Dynamic Pricing of Information Goods

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Dynamic Pricing of Information Goods
Onn Shehory IBM Haifa Research Labs

• Joint work with
• Gabi Koifman, Avigdor Gal
• Technion

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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

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Problem Statement

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

Bobs Agent
Alices Agent
I need more information NOW. Willing to spend 50
for it.
I can sell records to make profit
DomainStocks
DomainStocks
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(DB-based) Information goods market vs.
traditional market

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

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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 SF (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

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Buyers Anxiousness Level

• Assumption willingness to pay is proportional to
  buyers 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)

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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
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Utility Evaluation

• Distance(seller, buyer) number of tuples that
  exist in the sellers database and not in the
  buyers database
• If (distance (seller, buyer)gt ?) 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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Agent 1
Agent 2
DBE
Negotiation Model
Interaction diagram
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Simulation System

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

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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
Market Based Pricing
Derivative follower
Trial and Error
Personalized Pricing
Market Based Pricing
Derivative follower
Trial and Error
Personalized Pricing
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Equilibrium
PP agent should deviate to MBPP
MBPP agent should not deviate
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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
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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
Evaluation Methodology and Results

• Imprecision
• Mapping Effectiveness
• Mapping Cost

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Compatibility Evaluation (1) Mapping Imprecision
Evaluation Methodology and Results
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
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Compatibility Evaluation (2) Mapping
Effectiveness
Evaluation Methodology and Results
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Compatibility Evaluation (3) Mapping Cost
Evaluation Methodology and Results