Читать книгу Agent-Based Models - Nigel Gilbert - Страница 8

Agent-based modeling is a form of computational simulation. Although simulation as a research technique has had a very important part to play in the natural sciences for decades in disciplines from astronomy to biochemistry, it was relatively neglected in the social sciences. This may have been because a computational approach that respected the particular needs of the social sciences was lacking. However, in the early 1990s the value of agent-based modeling began to be realized, and, since then, the number of studies that have used agent-based modeling has grown rapidly (Hauke, Lorscheid, & Meyer, 2017).

Agent-based modeling is particularly suited to topics where understanding processes and their consequences is important. In essence, one creates a computer program in which the actors are represented by segments of program code, and then runs the program, observing what it does over the course of simulated time. There is a direct correspondence between the actors being modeled and the agents in the program, which makes the method intuitively appealing, especially to those brought up in a generation used to computer games. Nevertheless, agent-based modeling stands beside mathematical and statistical modeling in terms of its rigor. Like equation-based modeling, but unlike prose, agent-based models must be complete, consistent, and unambiguous if they are to be capable of being executed on a computer. On the other hand, unlike most mathematical models, agent-based models can include agents that are heterogeneous in their features and abilities, can model situations that are far from equilibrium, and can deal directly with the consequences of interaction between agents.

Because it is a new approach, there are few courses yet available to teach the skills of agent-based modeling, although the number is increasing, and there are few texts directed specifically at the interested social scientist. This short book introduces the subject, emphasizing the decisions that a social scientist needs to make when selecting agent-based modeling as an appropriate method, and offering some tips on how to proceed. It is aimed at practicing social scientists and graduate students. It has been used as the recommended reading on agent-based modeling for a graduate-level module or doctoral program in computational social science, and it is also suitable as background reading in postgraduate courses on advanced social research methods. It would be a good preparation for any of the textbooks that provide a more in-depth guide to agent-based modeling (e.g., Hamill & Gilbert, 2015; Heppenstall, Crooks, See, & Batty, 2012; O’Sullivan & Perry, 2013; Railsback & Grimm, 2012; Squazzoni, 2012; Wilensky & Rand, 2015).

A knowledge of and experience with computer programming in any language would be helpful but is not essential to understand the book.

The book concludes with a list of printed and Web resources, a glossary, and a reference section. (The glossary terms will appear in bold at first use in the text.) Because the field is growing so rapidly, it has been possible to mention only a few examples of current research and some textbooks that provide more detail on some topics. There is much more that could have been cited if there had been space. In particular, the book mentions only briefly two closely linked areas: network models and game theory models, both of which are covered in much more detail in other SAGE volumes such as Knoke and Yang (2008) and Fink, Gates, and Humes (1998).

A website to accompany the book at study.sagepub.com/researchmethods/qass/gilbert-agent-based-models-2e includes an annotated exemplar model using NetLogo.