Buyer Individual Rationality

Prop 1: Truthful ratings can maximize buyer scores.

Prop 2: Buyer utility is positive from a transaction if Rs > R0.

Prop 3: The upper bound of the price for buyers is C

R0.

Seller Individual Rationality

Prop 4: Utility is positive when seller reputation Rs > R0.

Prop 5: The lower bound of the price for sellers is C

δ.


Incentive

Mechanism

Y. Liu

Introduction

Model

Analysis

Initialization

Simulation

Summary

Incentive Compatibility

Buyer Incentives

Prop 6: More utility can be gained from a transaction if Rs ishigher.

Prop 7: The utility in providing truthful ratings is no less thanproviding untruthful ratings.

Seller Incentives

Prop 8: Sellers have incentive to improve their honesty bydelivering promised products.

Both buyers and sellers have incentive to be honest.


Incentive

Mechanism

Y. Liu

Introduction

Model

Analysis

Initialization

Simulation

Summary

Social Welfare

Definition of Social Welfare

W(Rs, Rb) = (Us(Rs)+Ub(Rs))1

1−Rb

= Rs(Vsb −C)

1

1−Rb

,

(1)

Prop 9: The proposed incentive mechanism can increase thetotal social welfare as defined in Equation (1).

Prop 10: The social welfare of the proposed system is no lessthan that of the free-trading e-marketplace.


Incentive

Mechanism

Y. Liu

Introduction

Model

Analysis

Initialization

Simulation

Summary

Initialization

Rb(0): 0

Rs(0): δ

To avoid re-entry, we assign membership feeM = N0(M1 +M2) = N0C where M1 = (1−R0)C,M2 = R0C, and N0 is determined by the Chernoff BoundTheorem based on the error rate.

When sellers leave the system, Mr will be returned tosellers

Mr=M(Rs)=

N0(M1 +M2) Rs > δ,

(Rs−R0

δ−R0)2N0M1+N0M2 R0≤ Rs≤δ,

0 Rs < R0.

(2)


Incentive

Mechanism

Y. Liu

Introduction

Model

Analysis

Initialization

Simulation

Summary

Experimental Validation

Settings

R0 = 0.6, δ = 0.85

C = 1, V∗ = 2, V ∗ = 2.5

η = 0.1, α = 0.5

Honest seller: delivering promised productsDishonest seller: delivering 50% quality productsHonest buyer: ‘1’ for honest transaction; ‘0.5’ fordishonest transactionDishonest buyer: ‘0.5’ for honest transaction; ‘1’ forhonest transaction

Bootstrap our system: 80 honest buyers and 40 sellers for1000 transactions


Incentive

Mechanism

Y. Liu

Introduction

Model

Analysis

Initialization

Simulation

Summary

Experiments

Experiments

Static experiments:

another 320 buyersconducting 9000 transactions

Dynamic experiments:

5 new sellers and 50 new buyers join in each 100transactionsconducting 400 transactions

Reentry experiments:

simulate the profit of sellers with reentry


Incentive

Mechanism

Y. Liu

Introduction

Model

Analysis

Initialization

Simulation

Summary

Static Experimental Results

Seller’s Honesty and Seller’s Profit

0.5

0.6

0.7

0.8

0.9

1

0 4 8 12 16 20 24 28 32 36 40 0.75

0.8

0.85

0.9

0.95

1

Selle

r H

ones

ty

Selle

r R

eput

atio

nSeller ID

(a)

HonestyReputation

0.5

0.6

0.7

0.8

0.9

1

0 4 8 12 16 20 24 28 32 36 40 0.1

0.15

0.2

0.25

0.3

Selle

r H

ones

ty

Selle

r Pr

ofit

Seller ID

(b)

HonestyProfit

Figure: The relationship between probability of sellers in behavinghonestly and (a) seller reputation, (b) average seller profit in thestatic setting


Incentive

Mechanism

Y. Liu

Introduction

Model

Analysis

Initialization

Simulation

Summary

Static Experimental Results (2)

Buyer’s Honesty and Buyer’s Utility

0.5

0.6

0.7

0.8

0.9

1

0 40 80 120 160 200 240 280 320 360 400 0.95

0.96

0.97

0.98

0.99

1

Buy

er H

ones

ty

Buy

er S

core

Buyer ID

(a)

HonestyScore

0.5

0.6

0.7

0.8

0.9

1

0 40 80 120 160 200 240 280 320 360 400 0⋅100

1⋅103

2⋅103

3⋅103

4⋅103

5⋅103

Buy

er H

ones

ty

Buy

er U

tility

Buyer ID

(b)

HonestyUtility

Figure: The relationship between probability of buyers in behavinghonestly and (a) buyer scores, (b) buyer total utility in the staticsetting


Incentive

Mechanism

Y. Liu

Introduction

Model

Analysis

Initialization

Simulation

Summary

Dynamic Experimental Results

Seller’s Honesty and Seller’s Profit

0.5

0.6

0.7

0.8

0.9

1

0 4 8 12 16 20 24 28 32 36 40 44 48 52 56 60 0.75

0.8

0.85

0.9

0.95

1

Selle

r H

ones

ty

Selle

r R

eput

atio

nSeller ID

(a)

