The Local Food Economy Game is an applied research and social action project of Sohodojo. Our goal is to increase the production and consumption of wholesome foods grown and sold locally. We are developing a web-based exploratory learning environment where folks can have fun while deepening their appreciation of the social and economic impacts of "Buy Fresh, Buy Local."

"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.

Another interesting use of agent-based modeling: Theme parks

This paragraph describes the construction and uses of an agent-based model of an amusement park, used to maximize the uses of all rides in the park.

An interesting use of agent-based modeling

The following paragraph talks about TRANSIMS, an interesting application of agent-based simulation technology:

A team from LANL's Technology and Safety Assessment Division has developed a traffic simulation software package to create products that can be deployed to metropolitan planning agencies nationwide. The TRansportation ANalysis SIMulation System (TRANSIMS) ABM package provides planners with a synthetic population's daily activity patterns (such as travel to work, shop, and recreation, etc.), simulates the movements of individual vehicles on a regional transportation network, and estimates air pollution emissions generated by vehicle movements. Travel information is derived from actual census and survey data for specific tracts in target cities, providing a more accurate sense of the movements and daily routines of real people as they negotiate a full day with various transportation options available to them. TRANSIMS is based on (and contributes to the further development of) advanced computer simulation codes developed by Lawrence Livermore National Laboratory for military applications. TRANSIMS models create a virtual metropolitan region with a complete representation of the region's individuals, their activities, and the transportation infrastructure. Trips are planned to satisfy the individuals' activity patterns. TRANSIMS then simulates the movement of individuals across the transportation network, including their use of vehicles such as cars or buses, on a second-by-second basis. This virtual world of travelers mimics the traveling and driving behavior of real people in the region. The interactions of individual vehicles produce realistic traffic dynamics from which analysts using TRANSIMS can estimate vehicle emissions and judge the overall performance of the transportation system. Previous transportation planning surveyed people about elements of their trips such as origins, destinations, routes, timing, and forms of transportation used, or modes. TRANSIMS starts with data about people's activities and the trips they take to carry out those activities, then builds a model of household and activity demand. The model forecasts how changes in transportation policy or infrastructure might affect those activities and trips. TRANSIMS tries to capture every important interaction between travel subsystems, such as an individual's activity plans and congestion on the transportation system. For instance, when a trip takes too long, people find other routes, change from car to bus or vice versa, leave at different times, or decide not to engage in a certain activity at a given location. Also, because TRANSIMS tracks individual travelers – locations, routes, modes taken, and how well their travel plans are executed – it can evaluate transportation alternatives and reliability to determine who might benefit and who might be adversely affected by transportation changes.

Agent based simulation of artifical electricity markets

Located here is an article about a program designed to simulate artifical electricity markets. In a software program called Power Agents, consumers and producers are modeled as "optimizing goal-seeking" agents in a an artificial electricy distribution market. Consumers respond to price changes by maximizing their subjective valuation of the electrical services provided.

A similar program could give us the very basics of what we are looking for--one could see how independent, utility-maximizing agents respond to a change in price of certain foods, because of, say, a change in where the food comes from.


Given the positive reviews of the agent-based modeling platform NetLogo contained in the article "Agent-Based Simulation Platforms: Review and Development Recommendations" (see my earlier blog about this article), it is worthwhile to add a direct link to NetLogo. From this page, one can read about and download the program. Once downloaded, one can play around with the sample models that come along with NetLogo (more on this later).

Agent-Based Modeling Resources

Thus far, the two best resources I've found for agent-based modeling are:

Integrated Development Environments: Eclipse

Several of the articles that I've been reading about Agent-Based Simulation have referenced Integrated Development Environments (IDEs)--in particular, Eclipse--as a tool for constructing simulation environments. "They offer such benefits as editors that automatically format code, finding and helping fix mistakes, automating common tasks such as writing import statements and getter and setter methods, and debuggers that let you step through a code's execution to find mistakes and understand what your model is doing" ( Eclipse, specifically, "is an open source community whose projects are focused on providing an extensible development platform and application frameworks for building software" (


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