Agent-based modeling in the social and behavioral sciences

2005

Agent-based modelling (ABM) is the computational study of social agents as evolving systems of autonomous interacting agents. ABM is a tool for the study of social systems from the complex adaptive system perspective. From this perspective, the researcher is interested in how macro phenomena are emerging from micro level behaviour among a heterogeneous set of interacting agents (Holland, 1992).

Environment and Planning B-planning & Design, 2007

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. I n 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 description of a system; and (iii) ABM is flexible. It is clear, however, that the ability of ABM to deal with emergent phenomena is what drives the other benefits. ABM captures emergent phenomena. Emergent phenomena result from the interactions of individual entities. 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. An emergent phenomenon can have properties that are decoupled from the properties of the part. 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. This characteristic of emergent phenomena makes them difficult to understand and predict: emergent phenomena can be counterintuitive. Numerous examples of counterintuitive emergent phenomena will be described in the following sections. ABM is, by its very nature, the canonical approach to modeling emergent phenomena: in ABM, one models and simulates the behavior of the system's constituent units (the agents) and their interactions, capturing emergence from the bottom up when the simulation is run. Here is a simple example of an emergent phenomenon involving humans. It is a game that is easy to play with a group of 10–40 people. One asks each member of the audience to randomly select two individuals, person A and person B. One then asks them to move so they always keep A between them and B so A is their protector from B. Everyone in the room will mill about in a seemingly random fashion and will soon begin to ask why they are doing this. One then asks them to move so that they keep themselves in between A and B (they are the Protector). The results are striking: almost instantaneously the whole room will implode, with everyone clustering in a tight knot. This example shows how simple individual rules can lead to coherent group behavior, how small changes in those rules can have a dramatic impact on the group behavior, and how intuition can be a very poor guide to outcomes beyond a very limited level of complexity. The group's collective behavior is an emergent phenomenon. By using a simple agent-based simulation (available at www.icosystem.comgame.htm) in which each person is modeled as an autonomous agent following the rules, one can actually predict the emerging collective behavior. Although this is a simple example, where individual behavior does not change over time, ABM enables one to deal with more complex individual behavior, including learning and adaptation. One may want to use ABM when there is potential for emergent phenomena, i.e., when: Y Individual behavior is nonlinear and can be characterized by thresholds, if-then rules, or nonlinear coupling. Describing discontinuity in individual behavior is difficult with differential equations.

2007

Agent-based modeling (ABM) is a technique increasingly used in a broad range of social sciences. It involves building a computational model consisting of “agents,” each of which represents an actor in the social world, and an" environment" in which the agents act. Agents are able to interact with each other and are programmed to be pro-active, autonomous and able to perceive their virtual world.

Lecture Notes in Computer Science, 2006

This thesis proposal aims to provide a new approach to the study of complex adaptive systems in social sciences through a methodological framework for modeling and simulating these systems like artificial societies. Agent based modeling (ABM) is well fitted for the study of social systems as it focuses on how local interactions among agents generate emergent larger and global social structures and patterns of behavior. The issues addressed by our framework are presented as well as its most important components.