A methodology for the design and development of integrated models for policy support
Introduction
Integrated Decision Support Systems (DSS) are rapidly gaining attraction in the planning and policy-making community. When introduced into the decision-making process in a controlled way, they can create high added value by bringing scientific knowledge to the decision makers’ table. Despite this high interest, only a few DSS are in actual use to support policy development and analysis. Academic literature recognizes several reasons for this, most notably a lack of transparency, inflexibility and a focus on technical capabilities rather than on real planning problems (Uran and Janssen, 2003, Vonk et al., 2005, Geertman, 2006). In order to deploy a DSS as an instrument for strategic policy making, it has proven to be crucial that the system matches the perceptions, experiences and operational procedures of the policy makers and that it enhances their current policy practices rather than replace existing and well-embedded ones (Van Delden, 2003, Van Delden et al., 2007, McIntosh et al., 2007, Te Brömmelstroet and Bertolini, 2008).
For integrated models to be able to provide useful support to policy making, they should be able to represent the complex interactions taking place in the human–environment system. Over the past decade the science on model integration has gained in importance and has been facilitated by the improved software capabilities that allow for the development of DSS featuring integrated models. Integrated modelling has evolved from the early integrated assessment models (Rotmans et al., 1990) and ecological economic models (Low et al., 1999) to the current spatially explicit and complex systems as described, for example, by Forsman et al. (2003) on linking farmer’s behaviour with air and water quality, by Amann et al. (2004) on air pollution and by Van Delden et al. (2007) on regional development and desertification. This paper builds on practical experience of Integrated Spatial Decision Support Systems (ISDSS) development over the past decades but much of the discussion is also relevant to systems with modest or no spatial representation. It provides a methodology for the design and development of integrated DSS that includes both ‘hard’ and ‘soft’ factors. Hard factors relate to the selection and development of a model, model integration, model evaluation and the selection of the software platform. The ‘softer’ factors relate to linking scientific knowledge to information relevant to policy support, emphasis on social learning of the different groups involved, the role of champions and the implementation of DSS in (policy) organisations. The need for this kind of approach in which both factors are incorporated is also recognised by McIntosh et al., 2007, Van Delden, 2009 and Volk et al. (in press). The methodology builds on principles of software engineering, product design and DSS development and incorporates techniques such as evolutionary design and rapid prototyping (Cross, 1994, Marakas, 1999, Robertson and Robertson, 1999). It incorporates elements from the domain of integrated assessment modelling (IAM) by including multiple issues and stakeholders, integrating the human and the natural sciences and by incorporating multiple scales of system representation, spatial and temporal behaviour and cascading effects (Rotmans and Van Asselt, 1996, Parker et al., 2002, Jakeman and Letcher, 2003). The methodology makes use of interaction design methods (Gullikson et al., 2003, Moggridge, 2007) to ensure a user-centred and demand-driven approach and builds on psychology and organizational theory (Weick, 1979, Langley et al., 1995) to understand the process of decision-making, providing insight into both the co-creation of DSS by developers and users and its implementation in organisations. Although literature on the design and development of decision support and other software systems is already widely available we aim to provide added value to this by focusing explicitly on the core characteristics of developing integrated models for policy support: the science-policy interface, the integration of models from different disciplines and the collaborative effort of users, scientists and software developers.
Section snippets
Integrated models for policy support
Integrated modelling systems for policy support can be found under a diversity of names, amongst others (Spatial) Decision Support Systems or (S)DSS (Turban, 1995), Planning Support Systems (Geertman and Stillwell, 2003) and Policy Support Systems or PSS (Van Delden et al., 2007). Although they differ in their specifics, for the purpose of this paper we will group them all under the name of Decision Support Systems (DSS) since they have sufficient characteristics in common (for examples of good
Methodology for design, development and implementation
There are some important lessons learnt from the past development of several DSS. An ideal development process can best be described as an iterative process of communication and social learning amongst three involved parties (see, e.g. Engelen, 2000, Te Brömmelstroet and Bertolini, 2008) (Fig. 1).
First, there are the end-users of the system. In this paper they are often called policy makers, but these include all kinds of actors that are present in the process that is supported. Together, they
Important tasks within the process
Within the general framework presented in Section 3 important tasks can be distinguished that have to be fulfilled during the design and development of a DSS. As stated before, these tasks need not – and often cannot – be carried out in a purely sequential order. Due to the complexity of the process and the innovativeness of it for most contributors, it is very unrealistic to have this expectation and therefore one should be able to go back to previous tasks, either to complement or detail
Involvement of users
Throughout the entire design and development process user interaction is of crucial importance; not only to ensure that their input is included in the further development, but also because including them enables social learning on the side of the users as well as on the developers’ side (scientists and IT-specialists). It is unrealistic to demand from users a detailed specification document at the beginning of the design and development process, simply because they are not aware of what can be
Conclusions
In this paper we have proposed an approach to the design and development of DSS, which provides a methodological basis for the requirements discussed by Diez and McIntosh, 2009, Van Delden, 2009 and Volk et al. (in press). The proposed methodology emphasizes an iterative design and development process that enables social learning of the different groups involved. We have distinguished several important elements for models that aim to provide support to policy processes, and to which we refer as
Acknowledgments
The New Zealand Foundation for Research, Science, and Technology funds the Creating Futures project (ENVW0601), the European Commission has funded the development of LUMOCAP (FP-6 SSPE-CT-2005-006556), MedAction (FP-5 EVK2-2000-22032) and DeSurvey (FP-6 IP 003950), the Puerto Rican government funds the development and implementation of the Xplorah SDSS and the German government finances the Elbe-DSS.
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