Loading in 2 Seconds...
Loading in 2 Seconds...
MS Ecommerce course 20-853 Electronic NegotiationSummer 2004 Professor Tuomas Sandholm School of Computer Science Carnegie Mellon University Instructor’s web page: www.cs.cmu.edu/~sandholm Course web page: http://www.cs.cmu.edu/~gilpin/ec20-853/ec20-853.htm
Course content at a high level • Covers the state-of-the-art • Covers • game-theoretic aspects • computational aspects • Additional readings (and proofs of claims) are available on the web site of my PhD-level course Foundations of Electronic Marketplaces
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 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
Automated negotiation systems … • Fertile, timely area • Deep theories from game-theory & computer science 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 • It is in this setting that the prescriptive (=normative) 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
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 • In determining the optimal strategy • In executing the optimal strategy • Has significant impact on prescriptions • Has received little attention in game theory
A bold vision: How automated negotiation techniques could play a role in different stages of an ecommerce transaction
Automated negotiation techniques in different ecommerce stages • 1. Interest generation • 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 machine learning algorithms cope with new features? • Want to get a bundle => need to find many vendors
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 • Fixed-menu take-it-or-leave-it offers -> negotiation • Cost of generating & disseminating catalogs? • Other customers see the price? • Negotiation overhead? • Personalized menus (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
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
Example applications • Application classes • B2B (business-to-business), e.g. procurement RFPs/RFQs, buying consortia (e.g. Covisint), … • 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
Basics Agenthood, utility function, evaluation criteria of multiagent systems
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
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 Note that 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
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 • Implied by social welfare maximization • 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