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Information Economies

By J. Kephart, J.Hansen, D. Levine, B. Grosof, J. Sairamesh, R. Segal, “Emergent Behavior in Information Economies”.

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Information Economies

Research AgendaInformation Filtering Economy

Monopoly Duopoly Large System

Emergent behavior in information economies.

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Open Markets of Independent Agents

Internet evolves towards a milieu of Agents.Open, free-market information economy of

agents engaged in economic activities. Inter-agent economic transactions.

Is information economy inevitable? Increasingly important role of agents in e-

commerce; Evolution of agents from transaction facilitators to

decision-makers; Increasing autonomy and responsibility.

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Economic Mechanisms

Traditionally developed for human agents.May not be directly applicable to agent

communities: Agents make decisions and act at a much

greater speed; Agents are much simpler then humans with

respect to their governing rules; Less flexible; Lack in “common sense”.

Do these differences matter?

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Research Agenda

“Is an information economy inherently capable of governing billions of software agents, and if so what are the minimal requirements on the infrastructure of such an economy and on the agents that populate it? ”

Dynamical systems: nonlinear systems are capable of chaotic behavior.

SI agents can be susceptible to unpredictable collective behavior.

Efficient resource allocation as an emergent feature of a system.

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Challenges of the Web

Open system.No global purpose.No global cooperation.No universal medium of exchange.No universal ontology.No universal set of agent types or

algorithms.Will these features emerge?

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Research Approaches

Analytical Game theory, mechanism design, etc.

Multi-Agent Simulation

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A News Filtering Economy

Agents engaged in trading news articles.

Simple behavioral rules.Focus on dynamical and economical

aspects of the system; Assume that non-economic issues

(security, transaction processing, etc.) have been solved.

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Model

Source agent: publishes the articles.C consumer agents: want to buy articles.B broker agents: buy selected articles from

the source and resell them to consumers.System infrastructure that provides

computation and communication infrastructure for the agents.

All the agents maximize their utility function.

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Model (cont’d)

System

Source, {j}, Ps

Broker 1, {1j}, P1

Broker 2, {2j}, P2

Consumer 1, {1j}, V

Consumer 3, {3j}, V

Consumer 2, {2j}, V

PT

PT, PC

PC

PS

PS

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Model (cont’d) The source agent publishes an article at each time

step t and assigns it to category j with probability j.

The article is offered for sale to all brokers at PS. For each article sold to the broker the source agent pays PT transportation fee to the system.

A broker decides whether or not to buy the article; The decision may be uncorrelated with the classification.

For each evaluation the broker pays PC computation fee to the system.

The broker’s decision method is parametrized through (random process).

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Model (cont’d)

When a broker purchases an article, it immediately sends it to a set of subscribing consumers, paying a transportation fee PT to the system.

Subscribers examine the article and pay to the broker (Pb) if they want to keep the article (consume it).

When a consumer receives an article, it pays PC

computation fee to the system for evaluation. The broker’s decision method is parametrized

through (random process).

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Analysis of the System

Utility maximization settingProbabilistic setting for modeling purposes.Problem: Maximize expected utility per article for

consumers and brokers.Even for a simple model the expected profit per

article formulae (for brokers and consumers) become quite involved (see the paper).

Computation requires a lot of state information.In a distributed system such information would be

unavailable/prohibitively expensive to obtain.

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System with one broker A single broker offers a single category to an arbitrary

large number of consumers. B=1, C

The broker and the consumers have complete info about the system.

They act instantaneously to maximize their profit: Broker chooses a set of preferred consumers; Consumers decide whether they want to subscribe to the broker

or not. The broker and the consumers agree that the

subscription makes sense if consumer’s interest is above a certain threshold.

They disagree on what the threshold should be. If both sides can reach an agreement an equilibrium

price is established.

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Two-broker System

Add another broker and another information category.

Each broker sets its price and interest level to the value that maximizes its profit given the other broker’s parameters; Inter-agent feedback.

The settings are not changed until the other broker moves its parameters.

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System Behavior: Two-broker case Brokers make adjustments at every time tick:

P1(t+1)=P1*(P2(t)), P2(t+1)=P2

*(P1(t)), etc. Emergent phenomena:

Price war; Spontaneous specialization.

Asymptotic behavior of the system is a limit cycle: Regardless of the initial price p1, two brokers will become

trapped in a price war. First, each broker tries to grab a large portion of the

market by undercutting the price of the other broker. Eventually, losses overweigh the desire to undercut the

competitor, and a broker raises the price sharply. The second broker raises the price in response. Undercutting starts again.

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Price wars

Results in a more complex analog of a price war.

Both the prices and the set of categories offered cycle endlessly.

Price wars are harmful to the brokers. Each broker gets only half of the utility it expected

to get.Going for an instantaneous global optimum

does not work in a system with more then one broker. Greedy strategy fails.

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Can it be overcome?

The agents are myopic: there decisions are made without anticipating the other player’s response.

Solution: add foresight: An agent modeling another agent; Level-one agent models its opponent as a

myopic agent; Level-two agent models its opponent as level-

one agent, etc. Agents cannot accurately model each other in

most cases.How deep should we go?

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Large SystemSimulation handles several thousand agents

and hundreds of categories..A typical consumer is completely interested

in a few categories (~3).Broker knows nothing about its competitors.

Must rely on historical data on financial consequences of their actions.

To set prices, brokers randomly shift their price by a small amount up or down. If the profit increases the broker keeps moving

the price in this direction; Otherwise, it reverses it.

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Large System (cont’d)

Brokers also vary their parameter depending on the trend of the profit.

Brokers and consumers estimate utility of adding or canceling a subscription.

Originally, each consumer chooses a broker randomly.

Agents make asynchronous, independent decisions about adjusting prices, interest vectors (parameters), or subscriptions.

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Emergent Behavior Only a fraction of brokers remain active after

substantial time elapses; 120 out of 500 after 200000 ticks.

The rest have gone broke or do not participate in the exchange (retired).

All the agents have specialized in a single category; Some categories disappear from active exchange.

Some consumers temporarily drop out of the exchange while before the system reaches the equilibrium.

Once a broker has semi-coherent following, it is encouraged by its clientele to specialize.

Once the broker specializes, it accrues more clientele interested in its offerings.

After 100000 ticks every broker has specialized. Consumers receive no junk.

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Conclusions. It will be necessary to build agents and agent

environments that are robust to deleterious inter-agent feedback effects.

Environment that has the right mix of flexibility and constraints can support a stable economy.

Small systems: price wars.Large systems: spontaneous specialization.With more sophisticated agents, we can expect

deleterious emergent phenomena harder to combat, and beneficial once to create inestimable value.