1. CS 15-892 Foundations of Electronic Marketplaces (Fall 2007) Tuomas Sandholm Professor Computer Science Department Carnegie Mellon University Instructor’s web page: www.cs.cmu.edu/~sandholm Course web page: www.cs.cmu.edu/~sandholm/cs15-892F07/cs15-892.htm

2. Motivation

3. Automated negotiation systems Agents search & make contracts –Through peer-to-peer negotiation or a mediated marketplace –Agents can be real-world parties or software agents that work on behalf of real-world parties Increasingly important from a practical perspective –Developing communication infrastructure (Internet, WWW, NII, EDI, KQML, FIPA, Concordia, Voyager, Odyssey, Aglets, AgentTCL, Java Applets,...) –Electronic commerce on the Internet: Goods, services, information, bandwidth, computation, storage... –Industrial trend toward virtual enterprises & outsourcing Automated negotiation allows (somewhat) dynamically formed alliances on a per order basis in order to capitalize on economies of scale, and allow the parties to stay separate when there are diseconomies of scale

4. Automated negotiation systems … Fertile, timely research area –Deep theories from game-theory & CS merge Started together in the 1940’s [Morgenstern & von Neumann] There were a few decades of little interplay Upswing of interplay in the last few years –The intersection is a very fruitful, relatively open research area It is in this setting that the prescriptive power of game theory really comes into play –Market rules need to be explicitly specified –Software agents designed so as to act optimally unlike humans ("As far as the laws of mathematics refer to reality, they are not certain; and as far as they are certain, they do not refer to reality.“ - Albert Einstein) –Computational capabilities can be quantitatively characterized, and prescriptions can be made about how the agents should use their computation optimally

5. This course Covers –The most relevant classic results from game theory –The state-of-the-art through recent research papers Many of them have not even been published yet Covers –game-theoretic aspects –computational aspects –and most importantly, the intersection

6. Systems with self-interested agents (computational or human) Mechanism (e.g., rules of an auction) specifies legal actions for each agent & how the outcome is determined as a function of the agents’ strategies Strategy (e.g., bidding strategy) = Agent’s mapping from known history to action Rational self-interested agent chooses its strategy to maximize its own expected utility given the mechanism => strategic analysis required for robustness => noncooperative game theory But … computational complexity –In executing the mechanism E.g. combinatorial auctions NP-complete & inapproximable to clear –In determining the optimal strategy E.g. NP-complete valuation calculations E.g. uncomputable best-response strategies in repeated games –In executing the optimal strategy E.g. chess: how much space needed to represent an optimal strategy? Has significant impact on prescriptions –Has received little attention in game theory

7. A vision: How these techniques can/could play a role in different stages of an ecommerce transaction

8. Automated negotiation techniques in different ecommerce stages 1. Interest generation (vendors compete for customers’ attention) –Sponsored search Search keyword auctions (Google, Yahoo!, MSN) –Bid optimization vendors Banner ad markets (Yahoo!, DoubleClick (now part of Google), Right Media (now part of Yahoo!)…) –Funded adlets that coordinate Avatars for choosing which ads to read Customer models for choosing who to send ads and how much $ to offer 2. Finding –Simple early systems: BargainFinder, Jango –Meta-data, XML –Standardized feature lists on goods to allow comparison How do these get (re)negotiated –Different vendors prefer different feature lists –Shopper agents need to understand the new lists –How do algorithms cope with new features? –Want to get a bundle => need to find many vendors

9. Automated negotiation techniques in different ecommerce stages... 3. Negotiating –Advantages of dynamic pricing Right things sold to (and bought from) right parties at right time World becomes a better place (social welfare increases) –Further advantages from discriminatory pricing Can increase social welfare (e.g., if production increases) –Fixed-menu take-it-or-leave-it offers -> negotiation Cost of generating & disseminating catalogs? Other customers see the price? Negotiation overhead? Personalized menus –Could check customer’s web page, links to & from it, what other similar customers did, customer profiles Generating/printing the menu may be intractable, e.g. mortgages 5 30 –Negotiation will focus the generation, but vendor may bias prices & offerings based on path –Preferences over bundles –Coalition formation dynamic static nondiscriminatorydiscriminatory Pricing

