The genealogy of complexity can be traced to the natural sciences, notably physics, mathematics, cybernetics, systems science and biology (Kauffman 1995; Mitchell 2009). While complexity builds upon several strands of work, it was the earlier work of chaos theory, which provides a mathematical vocabulary for complexity researchers, that the new science of complexity began to emerge (Kauffman 1995; Mitchell 2009).
Chaos theory first came to light in 1961 through the work of a meteorologist, Edward Lorenz (Gleick 1987). Lorenz was trying to predict the weather by running a series of computations on his computer. When he came to repeating his computations, he tried to save time by rounding some of his starting numbers and found that the difference produced an entirely different weather forecast. This was the origin of the principle ‘sensitivity to initial conditions’ popularly referred to as the ‘butterfly effect’. By the time of Lorenz’s discovery, non-linear equations
had been around for a long time, but no one was able to solve them (Gleick 1987). Traditional Newtonian science, largely based on linearity, order and stability, was being challenged by this new theory based on polar-opposites – non-linearity, disorder and instability (Elliott & Kiel 1997). Some of the earlier work was so revolutionary that papers were often rejected for publication (Gleick 1987). By the mid-1970s, chaos as a new science had emerged and has since been applied in areas such as medicine, to explain the beating of the heart and spread of diseases, and economics to explain the movements in the stock market (Gleick 1987). Although Lorenz’s discovery was an accident, it became the catalyst to create a paradigm shift and create the new science of chaos theory that later provided a core building block to the broader science of complexity (Gleick 1987; Kauffman 1995; Mitchell 2009).
In 1984, the new science of complexity finally emerged following the creation of the Sante Fe Institute, a dedicated research centre to study complex systems, by a group of prominent scientists and mathematicians (Kauffman 1995; Mitchell 2009). Early pioneers of complexity research include Murray Gell-Mann (physicist), John Holland (physicist, mathematician, computer scientist and psychologist) and Stuart Kauffman (theoretical biologist) (Mitchell 2009). Since then, other research centres have emerged, notably the New England Complex Systems Institute (‘NECSI’) in 1996. Shortly thereafter, in 1997, NECSI’s founding President, Yanner Bar-Yam (physicist), published a comprehensive and technical book called Dynamics of Complex Systems and remains one of the leading experts in the field of complexity. Bar-Yam largely uses mathematical models to study ‘how interactions lead to patterns of behaviour’ (NECSI 2017). Today, these Institutes (Sante Fe Institute 2017; NECSI 2017) are at the forefront of complexity research and are attempting to solve some of the hardest, most fundamental and most challenging problems in today’s world of science and society.
In summary, since the emergence of the science of complexity some thirty years ago, today, complexity researchers are leading the way in twenty-first century science.
3.3.2 Social sciences
Historically, the social sciences have a long history of borrowing theories, concepts and principles from the natural sciences (Maxfield & Babbie 2014). It was
following the publication of James Gleick’s 1987 book Chaos, Making a New Science, that chaos theory first caught the attention of social scientists and the general public alike (Williams & Arrigo 2002). This seminal book was followed five years later in close succession by Roger Lewin's 1992 book, Complexity: Life at the Edge of Chaos, Mitchell Waldrop's 1992 book, Complexity: The Emerging Science at the Edge of Order and Chaos and Stuart Kauffman’s 1993 book, The Origins of Order: Self- Organisation and Selection in Evolution. These authors did the same for complexity as Gleick did for chaos. In Gleick’s twentieth anniversary edition of his 1987 book, Gleick comments in an afterword section ‘what a difference twenty years make. The ideas of chaos have been adopted and internalised, not just by mainstream scientists, but also by the culture at large’ (Gleick 2008:319). Likewise, in Lewin’s (1999) second edition of his 1992 book, Lewin comments in the preface that since the first edition, ‘complexity science has become firmly established as an important field’.
Although complexity remains a relatively new science, its concepts have been borrowed and applied by social scientists since the early 1990s (ed. Milovanovic 1997; Milovanovic 1997a; eds Elliott & Kiel 1997; Williams & Arrigo 2002). TR Young emerged as an early leader in applying chaos theory to the social sciences (Milovanovic 1997a). Her work significantly influenced the work of others through the 1990s, notably Bruce Arrigo, Dragan Milovanovic, Hal Pepinsky and Robert Schehr (Milovanovic 1997a). In 1998, Paul Cilliers book, Complexity and Postmodernism, greatly influenced the application of complexity in the social sciences (Byrne & Callaghan 2014). Since then, a number of disparate attempts have been made over the last two decades, notably in the study of organisations, business, management, health, social work and politics (eds Eve et al 2007; Bousquet & Curtis 2011; Byrne & Callaghan 2014; eds Pycroft & Bartollas 2014). While some of these efforts are more influential than others, most are insufficiently advanced. Notably, where research is qualitative, the conceptual language is often developed to a ‘sophisticated degree within complexity’ but on application ‘a full appreciation of that underlying sophistication is absent or left unstated’ (Bousquet & Curtis 2011:44). In the social sciences, the availability of suitable quantitative data remains a key challenge, however, as argued by social scientists, complexity concepts may still be applied metaphorically (Hayles 1990; Marion 1999; Kellert 2008; Kuhn 2009; Wolf-Branigin 2013). For example, Wolf-Branigin (2013), suggests a number of complexity qualitative research methods in social program
evaluations. As Lee (1997:29) suggests, ‘a variety of languages and methods are actually necessary if we wish to produce an ongoing description of differing relationships that behave quite differently at differing levels’. Just because social science lacks time-series data, does not mean that ‘social science ought to downplay mathematical [complexity] and its insights’ (Dendrinos 1997:242). In modelling social processes, lack of adequate and suitable data is always a problem, but using complexity concepts to social dynamics is still ‘helpful’ (Lee 1997:29). For example, ‘the inherent non-linearity of many social phenomena’ demonstrates the value of the application of complexity to the social sciences (Elliott & Kiel 1997:4). While there are both pros and cons in using complexity to understand human and social phenomena, complexity ideas have certainly reconfigured social scientists’ understanding of what is ‘normal’ and what is not. As Waldrop (1992:12) points out, ‘there is more here than just a series of nice analogies’. Notably, in Byrne and Callaghan’s (2014:213) recent book, Complexity Theory and the Social Sciences, they comment on ‘how complexity is becoming part of what might best be called the intellectual culture’ in today’s social sciences and new ways of thinking. Williams and Arrigo (2002:3) sum up the application of complexity as directing ‘our attention to those previously disregarded factors of human social interaction, defined as anomalies, inconsistencies, or “noise” in a system’. Bousquet and Curtis (2011:48) further note that ‘the turn towards complexity in the social sciences has been partly driven by the growing realisation that non-linear and networked social relationships characterise much of the contemporary world’. As a dynamic theory, capturing movement and change, it can be argued that complexity is perhaps a more useful ally than more traditional theories of social phenomena (Eve et al 1997).
In summary, upon a review of the available literature, applying complexity to problems in the social sciences has certainly been influential, but not dramatic, unlike the impact in the natural sciences.