Start Date
1-7-2006 12:00 AM
Abstract
Scientific policy aid, especially in environmental management facing complex choices involvingdivergent beliefs and interests/ values, has evolved into a number of distinct fields such as Decision SupportSystems (DSS), Expert Systems (ES), Integrated Modelling and Assessment, Risk Analysis. While theapproaches pursued by these systems are different, they are all focused on sustainable development andimprovement of decision making involving intractable, elusive and ill-structured problems.Computer-aided policy support is expected to explore multiple perspectives of the problem at hand; enhancedecision makers’ insight into the problems drivers and policy outcomes; and facilitate communication andknowledge transfer between the actors involved in or affected by the decision. In this context computersystems play a crucial role as catalysts of interdisciplinary research and promoters of scientific policy advice.Applied policy research, however, seems to be losing its appeal mainly due to the persistent lack ofsuccessful implementation. There are different reasons for which policy makers do not embrace scientificpolicy recommendation, including the systems’ failure to address the changing context of the problems;system complexity; highly demanding user interfaces not geared to users’ skills; the low transparency of thesystems’ operation (‘black box’ technology); cognitive obstacles, such as an aversion among seniorexecutives to DSS technology; ignoring the broader organisational and institutional context.In this paper we explore recent trends leading to a new generation of policy support systems [Beynon et al.,2002; Courtney, 2001; McCown, 2002a; McCown, 2002b; Rauscher, 1999; Shim et al., 2002] set toovercome these problems. Attention is paid to methodological pluralism favouring the simultaneousapplication of different methodologies which stimulate learning, question beliefs and surface tacitassumptions; comprehensive risk/uncertainty analysis and communication, accounting for all sources/typesof uncertainty; deliberative methods pursuing pluralistic, inclusive approaches to decision making;participatory assessment [Renn, 2006; Stirling, 2003] and group model building [Vennix, 1999]
Paradigm Shift in Computer-Aided Policy Support
Scientific policy aid, especially in environmental management facing complex choices involvingdivergent beliefs and interests/ values, has evolved into a number of distinct fields such as Decision SupportSystems (DSS), Expert Systems (ES), Integrated Modelling and Assessment, Risk Analysis. While theapproaches pursued by these systems are different, they are all focused on sustainable development andimprovement of decision making involving intractable, elusive and ill-structured problems.Computer-aided policy support is expected to explore multiple perspectives of the problem at hand; enhancedecision makers’ insight into the problems drivers and policy outcomes; and facilitate communication andknowledge transfer between the actors involved in or affected by the decision. In this context computersystems play a crucial role as catalysts of interdisciplinary research and promoters of scientific policy advice.Applied policy research, however, seems to be losing its appeal mainly due to the persistent lack ofsuccessful implementation. There are different reasons for which policy makers do not embrace scientificpolicy recommendation, including the systems’ failure to address the changing context of the problems;system complexity; highly demanding user interfaces not geared to users’ skills; the low transparency of thesystems’ operation (‘black box’ technology); cognitive obstacles, such as an aversion among seniorexecutives to DSS technology; ignoring the broader organisational and institutional context.In this paper we explore recent trends leading to a new generation of policy support systems [Beynon et al.,2002; Courtney, 2001; McCown, 2002a; McCown, 2002b; Rauscher, 1999; Shim et al., 2002] set toovercome these problems. Attention is paid to methodological pluralism favouring the simultaneousapplication of different methodologies which stimulate learning, question beliefs and surface tacitassumptions; comprehensive risk/uncertainty analysis and communication, accounting for all sources/typesof uncertainty; deliberative methods pursuing pluralistic, inclusive approaches to decision making;participatory assessment [Renn, 2006; Stirling, 2003] and group model building [Vennix, 1999]
