InfoCenters and Information E-markets

Itai Yarom

PhD Researcher - AI Lab

jarom@cs.huji.ac.il

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Agenda

Introduction and motivation

The model

The Experiments

Results and conclusions

Future research

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IntroductionE-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

InformationE-marketplace

Buyer

Buyer

Buyer

Seller

Seller

InfoCenter InfoCenter

InfoSP InfoSP

I1

I2

I1 I2&

I1 I2

CombineTranslate

I1 I2

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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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MotivationWhen 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 BuyersCompare-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 Nash-equilibria

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.

Juicemaking service

Packaging service

InfoSPs

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InfoCenters’ CapabilitiesManipulate information

InfoCenters can approach InfoSP agents in order to obtain manipulated information (e.g., combine, translate operators).

Switch informationInfoCenters can change the information they offer.

CooperationInfoCenters can share the information products that they offer.

Intelligent InfoCenterThe 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 AgentsWho 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:[0,1]->[0,a] when a>0

Sj

j

i

tprofit

tprofittIU

)(

)(),(

rtCtPtprofitr

ifi

g/))()(()(

1

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Criteria for Evaluation

InfoCenter profitThis criterion compares the gain obtained by the InfoCenter in each one of the configurations tested.

Stability of the marketplaceThis 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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ResultsInfoCenters 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

MYCo-

MY

Switching

MYIC

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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ResultsInfoCenters 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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ResultsIntelligent 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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ConclusionsInfoCenters 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 ResearchWhat 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 InformationThe 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 E-marketplaces 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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ReferenceDynamic 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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