Reconciling the Supply of and Demand for Research

by Roger Pielke, Jr.
Founding CSTPR Director, CSTPR Faculty Affiliate and Professor at University of Colorado Boulder

In 2006, as part of a major NSF research project on decision making under uncertainty, Dan Sarewitz and I published a paper outlining a methodology for the reconciliation of the supply of and demand for science (read the paper here). The method took some concepts that are ever-present in our community’s thinking about science and decision making and set them forth as a formalized approach to better connecting research with user needs.

We began with recognition that there are those of us primarily in the business of producing research, what we termed the supply side. There are also those of us primarily in the business of using research, which we called the demand side. We live in an era where much science, especially that which is publicly funded, is expected to have some connection with the needs of decision makers. We called efforts to make such a connection “reconciliation of supply and demand” (or RSD) recognizing that there are many similar concepts in the literature, such as well-ordered science, boundary work, Mode 2 science and so on.

We illustrated the method via a simple 2 x 2 matrix shown below.

The effective reconciliation of supply and demand is illustrated in two quadrants (upper right and lower left, illustrated with the green stars), for instance:

  • Research could be produced that is well-used by decision makers;
  • Decision makers may have no need for research, and research agendas are not seeking to produce results for these decision makers;

Such outcomes would represent success.

There are also cases where research and use are misaligned, creating opportunities for reconciliation, represented by the red circles in 3 of the 4 quadrants, in situations such as:

  • Users might desire information, but it is not being produced;
  • Users might benefit from information, but are unaware of that benefit and it is not being produced;
  • Information is being produced and with potential value, but it is not being used by decision makers.

Each of these possible situations is of course far more complex and nuanced than summarized here, however they clearly represent opportunities for science and decision making to be more effectively connected.

The intellectual exercise of RSD can be thought of as a mapping exercise. One intellectual task is to obtain a sense of what research is actually being produced. A second intellectual task is to evaluate the information needs, wants and opportunities of relevant decision makers. And third, the task of reconciliation is to identify opportunities to better align supply and demand. RSD seeks to coordinate these disparate activities under a common approach.

For us, the over-arching goal of the RSD methodology was more than just a series of intellectual exercises, but to provide an opportunity for reflection on institutional and professional relationships in a manner that would open up opportunities for science and its use to be more productively interconnected:

. . . the research method itself creates feedbacks between supply and demand that will expand the constituencies and networks engaged in science policy discourse, expand the decision options available to science policy makers, and thus expand the opportunities to make climate science more well ordered. Undoubtedly, institutional innovation would need to be a part of this process as well . . .

A recent paper (Leith et al. 2018) documents a long-term effort in Australia to implement the RSD methodology, and reported some success, particularly with respect to our aim to foster institutional innovation:

In this account of a cross-scale operation on the ‘neglected heart of science policy’ we have suggested that, when knowledge production is actively conceived within the emerging paradigms of sustainability science, reconciling supply and demand is not primarily oriented to creation of information for decision- makers. RSD necessarily works to open up and actively reframe problems and possibilities. This means that RSD must challenge existing frames and naïve supply-side push and demand-side pull. Even where knowledge gaps persist and are considered substantial (such as in planning for an uncertain future), RSD must acknowledge that these are not necessarily filled by information. In our case people with knowledge and the capacity to use it to good effect in specific contexts were the primary constraint and focus. Through SCARP the majority of NRM planners appear to have benefited from the RSD process. This was not solely because information was deemed relevant, credible and legitimate. Participants frequently reflected on their growing courage to lead efforts towards adaptation within their organisations and communities. Ultimately, SCARP’s reports were an institutionally necessary means of legitimising those actions, based on knowledge gleaned from an extended collaboration.

It is especially rewarding to see that the RSD methodology was accompanied by greater “courage” in efforts to better align research with its use in situations where conventional norms and practices may have created obstacles.

While research products will likely always be a necessary part of mechanisms of accountability and evaluation for science projects, the effective reconciliation of the supply of science and its demand will always require far attention to more intangible results of implementing a systematic process of co-production. It in the long run, when science and its use are more effectively interconnected improved decision making – more effective, more legitimate – may be the result.

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