Edited by Robert Geyer and Paul Cairney
Chapter 14: Using agent-based modelling to inform policy for complex domains
Complex systems have been a topic of study in the natural sciences for decades. Practitioners in physics, chemistry, biology, mathematics, meteorology and engineering (Braha et al., 2006; Flake, 1998; Gell-Mann, 1994; Gleick, 1988; Nicolis and Prigogine, 1989; Strogatz, 2000; von Bertalanffy, 1969; Callebaut and Rasskin-Gutman, 2005) have used the concept of complex systems to explain phenomena as diverse as phase transitions in physical matter, immune system functions, and weather patterns. These systems have been modeled using, for the most part, the concept of dynamical systems and nonlinear equations. Recently, social scientists have started experimenting with complex systems tools developed in physics, mathematics and engineering in order to better understand the nature of issues that our society is facing today. Unlike natural systems, social systems involve the active participation of system elements (they possess ‘will’). Subsequently, tremendous progress has been made in applying the methodology of complex adaptive systems (CAS) to economics, sociology, transportation, warfare, decision making and other disciplines (Axelrod and Cohen, 1999; Bonabeau et al., 1999; Buchanan, 2002; 2007; Capra, 1982; 2002; Dooley, 1997; Durlauf and Young, 2001; Epstein, 2006; Gell-Mann, 1994; 1995; Gribbin, 2004; Innes and Booher, 1999; Johnson, 2007; Hazy et al., 2007; Holland, 1992a; 1992b; 1995; 1998; Kauffman, 1993; 1995; 2000; 2008; Khlebopros et al., 2007; Kohler and Gumerman, 2000; Kollman et al., 2003; Krugman, 1996; Langton, 1995; Suleiman et al., 2000; Waldrop, 1993).
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