10. Automated negotiation techniques in different ecommerce stages... 4. Contract execution –Digital payment schemes –Safe exchange Third party escrow companies –E.g., Tradesafe Inc. & Tradenable Inc. (formerly i- Escrow Inc.) –Two-sided, e.g., www.safefunds.com Sometimes an exchange can be carried out without enforcement by dividing it into chunks [Sandholm&Lesser IJCAI-95, Sandholm96,97, Sandholm&Ferrandon ICMAS-00, Sandholm&Wang AAAI-02] 5. After sales

11. Automated negotiation techniques in different ecommerce stages... 4. Contract execution –Digital payment schemes –Safe exchange Third party escrow companies –Tradesafe Inc. –Tradenable Inc. –i-Escrow Inc. Sometimes an exchange can be carried out without enforcement by dividing it into chunks [Sandholm&Lesser IJCAI-95, Sandholm96,97, Sandholm&Ferrandon ICMAS-00, Sandholm&Wang AAAI-02] 5. After sales

12. Example applications Application classes –B2B (business-to-business), Sourcing & procurement (live auctions & RFPs/RFQs) –Ariba, CombineNet, Emptoris, Procuri –Buying consortia (e.g. Covisint, CombineNet, CombineMed, Trade-ranger) –IntercontinentalExchange, Inc. (acquired ChemConnect 6/2007) –B2C (business-to-consumer), e.g. goods, debt –C2C (consumer-to-consumer), e.g. eBay –Task and resource allocation in computer systems (networks, computational grids, storage systems…) –…–… Just a few example application areas –Electricity markets –Manufacturing subcontracting –Transportation exchanges –Stock markets –Collaborative filtering

13. Tuomas Sandholm Professor Computer Science Department Carnegie Mellon University Agenthood, utility function, rationality & bounded rationality, evaluation criteria of multiagent systems

14. Agenthood We use economic definition of agent as locus of self-interest –Could be implemented e.g. as several mobile “agents” … Agent attempts to maximize its expected utility Utility function u i of agent i is a mapping from outcomes to reals –Can be over a multi-dimensional outcome space –Incorporates agent’s risk attitude (allows quantitative tradeoffs) E.g. outcomes over money Risk aversion => insurance companies Lottery 1: $0.5M w.p. 1 Lottery 2: $1M w.p. 0.5 $0 w.p. 0.5 Agent’s strategy is the choice of lottery M$ u i Risk seeking Risk neutral Risk averse 1 0.5 0 0 1

15. Utility functions are scale-invariant Agent i chooses a strategy that maximizes expected utility max strategy  outcome p(outcome | strategy) u i (outcome) If u i ’() = a u i () + b for a > 0 then the agent will choose the same strategy under utility function u i ’ as it would under u i –(u i has to be finite for each possible outcome; otherwise expected utility could be infinite for several strategies, so the strategies could not be compared.)

16. Full vs bounded rationality Full rationality Bounded rationality Descriptive vs. prescriptive theories of bounded rationality

17. Criteria for evaluating multiagent systems Computational efficiency Distribution of computation Communication efficiency Social welfare: max outcome ∑ i u i (outcome) –Requires cardinal utility comparison –… but we just said that utility functions are arbitrary in terms of scale! Surplus: social welfare of outcome – social welfare of status quo –Constant sum games have 0 surplus. Markets are not constant sum Pareto efficiency: An outcome o is Pareto efficient if there exists no other outcome o’ s.t. some agent has higher utility in o’ than in o and no agent has lower –Social welfare maximization => Pareto efficiency Individual rationality: Participating in the negotiation (or individual deal) is no worse than not participating Stability: No agents can increase their utility by changing their strategies Symmetry: No agent should be inherently preferred, e.g. dictator