InfoCenters and
Information E-markets
Itai Yarom
PhD Researcher - AI Lab
jarom@cs.huji.ac.il
Agenda
Introduction and motivation
The model
The Experiments
Results and conclusions
Future research
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Introduction
E-commerce opens up the opportunity to trade
with information, e.g., single articles, customized
news, music, video.
E-marketplaces enable users to buy/sell
information commodities.
Information intermediaries can enrich the
interactions and transactions implemented in such
markets (we extend the basic model presented by Kephart
et al.(2000)).
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What are e-markets?
Market infra-structure
Centralized or decentralized (P2P) market.
Trading protocol
How agent communicate? E.g. Web-services.
Trading mechanism
Post-Prices, CDA, Auction, reverse auction.
Market Policies
E.g., reputation mechanism.
Agent strategies
How do the agents decide what to do?
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Information E-marketplace
I1 & I2
Combine
InfoSP
InfoSP
Buyer
Buyer
Translate
Seller
I1
I
I
Information
1 2
E-marketplace
I2
I1 I2
Buyer
InfoCenter
Seller
InfoCenter
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InfoCenter Agents
E-markets are extended to include
InfoCenter agents and Information Services
Providers (InfoSPs).
Advantages:
InfoCenters have wide accessibility to
information commodities and can contact
different information sources.
InfoCenters can approach InfoSPs to obtain and
sell manipulated information.
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Motivation
When does the need for InfoCenter agents rise?
When InfoCenters already exist.
e.g., Stanford Digital Library Project.
When the buyers benefit from them.
Question answering service.
When the sellers benefit from them.
e.g., Kamoon (information matching service).
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The model
The E-market contains buyers, sellers,
InfoCenters and InfoSPs.
The number of buyers is significantly larger
than the number of sellers and InfoCenters.
Each agent performs an action at a random
rate (action = buy or set a price).
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The Buyers
Compare-All (70%)
Choose the seller with the
lowest price.
Compare-None (10%)
Choose a seller randomly.
Compare-Two (20%)
Choose two sellers
randomly and buy from the
one with the lowest price.
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The Sellers
Myoptimal (MY)
Choose the optimal price at a specific time.
Game Theory (GT)
Choose one price from the existing mixed Nashequilibria
Deviate Follower (DF)
Continue to change the price in the same direction (i.e.,
increase or decrease) until the profit falls under a
certain value.
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The InfoSPs
InfoSPs are
Information Service
Providers agents.
The services can be:
Different presentation
formats and resolutions.
Information updates.
Combining and
summarization.
Juice
making
service
InfoSPs
InfoCenters and Information E-markets
Packaging
service
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InfoCenters’ Capabilities
Manipulate information
InfoCenters can approach InfoSP agents in order to
obtain manipulated information (e.g., combine,
translate operators).
Switch information
InfoCenters can change the information they offer.
Cooperation
InfoCenters can share the information products that
they offer.
Intelligent InfoCenter
The InfoCenter can use AI technique, as planning and
approaching buyers to understand their needs.
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AI techniques
InfoCenters can use AI techniques, including:
Approach buyers in order to understand which
information they interested at.
Apply planning algorithms in order to use wisely
the InfoSPs’ services.
Share information on buyers’ preferences and on
InfoSPs’ services.
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Utility of the Agents
Who have the permission to set the market utility function?
•
Each agent.
•
Each agents’ creator.
•
Each marketplace.
The alternatives:
1. The sum of the agent’s profit.
2. The average of the agent’s profit.
3. The normalized average profit.
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Utility of the Agents (continue)
Utility characteristics:
Time independence.
Monotonic in the profit.
Monotonic in the
transaction.
Normalization:
U (I , t) 
profit i (t )
 profit (t )
j
jS
• U:[0,1]->[0,a] when a>0
r
profiti (t )  ( P(t )  C f g (t )) / r
i 1
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Criteria for Evaluation
InfoCenter profit
This criterion compares the gain obtained by
the InfoCenter in each one of the configurations
tested.
Stability of the marketplace
This criterion checks the effect of the
InfoCenter on market behavior.
