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. 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. For each category, one or several business applications are described and analyzed. 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, which relies on the power of computers to explore dynamics out of the reach of pure mathematical methods (1, 2). 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 (3) 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. Sophisticated ABM sometimes incorporates neural networks, evolutionary algorithms, or other learning techniques to allow realistic learning and adaptation.ABM is a mindset more than a technology. The ABM mindset consists of describing a system from the perspective of its constituent units. 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. A synonym of ABM would be microscopic modeling, and an alternative would be macroscopic modeling. 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. 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. What the reader will be able to take home is a clear view of when and how to use ABM. 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. Because the technique is easy to use, one may wrongly think the concepts are easy to master. But although ABM is technically simple, it is also conceptually deep. This unusual combination often leads to improper use of ABM.Benefits of Agent-Based Modeling. The benefits of ABM over other modeling techniques can be captured in three statements: (i) ABM captures emergent phenomena; (ii) ABM provides a natural desc...

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'any collective activities p3farmed by social insects result ial complex spatiotemporal patterns. Ethologists are often tempted to assume that such complex patterns at the colony level can be generated only by complex individuals, that is, by i~(~~vic~i~a~~ who are able to take into account nmerous parameters to moclu~ate their behaviours. Theories of sel8-o~ganlzatlon (SO) (originally developed in the context of physics and chemistry in order to describe the emergence of macroscopic patterns out of prucesses and interactiotis defined at the microscopic level',") can be extended to ethol;gical systems, particularly social insects, to show that complex collective behaviours may emerge from interactions among individuals that exhibit simple behaviours. In these cases, there is no need to invoke individual complexity.Recent research shows that SO is indeed a major component of a wide range of collective phenomena in social insects:'. But work on SO in insect societies, and more genrerally in ethology, is c&ly overlooked IaWause the emphasis of SO is on how'i collective behaviours Eric i3onabeau is at the Santa Fe Institute.

Research in social insect behaviour has provided computer scientists with powerful methods for designing distributed control and optimization algorithms. These techniques are being applied successfully to a variety of scientific and engineering problems. In addition to achieving good performance on a wide spectrum of 'static' problems, such techniques tend to exhibit a high degree of flexibility and robustness in a dynamic environment.

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A model of division of labour in insect societies, based on variable response thresholds is introduced. Response thresholds refer to the likelihood of reacting to task-associated stimuli. Low-threshold individuals perform tasks at a lower level of stimulus than high-threshold individuals. Within individual workers, performing a given task induces a decrease in the corresponding threshold, and not performing the task induces an increase in the threshold. This combined reinforcement process leads to the emergence of specialized workers, i.e. workers that are more responsive to stimuli associated with particular task requirements, from a group of initially identical individuals. Predictions of the dynamics of task specialization resulting from this model are presented. Predictions are also made as to what should be observed when specialists of a given task are removed from the colony and reintroduced after a varying amount of time: the colony does not recover the same state as that prior to the perturbation, and the di¡erence between before and after the perturbation is more strongly marked as the time between separation and reintroduction increases.

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