FORESIGHT, GOVERNANCE AND COMPLEXITY OF SYSTEMS: ON THE WAY TOWARDS PRAGMATIC GOVERNANCE PARADIGM

Authors

  • J. Stenvall
  • J. Kaivo-Oja

DOI:

https://doi.org/10.5755/j01.eis.0.7.4236

Keywords:

Complexity theory, foresight research, governance, leadership, system theory

Abstract

The analytical connection between foresight and governance is a vital scientific issue in management sciences. The landscape of management includes elements of foresight and governance. As we know foresight is focused on futures and governance is focused on current and intentional decision-making processes. There has not been too much theoretical discussion about these two key concepts of management sciences. The grand aim of the article is to define key elements of new pragmatic governance paradigm. The article focuses on theoretical discussion of these two concepts: foresight and governance. Foresight process includes in an ideal case three elements: diagnosis, prognosis and prescription (DPP framework) which were invented in the FOR-LEARN project of the European Union. The structures of governance are linked to these elements of foresight. By good governance managers and leaders want to create effective and just structures of society. Authors note that good governance requires deep understanding of complexity. There are systems with low level of complexity (simple systems) and systems with very high level of complexity (chaotic systems), and complicated systems and complex systems. Systems can be either in form of order or disorder. Action of governance aims to create order, but it does not always be successful in this aim. The degree of complexity varies and foresight analyses needed in different decision-making situations depend on the degree of complexity of systems. For example, in chaotic systemic conditions, we do not need much foresight processes, but actions. Following David Snowden (2002) and Snowden & Kurtz (2003) there are four decision models: (1) For simple systems and Known systems: Sense - categorise – respond; (2) For complicated systems and for systems which are Knowable: Sense-analyse-respond; (3) For complex systems and for systems which are Unknowable, complex: Probe-sense­respond; (4) For chaotic systems and Unknowable, chaotic: Act-sense-respond. These four decision models can be key elements of pragmatic governance. In the article authors present a DPP methodology map for complexity management. Summary section of the article includes key elements of pragmatic governance paradigm.

DOI: http://dx.doi.org/10.5755/j01.eis.0.7.4236

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Published

2013-09-17

Issue

Section

Social Evolution of Europe