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The Experiments
InfoCenters operate as sellers’ assistances.
How will the InfoCenters effect the e-market?
InfoCenters operate as autonomous agents.
What is the preferred discount method?
What are the best pricing strategies?
Intelligent InfoCenters.
Which AI techniques can increase the
InfoCenters profit?
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Results
InfoCenters operate as sellers assistance
InfoCenters that cooperate, switch or sell
manipulated information are more profitable than
basic ones.
In homogeneous markets, switching InfoCenters
are the most profitable.
In heterogeneous markets, InfoCenters who sell
manipulated information are the most profitable.
InfoCenters will affect the market by selling at
more stable prices, that are also higher on average.
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InfoCenters Price Behavior
Basic
MY
CoMY
Switching
MY
IC
MY
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InfoCenters’ Payment Strategies
Full Price (FP)
The InfoCenter pays the list-price for the information it
buys.
Wholesale Price (WP)
The InfoCenter pays a reduced price when it buys a
large quantity.
Subscription Price (SP)
The InfoCenter pays a subscription fee in order to
obtain the information, and then royalties for all
information it sells.
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Results
InfoCenters operate as autonomous agents
InfoCenters gain positive profit.
The buyers and the sellers benefit from the
existence of the InfoCenters.
InfoCenters benefit more from the sellers price
competition than from the sellers discount
(available at WP and SP).
The InfoCenters prefer to use MY or GT pricing
algorithm over the DF pricing algorithm.
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Results
Intelligent InfoCenters
Intelligent InfoCenters are more profitable than
regular InfoCenters.
Planning will reduce the costs of creating new
information. Therefore, planning will be more
significant as long as the variety of InfoSPs is
larger.
Approaching buyers will enable the InfoCenters to
offer more profitable information.
Using several AI technique will gain higher profit
as compare to using one technique.
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Stability of the Marketplace
In most of the cases the price behavior of an
e-market with InfoCenters are the same as one
without them, except:
When the InfoCenters cooperate or switch information.
In that case, the frequent of the price change is lower.
When the InfoCenter create new information. In that
case, the price change of the new information is similar
to the case of a market place with sellers offering that
information.
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Request for Price Quotes (RPQ)
Price sniffing (by buyers, sellers and InfoCenters)
will create a load on the sellers and the
InfoCenters1.
Sellers can handle the load by:
Using the InfoCenter as a retailer.
Ask for fee for every RPQ.
Deciding when to ask for price quote should be
done ‘wisely’.
1“When
bots Collide”, Kephart and Greenwald.
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Conclusions
InfoCenters succeed to gain positive profit.
Buyers and sellers gain from the existence of
InfoCenters in the market:
Provide more focused information to buyers.
Increase the sellers transaction.
Intelligent InfoCenter gain higher profit
comparing non-intelligent InfoCenters.
The price behavior with and without InfoCenters
is similar.
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Future Research
What is the best pricing algorithm for an agent in
an information e-market?
Will we get a different results when using other
market mechanism (or combination of them), like
auction or CDA?
How decentralized market will effect the agent
profit?
How can InfoCenters benefit from coalition?
What mechanism can be used to generate trust
between the agents? And does a trustworthy agent
is a good thing?
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More Information
The Role of Middle-Agents in Electronic
Commerce,
(IEEE Intelligent System Journal).
The Benefit of Software Middlemen in Information
E-markets: An Empirical Study
(Journal version in preparation).
The Design of Utility Functions for Information Emarketplaces with Price Quote Fees (not published).
The Impact of InfoCenters on E-Marketplaces
(AAMAS’02).
Pricing and Manipulation of Information in
E-Marketplaces (BISFAI 2001).
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Reference
Dynamic Pricing by Software Agents, Kephart,
Hanson and Greenwald, Computer Networks
(2000).
Shopbots and Pricebots, Greenwald and Kephart,
IJCAI-99.
Middle-agents for the internet, Decker, Sycara
and Williamson, IJCAI-97.
Agent-Human Interactions in the Continuous
Double Auction, Das, Hanson, Kephart and
Tesauro, IJCAI-01.
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