Knowledge integration for societal challenges: from interdisciplinarity to research portfolio analysis
For research to address societal challenges, indicators of average degree of ‘interdisciplinarity’ are not relevant. Instead, we propose a portfolio approach to analyze knowledge integration as a systemic process; in particular, the directions, diversity and synergies of research trajectories.
‘Convergence’ as knowledge integration for grappling with societal challenges
Last October the US National Academies held a workshop (available here) to gather views on how to better measure and assess the implications of interdisciplinarity, or convergence, for research and innovation. The use of the term convergence as a synonym of interdisciplinarity followed from two previous reports by the National Academies (2014 and 2019). These reports understood convergence as the ‘integration of knowledge and ways of thinking to tackle complex challenges and achieve new and innovative solutions that could not otherwise be obtained.’ (A discourse that echoes European discourse on interdisciplinarity for grand challenges and missions.)
In this blog, I will summarise the argument I put forward in the workshop: that for mapping progress towards this goal (that is: the successful knowledge integration for addressing a given societal challenge), we should conduct multidimensional portfolio analyses on the types of knowledge to be integrated rather than produce synthetic indicators of interdisciplinarity.
For two main reasons. First, since knowledge integration for societal challenges is a systemic and dynamic process, we need broad and plural perspectives and therefore we should use a battery of analytical tools, as developed for example in research portfolio analysis, rather than a narrow focus on interdisciplinarity. The second reason is that while interdisciplinarity is one (but not the only) of the relevant concepts in knowledge integration, the concept of interdisciplinarity is too ambiguous, diverse and contextual to be captured by traditional indicators, as discussed in a previous blog.
Fostering plural innovation pathways in the face of uncertainty and ambiguity
It has long been argued that addressing societal challenges, such as climate change or COVID-19, benefits from the combination of disparate types of knowledge. Societal challenges are ‘wicked’ problems, in the sense that the framings of both the problems and the solutions are complex, disputed and uncertain.
Under these conditions of ambiguity and uncertainty, research contributions are likely to come from combinations of diverse types of knowledge (or ways of knowing). This is: diversity within projects is needed. However, diversity across projects is also necessary. Since we do not know or even agree in advance on what types of expertise are appropriate to tackle a given problem, it is also important to have a plurality of research trajectories. Take the example of malaria: in spite of decades of efforts to develop drugs or vaccines, the most successful strategies so far have been fighting mosquitoes that transmit it, in particular with insecticide-treated bed nets.
Therefore, rather than just aiming at fostering a ‘melting pot’ of disciplines, research systems should also produce a high number of disparate research trajectories – knowing that only some of them will ever be technically successful.
Moreover, different research and innovation pathways are not equally desirable from a public value perspective – directionality matters. Some solutions are more socially preferable than others depending on their effects on public goods such as equity or environmental sustainability. Which means that public investment, while keeping a diverse portfolio of research strategies, should favour those which are perceived as more socially robust and relatively underfunded by the private sector.
In summary, policy for S&T convergence should aim at fostering systemic diversity, rather than interdisciplinarity in every single project or program, but it should also take into account the preferred research directions in particular contexts or societies.
From ‘measuring’ interdisciplinarity to multi-level mapping of knowledge integration
Measurement approaches to convergence should reflect this turn towards a systemic perspective on knowledge integration for societal challenges.
This shift in the conceptualisation of S&T indicators from individual to systemic properties is similar to the shift in biology towards ecological approaches. The forest should not be measured by the average size of its trees or the timber it yields (scalars), but by the distribution (vectors) of all types of species and how they interact (matrices). Because the wealth, in sustainable terms, that can be derived from the forest comes from this diversity: water resources, herbs and mushrooms that unexpectedly yield nutritional or pharmacological benefits, spaces for leisure and well-being, etcetera.
Similarly, the ‘solutions’ to societal challenges will not emanate from 1,000 labs with the same combination of disciplines, but from labs of various epistemic combinations and social embeddings. Therefore, our measurement should not focus on an average degree of interdisciplinarity. Instead, it should focus on mapping the directions and diversity of research approaches. To do this, we need to shift towards statistical descriptions of the vectors and distributions of research trajectories over knowledge landscapes. A framing in terms of research portfolios can help conduct this type of analyses.
Portfolio analysis: exploring directions, diversity and synergies
In a nutshell, the key idea is that for a given societal issue, the contribution of research should be explored by mapping the relevant types of knowledge over a research landscape (e.g. see obesity). The portfolio or repertoire of a given laboratory, university or territory, can then be visualised by projecting (overlaying) their activities of this research landscape, as illustrated in the figure above for ‘rice research’ (or avian flu).
First, this portfolio provides us with information on the main directions that the research on a given topic is taking – which is pointing to the type of solutions envisaged for a grand challenge. For example, in the example in the figure above on rice, if the focus is related to genomics, mainstream research investments can be expected to deliver via Genetically Modified seeds (the case of the US). But if the focus is in fertilizers and yields (the case of India), the main goal is to increase productivity.
Second, the portfolio can tell us about the diversity of research efforts, i.e. whether investments are heavily concentrated in a few areas, or distributed across a variety of fields. In the face of uncertainty and contested views on preferred innovation pathways (e.g. in renewable energies), one would expect a variety of pathways to be supported. This way the bets are hedged against unexpected scientific results or social reactions to certain approaches. Indicators of interdisciplinarity provide a view of the epistemic diversity in specific projects, labs or centres. This is a valuable but only a partial perspective of the research landscape.
Third, by analysing the interrelations between innovation areas, a portfolio approach helps think about the synergies or lack thereof across research pathways. For example, in a portfolio of energy technologies, solar cells and small wind turbines have positive synergies as they both fit with distributed electricity infrastructure, while they have negative synergy with nuclear energy which needs centralisation. Understanding these positive or negative relations is important in balancing portfolios.
From ‘atomistic’ to systemic and dynamic descriptions
In summary, since social contributions are multifaceted, the analysis of research for societal challenges needs to adopt systemic perspectives, and thus take multidimensional forms. Research portfolio analysis offers a battery of tools, among other possibilities of exploring systemic properties of a research landscape. While interdisciplinary research is paramount in certain points, it is not required across the whole landscape. Therefore, rather than indicators of aggregates or averages, we need rich description of knowledge landscapes including the directions, diversity and synergies of research trajectories.
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