Managing Complex Issues through Evolutionary Learning Laboratories

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Abstract

Policy makers, managers and leaders in organizations, governments and business institutions are under increasing pressure to make the right management decisions in the face of a continually changing political and socio-economic landscape. To make matters more challenging, the complex environmental, socio-economic, business-financial issues that decision makers need to deal with tend to transcend the jurisdictions and capacities of any single organization. There is a multitude of difficult, long-term global challenges ahead, almost all of which are coupled with the most pressing concerns of different countries at national and local levels. Despite many efforts to deal with these complex issues facing our society, the solutions so far have seldom been long lasting, because 'treating the symptoms' and 'quick fixes', using traditional linear thinking, are the easiest way out, but do not deliver the solutions. This paper describes the processes for unravelling complexity through participatory systems analysis and the interpretation of systems structures to identify leverage points for systemic interventions. It further demonstrates the promotion of effective change and the enhancement of cross-sectoral communication and collaborative learning. This learning focuses on finding solutions to complex issues by applying an iterative, systems-based approach, both locally-Evolutionary Learning Laboratory (ELLab)-and globally-Global Evolutionary Learning Laboratory (GELL). A generic framework and processes for implementing and institutionalizing ELLabs are described, and how these become part of the GELL for managing complex issues is explained. Four case studies are used to demonstrate diverse examples of the application and implementation of the ELLab approach. © 2013 John Wiley & Sons, Ltd.

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Bosch, O. J. H., Nguyen, N. C., Maeno, T., & Yasui, T. (2013). Managing Complex Issues through Evolutionary Learning Laboratories. Systems Research and Behavioral Science, 30(2), 116–135. https://doi.org/10.1002/sres.2171

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