1

Dynamic Pricing of Information Goods

Joint work with:Gabi Koifman, Avigdor GalTechnion

Onn ShehoryIBM Haifa Research Labs

2

Motivation

Rapid growth in electronic commerce The information economy vision (Kephart et al.)

Agents accumulate knowledge, stored in databases

Agents can benefit from trading database tuples

No mechanism for such trade

3

Problem Statement

A mechanism for negotiating database-based information goods requires: – Correctly matching of attributes of database goods– Pricing of (DB-based) information goods

Bob’s Agent Alice’s

Agent

Domain:Stocks Domain:Stocks

ID Quote dateIBM 100 01/01/2002IBM 90 01/02/2002IBM 120 01/03/2002IBM 100 01/04/2002

stockID stockQuote dateIBM 100 01/01/2002IBM 90 01/02/2002

I can sell records to

make profit

I need more information

NOW. Willing to spend 50$ for it.

4

(DB-based) Information goods market vs. traditional market

Negligible marginal cost Uniqueness Pricing Experience goods (Advertising) Delivery Schema/tuple ambiguity

5

Compatibility Evaluation

DB information goods compatibility evaluation can be reduced to the schema mapping problem

A mapping F from S to S’ is a set of |S| pairs (a, a’), a S, a’ S’ {null} and S’=F (S)

μatt(a,a’) is the similarity measure of a, a’

μF is computed based on all μatt in F Utility is based on μF

6

Buyer’s Anxiousness Level

Assumption: willingness to pay is proportional to buyer’s anxiousness

A seller can perform price discrimination across consumers with different anxiousness level

Why should a buyer expose its true anxiousness level?

When discriminating based on TTD (Time To Deliver), learning anxiousness is enabled

(we use Bayesian learning)

7

Market Trends

training period

frame

periodtraining period

frameframe

period

Calc: average supply

Calc: average personal demand

set: reference supply\demand levels

Calc: current supply\demand levels

Re-calc: average supply

Re-calc: average personal demand

Re-set: reference supply\demand levels

8

Utility Evaluation

Distance(seller, buyer) = number of tuples that exist in the seller’s database and not in the buyer’s database

If (distance (seller, buyer)> ) then

proceed with negotiation Computing Distance() is problematic

– Database comparison, or– Zero-knowledge mechanism– Relief: can approximate via statistical measures

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Pricing Policies

Derivative-Follower (DF) Trial and Error (TA) Personalized Pricing (PP) Market Based Personal Pricing (MBPP)

Posted pricing – DF,TA Price discrimination – PP,MBPP Negotiation based pricing – PP,MBPP

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Negotiation Participants

DB Exchange agent – Trusted third party – Receives ads, publishes to subscribers

Players: buyers and sellers – Initial database – Buyer: maximize (number of distinct tuples),s.t min(cost)– Seller: maximize (profit)

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Interaction diagram

Agent 1 Agent 2DBE

Transfer GoodsCloser

Price Negotiation

CounterOffer

CounterOffer

CloseDeal

CloseDeal

TerminateNegotiation

TerminateNegotiation

Seller ProcessOffer Buyer Process

Offer

Market trends learning

AL learning

Utility Evaluation

RequestForDistanc

DistanceReplyCalc Distance(2,1)

Negotiation Model

Contact

RequestToPublishPublishingSeller

WillingToNegotiate

InitialOffer

Compatibility Evaluation

OntoBuilder

μ>T

SafeSignsReplyForQueries

RequestForQueries

Schema-mapping learning μ>T

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Simulation System

Java language – JMS on J2EE. MS-access database JMS messaging

15 agentsJ2ee serverdatabase

15 agentsJ2ee server

DBE

1:1 service

15 playersJ2ee serverdatabase

15 playersJ2ee serverDBE agent

1:1 service

15 agentsJ2ee serverdatabase

15 agentsJ2ee server

DBE

1:1 service

15 playersJ2ee serverdatabase

15 playersJ2ee serverDBE agent

1:1 service

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Simulation Participants

Buyers: Anxiousness level Max budget for transaction Distance threshold (0)

Sellers: Current price list Probabilities for anxiousness level

distribution Assumed supply Assumed demand

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Pricing Policies Evaluation:

