![]() Numerous examples of counterintuitive emergent phenomena will be described in the following sections. This characteristic of emergent phenomena makes them difficult to understand and predict: emergent phenomena can be counterintuitive. For example, a traffic jam, which results from the behavior of and interactions between individual vehicle drivers, may be moving in the direction opposite that of the cars that cause it. An emergent phenomenon can have properties that are decoupled from the properties of the part. By definition, they cannot be reduced to the system's parts: the whole is more than the sum of its parts because of the interactions between the parts. Emergent phenomena result from the interactions of individual entities. It is clear, however, that the ability of ABM to deal with emergent phenomena is what drives the other benefits. The benefits of ABM over other modeling techniques can be captured in three statements: (i) ABM captures emergent phenomena (ii) ABM provides a natural description of a system and (iii) ABM is flexible. This unusual combination often leads to improper use of ABM. But although ABM is technically simple, it is also conceptually deep. Because the technique is easy to use, one may wrongly think the concepts are easy to master. One of the reasons underlying ABM's popularity is its ease of implementation: indeed, once one has heard about ABM, it is easy to program an agent-based model. What the reader will be able to take home is a clear view of when and how to use ABM. These are the questions this paper addresses, first by reviewing and classifying the benefits of ABM and then by providing a variety of examples in which the benefits will be clearly described. As the ABM mindset is starting to enjoy significant popularity, it is a good time to redefine why it is useful and when ABM should be used. ![]() A synonym of ABM would be microscopic modeling, and an alternative would be macroscopic modeling. A number of researchers think that the alternative to ABM is traditional differential equation modeling this is wrong, as a set of differential equations, each describing the dynamics of one of the system's constituent units, is an agent-based model. The ABM mindset consists of describing a system from the perspective of its constituent units. Sophisticated ABM sometimes incorporates neural networks, evolutionary algorithms, or other learning techniques to allow realistic learning and adaptation. In addition, agents may be capable of evolving, allowing unanticipated behaviors to emerge. Even a simple agent-based model can exhibit complex behavior patterns (3) and provide valuable information about the dynamics of the real-world system that it emulates. At the simplest level, an agent-based model consists of a system of agents and the relationships between them. ![]() Repetitive competitive interactions between agents are a feature of agent-based modeling, which relies on the power of computers to explore dynamics out of the reach of pure mathematical methods (1, 2). Agents may execute various behaviors appropriate for the system they represent-for example, producing, consuming, or selling. Each agent individually assesses its situation and makes decisions on the basis of a set of rules. ![]() ![]() I n agent-based modeling (ABM), a system is modeled as a collection of autonomous decision-making entities called agents. For each category, one or several business applications are described and analyzed. After the basic principles of agent-based simulation are briefly introduced, its four areas of application are discussed by using real-world applications: flow simulation, organizational simulation, market simulation, and diffusion simulation. Agent-based modeling is a powerful simulation modeling technique that has seen a number of applications in the last few years, including applications to real-world business problems. ![]()
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