Agent-based Simulation

I have been trying to figure out the Metamodel presented by Jim yesterday on this website. I have tried to work through a simple model of an interaction between a buyer and seller at a farmers' market; it is shown below in the attached document. I am certain that this is not precisely how the interaction would be represented in our ABM program; any thoughts on how it should be altered? I want to make sure I have a simple interaction down before I move on to modeling larger environments, such as the entire farmers' market.

Click to view The LFEGame Model - Logical ViewWelcome to the Introduction to the first mini-book, Local Food Economy Game - Logical View, in the Local Food Economy Game Model Repository. This model will guide and constrain our design and implementation of the LFEGame.com agent-based simulation model.

This general class model is more accurately described as a metamodel. A metamodel is a model used to build other models. That is, it is a model that prescribes how to specify models that comply with its general architecture and constraints on the relationships between allowable model elements. "Allowable" in this sense means that the element is somehow derived from and related to an element in the metamodel.

A metamodel can be used two ways. It may be used ro guide development of new models built "from scratch" as solutions to a problem or design need. Or it can be used as a specification to guide the selection of technology candidates to be used in implementation of the proposed solution. We will, for example, take each of the agent-based simulation technology platforms that Jelal has identified through his web research and assess its features and capabilities in light of its ability to express models that satisfy our metamodel.

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.

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.

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.

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...

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

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:

The System Effectiveness Analysis Simulation (SEAS) is a government-owned, military utility analysis tool sponsored by Air Force Space Command, Space and Missile Systems Center, Directorate of Developmental Planning (SMC/XD). SEAS has been in development for over 10 years and was designed specifically to give military operations research analysts and decision makers a flexible means to quickly explore new warfighting capabilities; in particular, those provided by Space and C4ISR systems.

SEAS represents the latest in analytic simulation technology and offers a powerful agent-based modeling and simulation environment in which small-to large-scale joint warfighting scenarios can be constructed and explored to quantify the effectiveness of various system designs, architectures, and concept of operations (CONOPS). The ability to represent networked military units and platforms reacting and adapting to perception-based scenario dynamics in a 3-D physics-based Battlespace, makes SEAS ideally suited for exploring effects-based operations, network centric warfare, and transformational warfighting concepts.

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 modeling…At 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.

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.