CS 15-892 Foundations of Electronic MarketplacesMotivationAutomated negotiation systemsFertile, timely, important research areaThis courseSystems with self-interested agents (computational or human)A vision: How these techniques can/could play a role in different stages of an ecommerce transactionAutomated negotiation techniques in different ecommerce stagesAutomated negotiation techniques in different ecommerce stages...Slide 10Example applicationsAgenthood, utility function, rationality & bounded rationality, evaluation criteria of multiagent systemsAgenthoodUtility functions are scale-invariantFull vs bounded rationalityCriteria for evaluating multiagent systemsCS 15-892 Foundations of Electronic Marketplaces Tuomas SandholmProfessorComputer Science Department Carnegie Mellon UniversityInstructor’s web page: www.cs.cmu.edu/~sandholmCourse web page: www.cs.cmu.edu/~sandholm/cs15-892F11/cs15-892.htmMotivationAutomated 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–Developed communication infrastructure (Internet, WWW, EDI, …)–Electronic commerce on the Internet: Trading goods, services, information, advertising, predictions, 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 scaleFertile, timely, important 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•In this setting 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•Optimization has recently become scalable enough to make these things practical–Custom integer programs for clearing problems–Custom (e.g., convex) optimization for computing strategies•The applications change the worldThis 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 intersectionSystems 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 theoryA vision: How these techniques can/could play a role in different stages of an ecommerce transactionAutomated negotiation techniques in different ecommerce stages•1. Interest generation (vendors compete for customers’ attention)–Sponsored search•Search keyword auctions (Google, Baidu, Yahoo!, Bing)–Bid optimization vendors•Display ad markets (Yahoo!, DoubleClick (now part of Google), Right Media (now part of Yahoo!), adECN (now part of Microsoft), Baidu, …)–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 vendorsAutomated 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 530–Negotiation will focus the generation, but vendor may bias prices & offerings based on path–Preferences over bundles–Coalition formationdynamicstaticnondiscriminatory discriminatoryPricingAutomated 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 salesExample applications•Application classes–B2B (business-to-business), •Sourcing & procurement (live auctions & RFPs/RFQs)–Ariba, CombineNet, Emptoris–Buying consortia (e.g. healthcare GPOs, Covisint,