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Dynamic Pricing of Information Goods. Joint work with: Gabi Koifman, Avigdor Gal Technion. Onn Shehory IBM Haifa Research Labs. Motivation . Rapid growth in electronic commerce The information economy vision (Kephart et al.) Agents accumulate knowledge, stored in databases

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Dynamic pricing of information goods

Dynamic Pricing of Information Goods

Joint work with:

Gabi Koifman, Avigdor Gal

Technion

Onn Shehory

IBM Haifa Research Labs


Motivation
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


Problem statement
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

I need more information NOW. Willing to spend 50$ for it.

I can sell records to make profit

Domain:Stocks

Domain:Stocks


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

  • Negligible marginal cost

  • Uniqueness

  • Pricing

  • Experience goods (Advertising)

  • Delivery

  • Schema/tuple ambiguity


Compatibility evaluation
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


Buyer s anxiousness level
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)


Market trends
Market Trends

Calc:current supply\demand levels

Calc: average supply

Re-calc:average supply

Calc:average personal demand

Re-calc:average personal demand

set:reference supply\demand levels

Re-set:reference supply\demand levels


Utility evaluation
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


Pricing policies
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


Negotiation participants
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)


Negotiation Model

Agent 1

DBE

Agent 2

RequestToPublish

Contact

PublishingSeller

WillingToNegotiate

InitialOffer

TransferGoods

OntoBuilder

Compatibility

Evaluation

μ>T

RequestForQueries

SafeSigns

ReplyForQueries

Schema-mapping learning

μ>T

Utility

Evaluation

RequestForDistanc

DistanceReply

Calc Distance

(2,1)

CounterOffer

Seller Process

Offer

Price

Negotiation

CounterOffer

Buyer Process

Offer

CloseDeal

TerminateNegotiation

Market trends learning

CloseDeal

AL learning

TerminateNegotiation

Closer

Interaction

diagram


Simulation system
Simulation System

  • Java language – JMS on J2EE.

  • MS-access database

  • JMS messaging


Simulation participants
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


Pricing policies evaluation
Pricing Policies Evaluation:

  • System profit /volume

  • Equilibrium

    Market settings:

  • Non-competitive market

  • Competitive market


System profit
System Profit

Market

Based Pricing

Derivative

follower

Personalized

Pricing

Trial and

Error

Market

Based Pricing

Personalized

Pricing

Derivative

follower

Trial and

Error


Equilibrium
Equilibrium

PP agent should deviate to MBPP

MBPP agent should not deviate


Conclusions
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




Related work
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


Future work
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.


Database based information goods compatibility evaluation

Evaluation Methodology and Results

Database-based Information Goods Compatibility Evaluation

  • Imprecision

  • Mapping Effectiveness

  • Mapping Cost


Compatibility evaluation 1 mapping imprecision

Evaluation Methodology and Results

Compatibility Evaluation (1) :Mapping Imprecision

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

Not

Improved

29.8%

Not Improved

13.7

Improved

40.2%

No Change

(0 imprecision)

21.2%

Improved

50.8%

No Change

(0 imprecision)

21.4%

No change

8.5%

No change

14.2%


Compatibility evaluation 2 mapping effectiveness

Evaluation Methodology and Results

Compatibility Evaluation (2) :Mapping Effectiveness


Compatibility evaluation 3 mapping cost

Evaluation Methodology and Results

Compatibility Evaluation (3) :Mapping Cost


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