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Many computer models are being developed in research, with many either claiming political relevance or being financed precisely with that objective in mind. The challenges surrounding actual use of computer models in policy formulation are far from trivial, but are rarely investigated and documented in detail. Here, we would like to plead for more studies documenting both model use and non-use. Analysis of cases of non- or very partial use may be at least as enlightening as 'successful' cases, although modellers may find the results uncomfortable reading. In this chapter we have tried to conceptualize and summarize lessons learned, identifying by whom, when and how computer models are used in policy formulation, based on a number of demonstrated cases of land use and NRM where models did make a difference in policy formulation. We believe that some of the insights from hindsight may be generally applicable to other types of models and policy domains, but some may not be. Nevertheless, valuable general lessons can be learned.

The factors 'problem solving dynamics', 'boundary arrangements', 'model types', 'roles of models' and the 'matching' process allow insight regarding the who, when and how questions as to land use and NRM modelling. Based on this analysis and the further experience obtained in the example presented in Section 5, we conclude that in designing a modelling strategy with a promising opportunity for model use, equal attention must be paid to the technical requirements for model development and to the embedding of the work in a given or intended societal context. Contextualization and network building are essential to embed a model in the societal context, and to avoid modelling becoming too much of a scientific or technocratic purpose in itself.

A number of activities are particularly relevant for the matching process in various stages of the actual model development work. During the preparation, the scientists can clearly influence the proper choice of model type depending on the problem formulation dynamics and the required role of the model. Models are generally appreciated for their capability to address interactions between components of systems and between different environmental, economic and social aspects, including analysis of trade-offs. Policy questions that are likely to benefit from an integrative systems approach will allow better chances for model introduction. Studying the boundary arrangement will greatly facilitate the identification of a proper pathway for model introduction. Finally, stepping stones may be helpful when working in new or difficult boundary arrangements.

During the actual model development process, continuous attention is needed to match the possible and desired roles of the model in the specific phase(s) of policy formulation. Second, model contextualization requires attention, which implies that the underlying values and aspirations of the modellers are made explicit continuously and that these fit the social and biophysical context of the system and its stakeholders. Stepping stones in the science-policy interaction may continue to be highly instrumental in realizing this matching and contextualization.

A distinct quality of computer models is their heuristic role, that is, their potential contribution to learning, especially social learning (Muro and Jeffrey 2008; Reed et al. 2010), which can be defined as the convergence of stakeholder perspectives on the problem and possible solutions (De Kraker et al. 2011). Social learning can form the basis for integrated solutions that require collective support and/or concerted action of various stakeholders. In recent research, attempts have been made to measure social learning, with an emphasis on the role of computer models (van der Wal et al., 2014). It is our hypothesis that a more precise understanding of whether and how social learning is facilitated by models may strengthen the understanding of how they must be developed, both technically and socially. This, together with enhanced insight into the factors determining the introduction of a model, seem crucial steps towards a better understanding and use of computer models in policy formulation processes.


The authors would like to thank Andy Jordan, John Turnpenny and Tim Rayner for their valuable advice and assistance in the finalizing of this chapter.

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