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Agent-based Simulation

Directory of agent-based modeling links

Located here is a directory of links to applications of agent-based modeling, arranged by subject area. Of greatest importance to us is the economics section, which includes such projects as "artificial life simulation of the textile/apparel market," and "agent based simulation of the hotelling game." The page is put together by Craig Reynolds.

A simple introduction to agent-based modeling

For those who are unfamiliar with agent-based modeling (ABM), here is an easy-to-understand introduction to the subject. It is a list of answers to frequently asked questions about ABM, entitled "Agent-based modeling of complex, adaptive systems." It explains how agent-based modeling allows these systems to be modeled, and why modeling is a useful tool for understanding these systems.

Some articles on agent-based simulation

Just to update on some of the work I've been doing, here and here are links to a couple articles I've read about agent-based simulation that I did not find worthy of individual write-ups as they are not particularly useful to our project. The first article, by Robert Axelrod, presents a walk-through for those who plan to begin an agent-based simulation project, but does not really advance any new ideas for us.

Agent-Based Computational Economics

In addition to the multitude of agent-based computational economics on Leigh Tesfatsion's website, there is also an article on the subject here, written by Denis Phan. Will post more as I get through the article...

A few more agent-based simulation platforms

Moduleco is a "modular "multi-agent" platform, designed for to simulate markets and organizations, social phenomenons and population dynamics."

In addition to being a resource for agent-based modeling information, SwarmWiki is the home of Swarm, one of the original agent-based simulation programs. Swarm is a "multi-agent software platform for the simulation of complex adaptive systems. In the Swarm system the basic unit of simulation is the swarm, a collection of agents executing a schedule of actions. Swarm supports hierarchical modeling approaches whereby agents can be composed of swarms of other agents in nested structures. Swarm provides object oriented libraries of reusable components for building models and analyzing, displaying, and controlling experiments on those models." An in-depth description of Swarm can be found here

Agent-Based Simulation Application: The US Air Force

SEAS, the US Air Force's "Multi-Agent Theater Operations Simulation," is a further example of the broad ranges of uses for agent-based simulation. According to the SEAS website:

"Agent-based modeling: Methods and techniques for simulating human systems"

Located here is a very informative article about agent-based modeling (ABM), including information about what ABM is, the benefits of using ABM, and how ABM has been used in the past and will be used in the future. The article is written by Eric Bonabeau, and is from a May 14, 2002 issue of PNAS (Proceedings of the National Academy of Sciences of the United States of America). Some important passages from the article are presented below.

What is Agent-Based Modeling?

In agent-based modeling (ABM), a system is modeled as a collection of autonomous decision-making entities called agents. Each agent individually assesses its situation and makes decisions on the basis of a set of rules. Agents may execute various behaviors appropriate for the system they represent – for example, producing, consuming, or selling. Repetitive competitive interactions between agents are a feature of agent-based modelingAt the simplest level, an agent-based model consists of a system of agents and the relationships between them. Even a simple agent-based model can exhibit complex behavior patterns and provide valuable information about the dynamics of the real-world system that it emulates. In addition, agents may be capable of evolving, allowing unanticipated behaviors to emerge.

A question motivated by agent-based modeling articles I've been reading...

After reading several articles on agent-based modeling, I am struggling to answer a question that I've been pondering: How do you know that your agent-based model has a complete set of information?

When modeling customer behavior at a supermarket or amusement park, traders' behavior at NASDAQ, driver behavior in traffic, or any other human phenomenon, there are countless variables of human behavior that must be taken into account. When these models form a conclusion about how people act, and how theoretical changes in the structure of an institutional will affect human actions, how do they know that they have not left out a crucial piece of our thought processes that may be lost among the myriad of processes that have already been accounted for.

Agent-based Modeling Project: NASDAQ

A bit about NASDAQ's use of agent-based modeling:

To evaluate the impact of tick-size reduction, NASDAQ has been using an agent-based model that simulates the impact of regulatory changes on the financial market under various conditions. The model allows regulators to test and predict the effects of different strategies, observe the behavior of agents in response to changes, and monitor developments, providing advance warning of unintended consequences of newly implemented regulations faster than real time and without risking early tests in the real marketplace. In the agent-based NASDAQ model, market maker and investor agents (institutional investors, pension funds, day traders, and casual investors) buy and sell shares by using various strategies. The agents' access to price and volume information approximates that in the real-world market, and their behaviors range from very simple to complicated learning strategies. Neural networks, reinforcement learning, and other artificial intelligence techniques were used to generate strategies for agents. This creative element is important because NASDAQ regulators are especially interested in strategies that have not yet been discovered by players in the real market, again to approach their goal of designing a regulatory structure with as few loopholes as possible, to prevent abuses by devious players.

Use of agent-based simulation: Supermarket

The following passage describes the simulation technology used in SIMSTORE SIMSTORE is based on a real supermarket; it is intended to allow both buyers and sellers to see how they can minimize or maximize the time they spend in the store.

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