Knowledge and media history of complexity

Project leader Prof. Dr. Manfred Laubichler

We have identified the notion of complex adaptive systems (CAS) as an alternative to the dominant paradigm of control. Complex adaptive systems theory has emerged over the last thirty years as a unifying explanatory framework across a number of disciplines ranging from biology, medicine and sociology to economics, computer science and technology. It is now widely recognized that almost all systems that affect sustainability are indeed complex adaptive systems. Of special interest is the co-dependency and co-evolution between increasingly complex sociality (including technology) and biology as a distinguishing characteristic of our species, with numerous consequences for sustainability health and longevity, and disease (Laland et al 2000, 2010, 2013; Lewontin 1983).

The interactions between human society and biology are mediated by varied environmental contexts of course. And the role of the environment is further complicated by the fact that many aspects our environment—including some that are especially relevant for health and disease—are in fact socially constructed. Humans are quintessential exemplars of niche constructionists to the point that today, many environmental scientists are using the term Anthropocene to refer to the current geological era and talk about a domesticated earth (Crutzen 2006; Kareiva et al 2007; Smith and Zeder 2013; Wilkinson 2005).

Science has long benefitted from a protocol of systematically isolating and studying in detail critical subsets of real-world systems, including different components of human biology and the environment. However, there is a growing realization that it is equally important to understand and ultimately manage the many interactions among different biological components, socially mediated behaviors, and the human-constructed niche in which we live (Green 2005; Homer and Hirsch 2006; Leischow and Milstein 2006; Mabry et al. 2010; Maligo and Mabry 2011; Epstein 2006). Conducting such research and applying its insights involves conceptual and methodological frameworks that differ from those of reductionist science, as well as new supporting technologies. These interdisciplinary approaches to interactions between human society, biology, and environment increasingly combine complex adaptive systems concepts and computational modeling methods that we gloss here as systems science for short (Galea et al 2008; Luke and Stamatakis 2012; Miller and Page 2007; Mitchell 2009; Trochim et al 2005; Simon 1962).

Systems science applies a bottom up perspective to social and biological systems, focusing especially on the dynamic interactions between system components across multiple scales of integration. The human body is not composed of an undifferentiated mass of cells, nor are societies made up of unorganized groups of people. Rather, components in complex systems, like our bodies or societies, are hierarchically organized into clusters of interacting components, which in turn, interact with other clusters at more inclusive levels of integration. Important consequences of this kind of organization and multi-level feedback, recognized by systems science are that cause and effect can often be non-linear, and that novel properties can emerge at higher levels of integration in a system that cannot be predicted from the properties of the constituent components (Maglio and Mabry 2011; Axtell 2000; Mitchell 2009; Simon 1962).

Computational and mathematical modeling is commonly employed in systems science because the combination of many, simultaneously operating interaction at multiple scales often exceeds the intuitive capacity of a single researcher, and is compounded by the potential for non-linear dynamics and emergent properties (Epstein 2006; Borshchev and Filippov 2004). While such modeling can simulate the multi-scale interactions in complex systems in varying levels of detail, a potentially more powerful application of systems science uses different modeling platforms as environments for systematic, controlled, digital experiments in the dynamics of bio-social-environmental interactions that are at core of all sustainability challenges (Barton et al 2012; van der Leeuw 2004). Building computational models in a systems science framework can help refine and explore concepts about biological, social, and environmental systems, and generate complex hypotheses that can be tested against empirical datasets. In this sense, modeling is a kind of theory building for systems science in particular application contexts. The specific challenge for this project is to investigate how these trends in complexity theory apply to problems of sustainability and whether the theory of complex systems is a more adequate conceptual and methodological foundation for sustainability science that the prevailing notions of control and technological intervention.

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