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/cs15-892F13/cs15-892.htm
Motivation
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
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
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
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
A vision:
How these techniques
can/could play a role in
different stages of an
ecommerce transaction
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
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)
dynamic
– 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
Pricing
static
nondiscriminatory
discriminatory
– 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
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 iEscrow 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
Example applications
• Application classes
– B2B (business-to-business),
• Sourcing & procurement (live auctions & RFPs/RFQs)
– Ariba, CombineNet, Emptoris
– Buying consortia (e.g. healthcare GPOs, Covisint, Traderanger)
– 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, …)
Agenthood,
utility function,
rationality & bounded rationality,
evaluation criteria of multiagent systems
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
ui
Risk averse
1
Lottery 2: $1M
$0
w.p. 0.5
w.p. 0.5
Agent’s strategy is the
choice of lottery
Risk neutral
0.5
Risk seeking
0
0
0.5
1
Risk aversion => insurance companies
M$
Utility functions are scale-invariant
• Agent i chooses a strategy that maximizes expected utility
• maxstrategy Soutcome 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.)
Full vs bounded rationality
Full
rationality
Environment
Bounded
rationality
Environment
Perceptions
Actions
Agent
Perceptions
Actions
Agent
Reasoning
machinery
solution quality
Descriptive vs. prescriptive
theories of bounded rationality
worth of solution
time
deliberation cost
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