MS Ecommerce course 20-853
Electronic Negotiation
Spring 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/~sandholm/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 PhDlevel course Foundations of Electronic
Marketplaces
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
– 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
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
Pricing
• Can increase social welfare
– Fixed-menu take-it-or-leave-it offers -> negotiation
• Cost of generating & disseminating catalogs?
static
• Other customers see the price?
nondiscriminatory discriminatory
• 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
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
– 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.
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