Bottom-Up and Top-Down Approaches to Simulate Complex Social Phenomena

Bottom-Up and Top-Down Approaches to Simulate Complex Social Phenomena

Ahmed M'hamdi (Ibn Zohr University, Agadir, Morocco) and Mohamed Nemiche (Ibn Zohr University, Agadir, Morocco)
Copyright: © 2018 |Pages: 16
DOI: 10.4018/IJAEC.2018040101

Abstract

Social science research is concerned with the study of processes and phenomena in human societies, institutions and organizations. Social phenomena are complex due to many non-linear interactions between their elements. Social simulation represents a new paradigm for understanding social complexity with approaches that use advanced computational capabilities. The success of social simulation is largely due to its capability to test and validate hypotheses of social phenomena by the construction of virtual laboratories. This paper provides an introduction to social simulation and discusses approaches to model complex social phenomena.
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Introduction

Social science research is concerned with the study of processes and phenomena in human societies, institutions and organizations. However, this processes and phenomena in human societies are complex due to many non-linear interactions between their elements (Gilbert, 2004). Social simulation can help to understand, test and validate hypotheses of social phenomena by the construction of virtual social laboratories. Social simulation is a research field emerging from the intersection of computer science, statistics and social sciences in which new computer and mathematical methods are used to answer societal questions (Sallach & Macal, 2001). The field is intrinsically collaborative: social scientists provide a vital context and insight into relevant research questions, data sources and methods of acquisition while statisticians and computer scientists provide expertise, in developing a mathematical model and computer tools. The use of computers in social sciences is almost as old as computers in general. This is partly due to the fact that some of the pioneers of computer science, such as John Van Newman who was among the founders of game theory, were at the time the pioneers in the formulation of social sciences (Von Neumann & Morgenstern, 1947). In addition, Herbert A. Simon, one of the pioneers in the formalization of social sciences, was among the first to adopt computer-assisted methods to construct social theories (Simon, 1959).

Social simulation is derived from the interests of psychologists, sociologists, anthropologists and some economists who are interested in the study of behavioral patterns and social phenomena (Axelrod, 1997b; Billari et al., 2008). It allows the analysis of structures and social organizations regrouping a set of actors (individuals, animals, etc.) in interaction. Simulation is described as the third way of “doing science”, complementary to the two standard methods in social sciences, induction and deduction (Axelrod, 1997a, Blaschke, 2008 and Ostrom, 1988). According to Gilbert and Troitzsch (Gilbert, 1999), the goal of simulation is to better understand a phenomenon or to predict the evolution of a system. Simulation also allows us to study quite finely dynamic processes that naturally take into account the temporal evolution. Researchers have often been interested in simulation as a method of developing and testing social theories. Hence, Nigel Gilbert and Doran (Gilbert & Doran, 1994) demonstrated that computer simulation is an appropriate methodology whenever a social phenomenon is not directly accessible.

Social simulation represents a new paradigm for understanding social complexity. It applies approaches that use advanced computational capabilities (Cioffi-Revilla, 2014). Social simulation refers to many computer-based tools (Suleiman, Troitzsch & Gilbert, 2012) ranging from information extraction, algorithms to computer simulation models, as well as concepts and theories. Social simulation also uses statistical and mathematical methods (De Marchi, 2005), and in some cases other methods such as geo-spatial methods (Crooks & Castle, 2012) visualization (Grignard & Drogoul, 2017) to understand social complexity. This new paradigm allows the study of dynamics of all sizes of social groups (Axelrod, 1997a; Billari et al., 2008; M’hamdi et al., 2017; Nemiche & Pla-Lopez, 2000; Nemiche & Pla-Lopez, 2003; Nowak & Lewenstein, 1996; Pla-López, 1989, Pla-Lopez, 2007; Turchin, Currie et al., 2013). Social simulation is a subfield of Computational Social Science (CSS) as well as Analysis of Social Networks and Complex Systems which are the most well-known research axes in CSS (Cioffi-Revilla, C, 2014). The role of CSS is to formalize theory in order to express, study, experiment, and develop our understanding of social complexity which is something inaccessible by the traditional methods of social sciences (Axelrod, 1997a; Cioffi-Revilla, 2014; Ostrom, 1988).

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