When and How are Computer Models Used in Practice?

We argue that computer models and knowledge emerging from them may, but not necessarily will, be used, if a number of circumstances converge. More precisely, the specific phase of the problem solving or policy formulation cycle, the role of model, type of model and the so-called boundary arrangement between science and policy need to match (Figure 5.3). The chances that the computer models (or the knowledge emerging from them) actually will be used increase if this matching occurs in a process of contextualization and networking.

Problem solving dynamics and the main phases of policy formulation (Section 3), different roles of models (Section 4) and different types of models (Section 2) have been introduced earlier in the chapter. Boundary arrangements describe how actors conceive of the division of labour between science and policy. They characterize the institutional science-policy space and help to explain experiences of interactions between science and policy. Building on the work of Hoppe (2005), Sterk et al. (2009b) define four boundary arrangements based on two criteria: (1) who is perceived to initiate the research, that is, 'science' or 'policy', and (2) how logical and appropriate it is to integrate scientific knowledge and policy. Acknowledging the different existing boundary arrangements makes explicit the institutional space in which modellers function and the arrangements or facilitators that may assist in model introduction.

The actual matching of the four factors and the chances for model use are supported by 'contextualization' and 'network building'. Contextualization is the process that encompasses the explication of underlying values and aspirations of the modeller, fitting the model to a

Conditions that favour model application in policy formulation: matching of four factors through a process of model contextualization and network building

Source: Sterk et al. (2011).

Figure 5.3 Conditions that favour model application in policy formulation: matching of four factors through a process of model contextualization and network building

social and biophysical context and interpretation of the model (and its results) in relation to other knowledge sources such as expertise and the experiences of other involved actors. Network building, mostly led by the scientists, is about becoming linked to other societal stakeholders and fostering feelings of interdependency. In building a network, modellers, potential users, other stakeholders as well as the land use model itself take on roles. In the cases where land use models contributed to problem solving, substantial investments have always been made in network building and contextualization. It was not one specific actor (group) that made these investments; we came across examples where both modellers and future users took the initiative.

In the analysis of contextualization and network building processes, two 'critical leverage points' were identified (Sterk et al. 2011): first, participation of stakeholders and/or envisaged users in model development, and second, availability of 'stepping stones', the latter referring to the closer involvement of researchers or professionals other than the modeller within the policy sphere. A stepping stone is a person (or small group of people) that functions as a guide when a modeller starts to work in an unknown problem setting or moves into a different boundary arrangement.

Participation of stakeholders in model development has been a frequently debated aspect of modelling research (for example, Parker et al. 2002; Walker 2002; Jakeman et al. 2006). The argument holds that more participation increases the relevance and commitment of the involved stakeholders and consequently leads to greater impact of modelling outside science. Crucially, the cases where a land use model contributed to problem solving exhibited some degree of participation in model development, ranging from a few meetings to discuss the problem definition and research questions, informing the envisaged users about progress and fine-tuning the research further, to collaborative data collection of modellers and stakeholders. The observed consistent employment of participatory modelling suggests that it is a viable approach, although the implementation varied.

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