Dynamic Pricing of Information Goods

1. Dynamic Pricing of Information Goods Joint work with: Gabi Koifman, Avigdor Gal Technion Onn Shehory IBM 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 I need more information NOW. Willing to spend 50$ for it. I can sell records to make profit Domain:Stocks Domain:Stocks

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 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

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

9. 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

10. 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)

11. 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

12. Simulation System • Java language – JMS on J2EE. • MS-access database • JMS messaging

13. 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

14. Pricing Policies Evaluation: • System profit /volume • Equilibrium Market settings: • Non-competitive market • Competitive market

15. System Profit Market Based Pricing Derivative follower Personalized Pricing Trial and Error Market Based Pricing Personalized Pricing Derivative follower Trial and Error

16. Equilibrium PP agent should deviate to MBPP MBPP agent should not deviate

17. 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

18. The End

19. Backup Slides

20. 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

21. 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.

22. Evaluation Methodology and Results Database-based Information Goods Compatibility Evaluation • Imprecision • Mapping Effectiveness • Mapping Cost

23. 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%

24. Evaluation Methodology and Results Compatibility Evaluation (2) :Mapping Effectiveness

25. Evaluation Methodology and Results Compatibility Evaluation (3) :Mapping Cost