Artificially in Social Sciences

Artificially in Social Sciences

J. Rennard (Grenoble Graduate School of Business, France)
DOI: 10.4018/978-1-59140-984-7.ch001
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Abstract

This chapter provides an introduction to the modern approach of artificiality and simulation in social sciences. It presents the relationship between complexity and artificiality, before introducing the field of artificial societies which greatly benefited from the fast increase of computer power, gifting social sciences with formalization and experimentation tools previously owned by the “hard” sciences alone. It shows that as “a new way of doing social sciences,” artificial societies should undoubtedly contribute to a renewed approach in the study of sociality and should play a significant part in the elaboration of original theories of social phenomena.

Key Terms in this Chapter

Emergence: Characterizes the properties of a system which are new compared to the properties of the components isolated. Bottom-up modeling mainly deals with Bedau’s weak emergence which characterizes emerging properties that can only be derived by simulation.

Macro to Micro Problem: How to describe the relationship between macro-phenomena characterizing the dynamic of a system as a whole and micro-phenomena characterizing the dynamic of the components of the system.

GOFAI: Good Old-Fashioned Artificial Intelligence characterizes the traditional symbol-based artificial intelligence.

Complex Adaptive Systems: Complex systems where agents can learn and modify their rules according to their previous success.

Synthetic Method: The synthetic method starts like induction from the observed facts and the inferred theory (but it can also start like deduction from a set of assumptions). On this basis, the synthetic method engineers an artificial system, the objective being that, while operating, this system will behave like the real one, thus confirming the tested theory.

Bottom-Up Modeling: Applied to non-linear systems, bottom-up modeling is based on the gathering of components in order to analyze the corresponding emerging properties.

Cellular Automata: Lattice of sites whose states—belonging to a finite set—evolve in discrete time step according to rules depending on the states of the neighbors’ sites.

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