This chapter looks at how the concept of ‘context’ is interpreted in complexity sciences, where it typically plays a very different role from that given to it in other branches of policy studies. When complexity theory is applied to social systems, we are typically dealing with self-organising systems, which do not have an obvious ‘leader’ or ‘network manager’. In particular, complexity theory applies to that class of systems, known as ‘complex adaptive systems’, where the interconnectedness of the agents produces a dynamic interaction of agents that simultaneously react to and create their environment. These are ‘systems which are “more than most” dynamic, self-organising, environment shaping (through dynamic interactions of agents) and sensitive to initial conditions’ (Teisman and Klijn, 2008). The environment of such systems is therefore not a ‘given’ but rather a co-created ‘fitness landscape’, in which the agents most likely to flourish are those who can most readily adapt to changing circumstances and influence the behaviour of others (Kaufman, 1995). This concept fits with the analysis of Weick (1995) and Luhmann (1995) on autopoiesis, although it is only one potential cause of autopoiesis. This chapter considers the implications of this phenomenon for public policy, and in particular for attempts to model public policy outcomes through ‘logic chain’ or ‘cause-and-effect’ analysis. It concludes by considering the extent to which public policy is likely to be dealing with complex adaptive systems in the real world.
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