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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.

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:

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.

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.

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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.

Bilge, Venables, and Casti have developed an agent-based model of a supermarket. SIMSTORE is a model of a real British supermarket, the Sainsbury's store at South Ruislip in West London. The agents in SIMSTORE are software shoppers armed with shopping lists. They make their way around the silicon store, picking goods off the shelves according to rules such as the nearest-neighbor principle: "Wherever you are now, go to the location of the nearest item on your shopping list." Using these rules, SIMSTORE generates the paths taken by customers, from which it can calculate customer densities at each location.

It is also possible to link all points visited by, say, at least 30% of customers to form a most popular path. An optimization algorithm can then change where in the supermarket different goods are stacked and so minimize, or maximize, the length of the average shopping path. Shoppers, of course, do not want to waste time, so they want the shortest path. But the store manager would like to have them pass by almost every shelf to encourage impulse buying. So there is a dynamic tension between the minimal and maximal shopping paths. This model was originally aimed at helping Sainsbury's to redesign its stores to generate greater customer throughput, reduce inventories, and shorten the time that products are on the shelves.

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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.

Another application of ABM to flow management is the simulation of customer behavior in a theme park or supermarket. The collective patterns generated by thousands of customers can be extremely complex as customers interact: for example, how long one waits at an attraction in a theme park depends on other people's choices. A major theme park resort company was thinking about how to improve adaptability in labor scheduling, but knew that this depended on knowing more about the optimal balance of capacity and demand. Axtell and Epstein developed ResortScape (13), an agent-based model of the park that provides an integrated picture of the environment and all of the interacting elements that come into play in such a resort. The model provides a fast in silico way for managers to identify, adjust, and watch the impact of any number of management levers such as:

  • When or whether to turn off a particular ride.
  • How to distribute rides per capita throughout the park space.
  • What is the tolerance level for wait times.
  • When to extend operating hours.

In the simulation, agents represent a realistic and changeable mix of both supply (attractions, shops, food concessions) and demand (visitors with different preferences) elements of a day at the park. Leveraging existing resources and data, such as customer surveys, segmentation studies, queue timers, people counters, attendance estimates, and capacity figures, the model generates information about guest flow. Users can design and run an infinite number of scenarios to study the dynamics of the park space, test the effectiveness of various management decisions, and track visitor satisfaction throughout the day.

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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.

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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.

Artificial Microeconomy Simulation Platform

There is an interesting program located here:

The program is simplistic, using "food" and "gold" as the only two commodities. Actors are very limited in their actions, and allotment of each good is random. However, it is worth checking out, as it models what we are trying to do--only in a more simplistic manner.

A detailed description of the model is presented in a paper by Ken Steiglitz, Michael Honig, and Leonard Cohen, located at: