CS 15-892 Foundations of Electronic Marketplaces

1. CS 15-892 Foundations of Electronic Marketplaces 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/cs15-892F11/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 • 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 scale

4. Fertile, 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 world

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, 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 vendors

9. dynamic Pricing static nondiscriminatory discriminatory 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 530 • Negotiation will focus the generation, but vendor may bias prices & offerings based on path • Preferences over bundles • Coalition formation

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. Example applications • Application classes • B2B (business-to-business), • Sourcing & procurement (live auctions & RFPs/RFQs) • Ariba, CombineNet, Emptoris • Buying consortia (e.g. healthcare GPOs, Covisint, 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 • Markets for advertising (sponsored search, display ads, TV ads, print ads, …)

12. Agenthood, utility function, rationality & bounded rationality, evaluation criteria of multiagent systems

13. u i 1 Risk averse Risk neutral 0.5 Risk seeking 0 M$ 0 0.5 1 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 ui 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 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 Risk aversion => insurance companies

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

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

16. Criteria for evaluating multiagent systems • Computational efficiency • Distribution of computation • Communication efficiency • Social welfare: maxoutcome ∑i ui(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