Social simulation

Social simulation is a research field that applies computational methods to study issues in the social sciences. The issues explored include problems in psychology,organizational behavior,sociology, political science, economics, anthropology, geography, engineering, archaeology and linguistics (Takahashi, Sallach & Rouchier 2007).

Social simulation aims to cross the gap between the descriptive approach used in the social sciences and the formal approach used in the natural sciences, by moving the focus on the processes/mechanisms/behaviors that build the social reality.

In social simulation, computers support human reasoning activities by executing these mechanisms. This field explores the simulation of societies as complex non-linear systems, which are difficult to study with classical mathematical equation-based models. Robert Axelrod regards social simulation as a third way of doing science, differing from both the deductive and inductive approach; generating data that can be analysed inductively, but coming from a rigorously specified set of rules rather than from direct measurement of the real world. Thus, simulating a phenomenon is akin to generating it - constructing artificial societies. These ambitious aims have encountered several criticisms.

The social simulation approach to the social sciences is promoted and coordinated by three regional associations, ESSA for Europe, North America (reorganizing under the new CSSS name), and PAAA Pacific Asia.

The history of the agent-based model can be traced back to the Von Neumann machine, a theoretical machine capable of reproducing itself. The device von Neumann proposed would follow precisely detailed instructions to fashion a copy of itself. The concept was then improved by von Neumann's friend Stanislaw Ulam, also a mathematician; Ulam suggested that the machine be built on paper, as a collection of cells on a grid. The idea intrigued von Neumann, who drew it up—creating the first of devices later termed cellular automata.

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