Automated Negotiation and Bundling of Information Goods

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



2: abort-bargaining

2: accept-proposal(Offer, Precondition)

2: propose (Offer, Precondition)




2: abort-bargaining


2: propose (Offer, Precondition)

3: abort-bargaining




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




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?

Documents

Automated Negotiation and Bundling of Information Goods