System profit /volume Equilibrium

Market settings: Non-competitive market Competitive market

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System Profit

System Volume

0

50

100

150

200

250

300

Market BasedPricing

PersonalizedPricing

DerivativeFollower

Trial And Error

Non-competitve competitive

Derivativefollower

Trial andError

PersonalizedPricing

MarketBased Pricing

System Profit

01000

2000300040005000

60007000

Market BasedPricing

PersonalizedPricing

DerivativeFollower

Trial AndError

Non-competitve competitive

Derivativefollower

Trial andError

PersonalizedPricing

MarketBased Pricing

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Equilibrium

deviation policy deviative agent 29 PP1 MBPP 150.22 125.961 TA 23.47 127.731 DF 26.53 112.35

PP agent should deviate to MBPP

MBPP agent should not deviate

deviation policy deviative agent 29 MBPP1 PP 70.02 118.111 TA 20.09 106.521 DF 25.62 116.52

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Conclusions

We provide mechanism for trading databased-based information goods

Pricing policies that allow negotiation and personalization, perform better than (known in the art) posted pricing

Market based personalized pricing policy performs better than personalized pricing, in terms of stability

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The End

19

Backup Slides

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Related Work

Pricing Information Goods– (Varian) price discrimination: an issue when willingness to

pay varies across consumers. Need to: Determine the consumer's willingness to pay Prevent “black market”

Information Economy and Software Agents– (Kephart et al.) The vision– Agent: faster, but less intelligent and flexible

– Effects on Global Economy Multiagent Negotiation

– Protocol, objects, reasoning model (Jennings et al.) Multiagent Learning

– Bayesian learning in negotiation – Zeng and Sycara

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Future Work

Support buyers that wish to build a database from an initial empty tuples set.

Situations for compatibility that also use auxiliary information.

Suggest techniques that allow a fully automated algorithm.

Additional pricing policies. Suggest a secure algorithm for distance(a,b), with no

use of third trusted party. Allow the buyer to choose a bidding policy that

maximizes its utility under specific market settings.

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Database-based Information Goods Compatibility Evaluation

Imprecision Mapping Effectiveness Mapping Cost

Evaluation Methodology and Results

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Compatibility Evaluation (1) :Mapping Imprecision

Evaluation Methodology and Results

Imprecision improvement Using SafeSigns

with insertion control

no change14.2%

no change (zero

imprecision)21.2%

improved50.8%

not improved13.7%

improved

no change

no change (zero imprecision)

not improved

Imprecision improvement using SafeSigns algorithm

improved40.2%

no change (zero

imprecision)21.4%

not improved29.8%

no change8.5%

improved

no change

no change (zero imprecision)

not improved

Improved40.2%

No change8.5%

NotImprove

d29.8%

No Change)0 imprecision(

21.4%No change

14.2%

Not Improved13.7

Improved50.8%

No Change)0 imprecision(

21.2%

Using SafeSigns ability to generate 0-imprecision mappings was doubled!!!

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Compatibility Evaluation (2) :

Mapping Effectiveness

Mapping EffectivenessEncoutering a specific seller - No Learning

R2 = 0.9824

0.50.520.540.560.580.6

0.620.640.66

1 2 3 4 5 6 7 8 9 10buyer's i encouter with a specific seller

P(s

uff

icie

nt

map

pin

g)

Mapping EffectivenessEncoutering a specific seller

R2 = 0.9806

0.8

0.85

0.9

0.95

1 2 3 4 5 6 7 8 9 10buyer's i encouter with a specific seller

P(s

uff

icie

nt

ma

pp

ing

)P(sufficient mapping)Log. (P(sufficient mapping))

Evaluation Methodology and Results

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Compatibility Evaluation (3) :

Mapping Cost

Learning Curve no schema-matching learning

010203040506070

1 1001 2001 3001 4001 5001 6001 7001No. Encounter

Nu

m Q

uer

ies

Learning curveAll encouters are considered

0

10

20

30

40

50

1 1001 2001 3001 4001No. encouter

Nu

m Q

uer

ies

number queries

Log. (number queries)

Evaluation Methodology and Results