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Automated Negotiation and Bundling of Information Goods

Explore a system where autonomous agents dynamically negotiate subscription fees for information goods, enhancing flexibility and efficiency. Applicable to various industries with customizable subscription terms and bilateral bargaining. Implementing a Pareto-search method to achieve optimal outcomes.

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Automated Negotiation and Bundling of Information Goods

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  1. 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

  2. Outline talk • Describe the system • Negotiate about subscription fee • Agent system • Customer and shop agent • Bilateral bargaining • Multi-issue bargaining • Pareto-search method • results

  3. Overview System • Sell subscriptions through negotiation • High degree of flexibility • Automated by autonomous agents • Delegate time consuming process • Application: Financial News • Broadly applicable (e.g., software, music, and video clips)

  4. Setting System • Monopolistic setting: one seller many customers • Subscriptions for short periods, e.g. 1 day: • Micro-payment • Learning • Changing preferences

  5. Subscription Terms of subscription specify: • News categories, e.g., banks, ICT, telecommunication • Fixed price or subscription fee • Variable price: purchase of single additional news items

  6. Agent System • Seller agent represents news provider • Customer agent GUI: • Customer preferences • Negotiation strategy

  7. 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

  8. 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

  9. Bilateral Bargaining Process

  10. Bilateral Bargaining Process

  11. Bilateral Bargaining Process

  12. Multi-Issue Bilateral Bargaining • Issues fixed and variable price (pf,pv) • Competitive aspect: `tug-of-war’ • Aspiration level at time t • Concession 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

  13. Example Iso-utility curves for given bundle

  14. Example Iso-utility curves for given bundle

  15. Example Iso-utility curves for given bundle

  16. Example Iso-utility curves for given bundle

  17. 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

  18. Orthogonal Strategy

  19. Derivative Follower Extension Distance 1 Distance 2 < Distance 1? Increase step-size Distance 2

  20. Derivative Follower Extension Distance 1 Distance k > Distance k-1? decrease step-size and turn Distance 2 Distance k Distance k-1

  21. 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

  22. Results

  23. 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?

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