Imagine complexity: The past, present and future potential of complex thinking (original) (raw)
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Futures, 1996
is easy to overlook the fact that the apparent simplicity of the past was often more a function of the constraints put on the framing of the issue or problem at hand, both conceptually and in policy making, than it was a reflection of any inherent properties. Revisiting several case studies helps to illustrate the point that complexity, now or in the past, resides especially in the social relationships within and between institutions and agents. Much current thinking about complexity is moving towards development of ever more sophisticated methodologies with which to probe complex systems, hence to facilitate their management and control. We argue that such methodological elaboration frequently acts as a direct substitute for institutional development and reflexivity, and we urge instead for exploration of new forms of institutional mediation. Copyright 0 1996 Elsevier Science Ltd
Complexity, Governance & Networks, 2017
In this article, we present a critical discussion of complexity theory. We ask: what does it really offer policy studies? We suggest that its stated advantages-- interdisciplinarity, theoretical novelty, and empirical advance--are generally exaggerated and based more on hope than experience. In that context, we identify a cautiously positive role for complexity theory, primarily as a way to bridge academic and policymaker discussions by identifying the role of pragmatic responses to complexity in policymaking.
The governance of complexity and the complexity of governance
Putting Civil Society in Its Place
Civil society exists at the intersection of networks and solidarity as opposed to markets or command and its agents have the potential to guide markets and state action. It may serve as a means of self-responsibilization as well as self-emancipation. It comprises a heterogeneous set of institutional orders and pluralistic set of agents, many of which are operationally autonomous and resistant to control from outside – whether through market forces, top-down command by the state or horizontal networking. This chapter presents the theoretical background to interest in governance, shows the etymological roots of the concept, offers some reasons for the explosion of interest in governance in the 1960s and 1970s, and describes the main features of governance practices.
This chapter addresses the recent discovery of 'governance' as the complex art of steering multiple agencies, institutions, and systems that are both operationally autonomous from one another and structurally coupled through various forms of reciprocal interdependence. This discovery could well reflect the dramatic intensification of societal complexity that flows from growing functional differentiation of institutional orders within an increasingly global society with all that this implies for the widening and deepening of systemic interdependencies across various social, spatial, and temporal horizons of action. Indeed, governance appears to have moved up the theoretical and practical agendas because high levels of (increasingly globalized) functional differentiation undermine the basis for hierarchical, top-down coordination under the aegis of a single centre at the peak of a given societal formation -- let alone at the peak of a global social system with its continuing territorial division into nation-states still jealous of their declining formal sovereignty (cf. Luhmann 1984; Willke 1987, 1990).
Governance: Prospects of complexity theory in revisiting system theory
2007
The broadest meaning of governance is the regulation of social activities utilizing a variety of modes and mechanism of societal regulation. These range from collectively binding decisions to uncoordinated individual action guided by social norms and rationality principles. In the political science literature of the 1950s and 1960s this theoretical problem was treated in terms of "control" and "regulation" by variants of system theory. However, during the 1980s this systematic perspective was crowded out by individualist approaches-above all rational choice-and a macro perspective of societal regulation was lost. Although governance theory tries to speak to these questions, its foundation in general social theories is rather weak. This paper argues that various streams of complexity theory offer a broader and deeper theoretical foundation for theories of governance and regulation than other existing approaches. Complexity theory was initially developed in the physical and biological sciences. However, social scientists rapidly recognized its potential in formulating dynamic theories of the evolution of social systems. Whereas the various approaches differ in detail, they share common elements. These include the explicit modeling of multiple positive and negative feedbacks among the agents in a system, the introduction of learning and adaptation at the level of purposive agents, and the recognition of the multi-layer nature of social systems, in which phenomena at higher levels emerge from (but are not necessarily fully determined by) interactions at lower levels.
Ascertaining the Normative Implications of Complexity Thinking for Politics
2015
Central to representing the world as a complex dynamical system is understanding it as pertaining to an interdisciplinary approach to nonlinear processes of change in both nature and society. Although complexity research takes its origins from its applications in physics, chemistry, mathematics, and the “hard” sciences, undergoing its formative development in the 1970s, during the last two decades it has exerted an effect on the social sciences as well. Today complexity research is generating what Stuart Kauffman (2008, Preface) calls a “quiet revolution” in both the physical and social sciences.
Roundtable Report on the Progress of Complexity & Policy in the US and Internationally
The development of science and technology policy with human interaction is complex. Therefore, policy can be viewed as a complex adaptive system (CAS). CAS is defined as a system of networks and interdependent parts where the parts are greater than the whole, along with capacity for adaptation and emergence. This perspective allows for exploring research and effective approaches to policy with an emphasis on interaction dynamics that includes emergence and critical tipping points. Interaction dynamics are the interplay during the policy process of effects between agents and agents, agents and the collective systems, as well as agents, the collective systems, and the environment. Yet, what becomes of the established linear, cause and effect paradigm and how can complexity theory mesh with it? The roundtable participants provide a progress report of U.S. and international progression on complexity theory and methodology applied to policy, addressing these concerns. Complexity science can bolster research efforts and supplement traditional research with advanced modeling and computing power. Also, complexity tools can move research from "what is" to "what could be," with rich simulations that model the real world. Complexity applications in policy research continue to expand, as do the means to report results, like in the newly founded Journal on Policy and Complex Systems.
Olssen, Mark (2015) ‘Ascertaining the Normative Implications of Complexity for Politics: Beyond Agent-Based Modeling’, in Emilian Kavalski (ed.) (2015) World Politics at the Edge of Chaos: Reflections on Complexity and Global Life. SUNY Press, New York, pp. 139 – 168., 2015
Central to representing the world as a complex dynamical system is understanding it as pertaining to an interdisciplinary approach to nonlinear processes of change in both nature and society. Although complexity research takes its origins from its applications in physics, chemistry, mathematics, and the "hard" sciences, undergoing its formative development in the 1970s, during the last two decades it has exerted an effect on the social sciences as well. Today complexity research is generating what Stuart Kauffman (2008, Preface) calls a "quiet revolution" in both the physical and social sciences. One of the earliest centers for complexity research was at Santa Fe, where researchers developed the first research program with application to politics based on agent-based modeling.