HonestyReputation

0.5

0.6

0.7

0.8

0.9

1

0 4 8 12 16 20 24 28 32 36 40 44 48 52 56 60 0.1

0.15

0.2

0.25

0.3

Selle

r H

ones

ty

Selle

r Pr

ofit

Seller ID

(b)

HonestyProfit

Figure: The relationship between probability of sellers in behavinghonestly and (a) seller reputation, (b) average seller profit in thedynamic setting


Incentive

Mechanism

Y. Liu

Introduction

Model

Analysis

Initialization

Simulation

Summary

Dynamic Experimental Results (2)

Buyer’s Honesty and Buyer’s Utility

0.5

0.6

0.7

0.8

0.9

1

0 60 120 180 240 300 360 420 480 540 600 0.93

0.94

0.95

0.96

0.97

0.98

0.99

1

Buy

er H

ones

ty

Buy

er S

core

Buyer ID

(a)

HonestyScore

0.5

0.6

0.7

0.8

0.9

1

0 60 120 180 240 300 360 420 480 540 600 0⋅100

1⋅103

2⋅103

3⋅103

4⋅103

5⋅103

6⋅103

Buy

er H

ones

ty

Buy

er U

tility

Buyer ID

(b)

HonestyUtility

Figure: The relationship between probability of buyers in behavinghonestly and (a) buyer score, (b) buyer total utility in the dynamicsetting


Incentive

Mechanism

Y. Liu

Introduction

Model

Analysis

Initialization

Simulation

Summary

Reentry Experimental Results

Seller Reentry

-2

0

2

4

6

8

10

12

0 4 8 12 16 20 24 28 32 36 40

Tota

l Pro

fit o

f Sel

lers

No. of Period

(a)Rep=1

Rep=0.7Rep=0.55

-2

0

2

4

6

8

10

12

0 4 8 12 16 20 24 28 32 36 40

Tota

l Pro

fit o

f Sel

lers

No. of Period

(b)

ReentryPoint

Rep=1Rep=0.7

Rep=0.55

-4

-2

0

2

4

6

8

10

12

0 4 8 12 16 20 24 28 32 36 40

Tota

l Pro

fit o

f Sel

lers

No. of Period

(c)

ReentryPoint

Rep=1Rep=0.7

Rep=0.55

-2

-1.5

-1

-0.5

0

0.5

0.5 0.6 0.7 0.8 0.9 1

Prof

it Lo

ss o

f Sel

lers

Reputation

(d)

δ

Figure: Re-entry scenarios:(a) sellers’ total profit without re-entry and without membership fee,(b) sellers’ total profit with re-entry but without membership fee,(c) sellers’ total profit with re-entry and with membership fee,(d) profit loss of sellers by comparing (c) and (a)


Incentive

Mechanism

Y. Liu

Introduction

Model

Analysis

Initialization

Simulation

Summary

Comparison Results

Comparing Our Model with BRS in EMUL

0

1

2

3

4

5

6

4 8 12 16 20 24 28 32 36 40

Avg

Sel

ler

Prof

it

Seller ID

(a)

Honest Dishonest

Our MechanismSide-payment

0.8

0.9

1

1.1

1.2

1.3

1.4

1.5

40 80 120 160 200 240 280 320 360 400

Avg

Buy

er U

tility

Buyer ID

(b)

Honest Dishonest


Figure: The incentive comparison between our mechanism and theside-payment mechanism (a) seller incentive, (b) buyer incentive inEMUI


Incentive

Mechanism

Y. Liu

Introduction

Model

Analysis

Initialization

Simulation

Summary

Comparison Results (2)

Comparing Our Model with BRS in EMLI

0

0.2

0.4

0.6

0.8

1

4 8 12 16 20 24 28 32 36 40

Avg

Sel

ler

Prof

it

Seller ID

(a)

Honest Dishonest


0

0.1

0.2

0.3

40 80 120 160 200 240 280 320 360 400

Avg

Buy

er U

tility

Buyer ID

(b)

Honest Dishonest


Figure: The incentive comparison between our mechanism and theside-payment mechanism (a) seller incentive, (b) buyer incentive inthe EMLI


Incentive

Mechanism

Y. Liu

Introduction

Model

Analysis

Initialization

Simulation

Summary

Conclusions

Conclusions

We proposed an incentive mechanism to

Promote buyer honestyPromote seller honesty

Theoretical analysis

Buyers have incentives to provide honest ratingsSellers have incentives to deliver promised productsThe social welfare is improved

Experimental analysis

Buyers gain more utility from honest ratingsSellers gain more profit from honest deliveryBoth buyers and sellers have no incentive to reenterThe proposed model perform better than BRS in bothEMLI and EMUL


Incentive

Mechanism

Y. Liu

Introduction

Model

Analysis

Initialization

Simulation

Summary

THANK YOU


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