automated negotiation and bundling of information goods
DESCRIPTION
Automated Negotiation and Bundling of Information Goods. Koye Somefun , Enrico Gerding, and Han La Poutré Center for Mathematics and Computer Science (CWI) Amsterdam, The Netherlands. Outline talk. Describe the system Negotiate about subscription fee Agent system Customer and shop agent - PowerPoint PPT PresentationTRANSCRIPT
Automated Negotiation and Bundling of Information Goods
Koye Somefun, Enrico Gerding, and Han La Poutré
Center for Mathematics and Computer Science (CWI)Amsterdam, The Netherlands
Outline talk• Describe the system
• Negotiate about subscription fee• Agent system• Customer and shop agent
• Bilateral bargaining• Multi-issue bargaining• Pareto-search method• results
Overview System• Sell subscriptions through
negotiationHigh degree of flexibility
• Automated by autonomous agentsDelegate time consuming process
• Application: Financial News• Broadly applicable (e.g., software,
music, and video clips)
Setting System • Monopolistic setting: one seller many
customers• Subscriptions for short periods, e.g. 1
day:• Micro-payment• Learning• Changing preferences
SubscriptionTerms of subscription specify:
• News categories, e.g., banks, ICT, telecommunication
• Fixed price or subscription fee
• Variable price: purchase of single additional news items
Agent System
• Seller agent represents news provider• Customer agent GUI:
• Customer preferences• Negotiation strategy
Customer Preferences• Select the news categories• Utility function is Uc=bmax-(pf+pv·c)
• Bmax is maximum budget• pf is fixed price• pv is variable price and c is the customer’s estimation of the articles
read (for the specified news categories)
• Customer specifies bmax and c
• Agent will negotiate pf and pv
Seller Agent• Maximize expected utility:Us=pf+pv·s(pv)
• pf is fixed price,• pv is variable price, and s(pv) is the shop’s estimation of the articles read
• Shop specifies s(pv): • assume the higher pv the lower s (law of
demand)• Shop could use average customer behavior data
to predict s(pv)• Agent will negotiate pf and pv
Bilateral Bargaining Process
1: propose(Offer, Precondition)
Responder = Buyer or SellerInitiator = Seller or Buyer
Bilateral Bargaining Process
1: propose(Offer, Precondition)
2: abort-bargaining
2: accept-proposal(Offer, Precondition)
2: propose (Offer, Precondition)
Responder = Buyer or SellerInitiator = Seller or Buyer
Bilateral Bargaining Process
1: propose(Offer, Precondition)
2: abort-bargaining
2: accept-proposal(Offer, Precondition)
2: propose (Offer, Precondition)
3: abort-bargaining
3: accept-proposal(Offer, Precondition)
3: propose(Offer, Precondition)
Responder = Buyer or SellerInitiator = Seller or Buyer
Multi-Issue Bilateral Bargaining• Issues fixed and variable price (pf,pv)• Competitive aspect: `tug-of-war’
• Aspiration level at time tConcession Strategy
• Cooperative, multi-issue aspect• Find Pareto-efficient outcomes• Beneficial for seller and consumer(win-win)Pareto-search Strategy
• We develop techniques for the multi-issue aspect
ExampleIso-utility curves for given bundle
ExampleIso-utility curves for given bundle
ExampleIso-utility curves for given bundle
ExampleIso-utility curves for given bundle
Pareto-search Strategy • Find Pareto-efficient point without
knowing opponent’s curve• Approach Pareto-efficient solutions
during concession• Solutions:
• Orthogonal Strategy• Enhanced with Derivative Follower
Orthogonal Strategy
Derivative Follower Extension
Distance 1Distance 2 < Distance 1?
Increase step-size
Distance 2
Derivative Follower Extension
Distance 1
Distance 2
Distance k > Distance k-1?
decrease step-size and turn
Distance k-1Distance k
Computational Experiments• Evaluate efficiency and robustness of the
Pareto-search strategies• Seller agent:
• Convex preferences• Concession strategy with fixed concession
• Customer agent• Linear preferences• Hardhead,Fixed,Fraction,Tit-for-tat
• Compare to random search strategy
ResultsConcession Strategy
Pareto-distance (random)
Pareto-distance (orthogonal/DF)
Pareto-distance (+DF/+DF)
Hardhead 18.92 8.03 18.63
Fixed (20) 26.52 10.43 28.82
Fixed (40) 38.91 16.21 44.29
Fixed (80) 42.12 25.61 48.84
Fraction (0.025) 30.26 10.07 32.25
Fraction (0.05) 31.53 11.52 28.52
Fraction (0.1) 37.81 16.91 26.28
Tit-for-tat 72.78 59.60 56.64
Conclusion• Agent system for selling information
bundles through automated negotiation• Orthogonal Strategy enhanced with
Derivative Follower for approaching Pareto efficiency
• Works well for different concession strategies and preferences
Questions?