Adding ABM capabilities add further computational infrastructure. Creating a simulation from scratch is computationally intensive. Again, the application review is not comprehensive but selective, intended to motivate the wide-ranging applicability of ABM.įinally, we provide some how-to-get-started information. The general applicability of simulation, and the performance of modern computers, means simulation can be used in not only the descriptive role for which it is generally intended, but also in a prescriptive role with the addition of simulation-based optimization modules. Simulation is a powerful, general purpose analytical tool, more often than not listed as one of two favored tools among analysts (statistical analysis or regression modeling being the other favored tool). We then move onto recounting some of the applications of ABM. Rather, we recount some of the influences we view as key to the development of the current ABM paradigm. This background is not intended to be a definitive history of agent-based modeling, as once again such a history will have many versions based on the background and fundamental simulation beliefs of the writer of the history. We start the tutorial with some definitions of agent-based modeling and provide a view of the background work that has led to the current state of agent-based modeling. Purpose of the Simulation Taxonomy useful for determining when to use an agent-based modeling(Heath, Ciarallo, and Hill 2009). For this tutorial we use the agent-based modeling term throughout but discuss some of the rationale for use of the other terms. Other labels for the paradigm we discuss include agent-based simulation, complex adaptive simulation systems, even object-oriented simulation. Those with familiarity of the field might note that the term agent-based modeling is not the standard term. This tutorial provides background, application context and a how-to-get-started look at the simulation paradigm known as agent-based modeling (ABM). This tutorial provides an introduction to tools and resources for prospective modelers, and illustrates agent-based model flexibility with a basic war-gaming example. Agent-based models have been applied successfully in a broad variety of areas, including heuristic search methods, social science models, combat modeling, and supply chains. This flexibility makes them ideal as virtual laboratories and testbeds, particularly in the social sciences where direct experimentation may be infeasible or unethical. This is often a more natural perspective than the system-level perspective required of other modeling paradigms, and it allows greater flexibility to use agents in novel applications. Basing a model around agents (building an agent-based model) allows the user to build complex models from the bottom up by specifying agent behaviors and the environment within which they operate. Agents are self-contained objects within a software model that are capable of autonomously interacting with the environment and with other agents.
0 Comments
Leave a Reply. |
Details
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |