Simulation in Sociology

Abstract

Simulation has a long and checkered history in areas of substantive interest to sociology, from before Forrester's model of overpopulation to up-to-the-minute approaches based on complexity theory or distributed artificial intelligence. Although in some respects it has failed to live up to its inflated promise, it offers nonetheless a very useful paradigm. Moreover, advancing simulation technology offers some advantages, particularly the modelling of macro-micro links too complex to deal with linguistically or mathematically. This article briefly reviews the history of simulation in sociology and goes on to consider in more detail specific areas such as system dynamics, cellular automata, iterated game theory, distributed artificial intelligence, neural networks, multilevel simulation, simulation of social networks and organizations, and policy-oriented tax-benefit microsimulation. It concludes with a consideration of the role of statistics in simulation and the very good potential for expanded use of simulation in sociology.

When social computing meets soft computing: opportunities and insights

“…Second, computational social science is a cross-discipline among systems science, control science, and complex science. It mainly focuses on the research on social simulation and social system modeling by using equation based modeling and computational modeling [12]. Technically, data mining as a key technology for computational social science, is to discover the interesting and useful patterns from massive data by using machine learning approaches.…”

Mixed qualitative-simulation methods

“…Despite early calls to avoid an infatuation for emergence (e.g. Halpin, 1999) and the more metaphorical elements of complexity theory (Thrift, 1999), since the turn of the 21st century the bottomup approach has prevailed in agent-based simulation. Although the one-way, bottom-up approach provides a useful means to understand how patterns are generated, it need not be the only means to understand complex processes.…”

Agents: Agenthood, Agent Architectures, and Agent Taxonomies

“…In all these areas, the description of human actors have traditionally played a central role. The agent metaphor could build on well-established approaches like microsimulation in sociology (Halpin, 1999), individual-based modeling in ecology (Grimm and Railsback, 2005), or micro models of traffic systems (Pursula, 1999).…”