Direction not Destination

Wednesday, 4 July 2007

Stakeholder Participation and Expertise

The problems of equifinality and affirming the consequent suggest alternative criteria by which to validate or evaluate socio-ecological simulation models (SESMs) will be useful. In my last post in this series I suggested that trust and practical adequacy might be useful additional criteria. In light of the 'risk society'-type problems facing the systems that SESMs represent, and the proposed post-normal science approaches to examine and resolve them, the participation of local stakeholders in the model validation process seems an important and useful approach to ensure and improve model quality. If local stakeholders are to accept decisions and policies made based upon results from simulation models they will need to trust a model and, by consequence, the modeller(s).

Due to a perceived 'crisis of trust' in science over the last 20 years, Wilsdon and Willis suggest "scientists have been slowly inching their way towards involving the public in their work" and that we are now on the cusp of a new phase of public engagement that takes it 'upstream'. This widely used, but somewhat vague term, is used to refer to the early involvement of the lay public in the processes of scientific investigation. As such, engagement is 'upstream' nearer the point at which the research and development agenda is set, as opposed to the 'downstream' end at which research results are applied and the consequences examined (see Figure 1).

Figure 1 Public participation in the scientific research process. Recently it has been suggested that public engagement with the scientific process needs to move 'upstream' nearer the point at which the research agenda is set. After Jackson et al

Whereas previously the theory of the 'public understanding of science' was a deficit model suggesting that the public would trust science 'if only they understood it', the contemporary shift is towards and engagement and dialogue between science and society. The implication of this new turn is that the public will trust science 'if only they are involved in the process itself'. Recently, Lane et al. advocated this move upstream for forms of environmental modelling that address issues and concerns of rural populations. This position has been criticised as devaluing the worth of science, for patronising the public, and being a mask for political face-saving or insurance.

Regardless of other areas of science, in the case of developing simulation models for socio-ecological systems the participation of the public does not result in the first two of these criticisms. Engaging with local stakeholders to ensure a model is both built on a logically and factually coherent foundation and to ensure it examines the appropriate questions and scenarios is of great value to the modelling process and should improve representation of the empirical system. Contributing to successful iterations of this process, local stakeholders will gain both trust and understanding. However, the inclusion of local stakeholders in the modelling process does raise the issue of expertise.

With parallels in the three phases Wilsdon and Willis have suggested, Collins and Evans have suggested we are entering a third wave in the sociology of science. This third wave demands a shift from an emphasis on technical decision-making and truth to expertise and experience. Collins and Evans suggest there are three types of expert in technical decision-making (i.e. decision-making at the intersection of science and politics); 'No Expertise', 'Interactional Expertise', and 'Contributory Expertise'.

Individuals possessing interactional expertise are able to interact 'interestingly' with individuals undertaking the science, but not to contribute to the activities of science itself (contributory expertise). Brian Wynne's well-known study of the (inadequate) interaction between Cumbrian sheep farmers and UK government scientists investigating the ecological impacts of the Chernobyl disaster is a prime example of a situation in which two parties possessed contributory expertise, but neither interactional expertise. As a result, the 'certified' expertise of the government scientists was given vastly more weight than the 'non-certified' expertise of the farmers (to the detriment of the accuracy of knowledge produced). Such non-certified expertise might also be termed 'experience-based' expertise, arising as it does from the day-to-day experiences of particular individuals.

The importance of considering non-certified, contributory experience is particularly acute for SESMs. Specifically, local stakeholders are likely to be an important, if not the primary, source of knowledge and understanding regarding socio-economic processes and decision-making within the study area. Furthermore, the particular nature of the interactions between human activity and ecological (and other biophysical) processes within the study area will be best understood and incorporated into the simulation model via engagement with stakeholders. This local knowledge will be vital to ensure the logical and factual foundations of the model are as sound as possible.

Furthermore, engagement with local stakeholders will highlight model omissions, areas for improved representation, and guide application of the model. It provides an opportunity to enlighten experts as to the 'blind spots' in their knowledge and questions. As such, the local stakeholders become an 'extended peer community', lending alternative forms of knowledge and expertise to the model (and research) validation process than that of the scientific peer community. This knowledge and expertise may be less technical and objective than that of the scientific community, but this nature does not necessarily reduce its relevance or utility to the modelling of a system that contains human values and subjects.

I pursued this idea of stakeholder participation in the modelling I undertook for my PhD. Early in the development of my agent-based model of land use decision-making, local stakeholders were interviewed with regards to how they made decisions and their understanding about landscape dynamics. Upon completion of model construction I went to talk with stakeholders about the model as they offered the prime source of criticism about the model representation of their decision-making activities. By engaging with these stakeholders a form of qualitative, reflexive model validation was performed that overcame some of the problems of a more deductive approach.

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This work by James D.A. Millington is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 United States License.


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