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 Domain:Stocks 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) μ att (a,a’) is the similarity measure of a, a’ μ F is computed based on all μ att in F Utility is based on μ F

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: 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

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 Interaction diagram Agent 1Agent 2DBE Transfer Goods Closer Price Negotiation CounterOffer CloseDeal TerminateNegotiation Seller Process Offer Buyer Process Offer Market trends learning AL learning Utility Evaluation RequestForDistanc DistanceReply Calc Distance (2,1) Negotiation Model Contact RequestToPublish PublishingSeller WillingToNegotiate InitialOffer Compatibility Evaluation OntoBuilder μ>Tμ>T SafeSigns ReplyForQueries RequestForQueries Schema-mapping learning μ>Tμ>T

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 Derivative follower Trial and Error Personalized Pricing Market Based Pricing Derivative follower Trial and Error Personalized Pricing Market Based Pricing

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

23 Compatibility Evaluation (1) : Mapping Imprecision Evaluation Methodology and Results Improved 40.2% No change 8.5% Not Improved 29.8% No Change (0 imprecision) 21.4% No change 14.2% Not Improved 13.7 Improved 50.8% No Change (0 imprecision) 21.2% Using SafeSigns ability to generate 0-imprecision mappings was doubled!!!

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

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