WHAT KINDS OF KNOWLEDGE DO COMPUTER MODELS SEEK TO FEED INTO POLICY FORMULATION?
Scientists have choices in how they relate to decision makers. These choices have important effects on decisions or other outcomes arising from the science-policy interface. In his book The Honest Broker, Roger Pielke (2007) describes four roles a scientist can take in this respect: Pure Scientist, Science Arbiter, Issue Advocate and Honest Broker. A Pure Scientist is not involved in policy - (s)he publishes or presents his or her scientific work, without engaging with policymakers. A Science Arbiter responds to questions without expressing an opinion on related policy choices, in contrast to an Issue Advocate who takes a clear position and argues for specific policy action, using scientific knowledge. Finally, the Honest Broker engages in the policy process to use scientific information to expand or clarify the scope of choice available to the decision maker. In this role, the scientist reveals the different options and their possible consequences, without taking a stance.
Following Pielke, we work from the premise that the prime and preferred role of the scientist is that of an Honest Broker. However, it is virtually impossible for a scientist to take a value-free stance in societal and political issues. Scientists often have to make choices on what to include or exclude in their analysis for reasons of data availability, importance and resource (including time) availability; such choices are often affected by normative and personal factors. Yet, a key stated aim of a great deal of science is to better inform policymaking processes - through assessing proposed options in all relevant dimensions of sustainable development, and through revealing alternative options and their consequences - while not advocating particular solutions. This requires transparency about all kinds of choices made in the research process. It also requires a degree of engagement with the decision maker to make sure all relevant alternatives are investigated, and that the scientific analysis is indeed useful and understandable.
Quantitative systems models constitute an important means of learning, in the context of professional practice connected to human values (Leeuwis 2004). Learning through experience could be labelled experiential learning (Kolb 1984) through a continuous interaction and iteration between thinking and action. Models often seek to enhance such learning and thus seek to play a heuristic role. By their very nature, computer models are strong in handling all kinds of interactions between sub-components of the system and between different processes that determine its state. This may assist in providing insight into important processes and drivers of systems behaviour, thus contributing to meaning and knowledge. Scientific and policy-oriented research relies on this use of system models for all sorts of levels, ranging from the level of the gene (as in the case of Genetically Modified Organisms) to planetary systems (as in the case of the Intergovernmental Panel on Climate Change). Models may also be used to structure thinking about implications of systems configurations that do not yet exist, thus supporting ex ante or ex post assessment and evaluation of policies. Finally, if transparent, models may enhance learning by diversifying the solution space, revealing trade-offs and synergy among objectives, and supporting the selection of 'suitable' alternatives. Other proposed roles of models are relational (mediation of conflicts between stakeholders or actors and contributions to community-building) and symbolic (raising awareness and putting issues on the agenda). The extent to which these high aspirations are actually delivered is discussed in the next section.