Machine Learning for Unlocking the Policy Impact of Transdisciplinary Research

As the 31st International Conference on Transdisciplinary Engineering 2024 kicks off at UCL East, over 100 engineers from around the world—including the United Kingdom, Brazil, Mexico, United States, China, Japan, Sweden, Singapore, and others—converge in London to explore how transdisciplinary engineering can drive social change and improve the world. This conference provides a platform for discussing the crucial role of engineering and science in addressing societal challenges through innovative, interdisciplinary approaches.

We already know that transdisciplinary research teams comprising engineers and others, tend to produce research that is more likely to have policy and commercial impact. Yet, with over 334 categorized research fields, there are more than 35 billion possible combinations for interdisciplinary work for teams of up to 5 researchers. Depending on the combination of researcher capabilities, some interdisciplinary teams may be better suited for disruptive science, developing patents, or informing policy. But which combinations of fields lead to which type of impact?

Supported by Elsevier and working with the Growth Lab at Harvard Kennedy School, we are applying complexity methods and machine learning on bibliometric data to understand which combination of researcher capabilities leads to high-impact research. For this blog, we’ll discuss our work within the context of the impact of interdisciplinary climate research on public policy.

To calculate interdisciplinarity, we determine the capabilities of authors based on their publication history in different fields. Each author is represented by a vector indicating the number of times they have published in each field. These author vectors are then used to calculate the disciplinary diversity (DDA) of each paper, reflecting the combined expertise and capabilities of the co-authoring team.

As a first step, we ran a series of statistical analyses and regressions to evaluate the relationship between a paper’s interdisciplinarity score and the number of policy citations it received. Preliminary results show that interdisciplinarity explains almost 15% of the variance in policy citations, making it the strongest predictor of policy impact we have identified so far. In fact, transdisciplinarity is found to be three times better at predicting policy citations than conventional metrics of research excellence, exceeding the combined effects of academic citations, journal impact factor, and author h-index.

We then aggregated our analysis at the topic level by calculating the average interdisciplinarity score of papers within each topic. The first observation is that climate change-related topics with high interdisciplinarity are less common than those with low diversity. The distribution of these topics is shown in Figure 1.

Fig 1: Distribution of paper interdisciplinarity in climate research

The second observation is that topics with higher ratios of policy citations have nearly double the average interdisciplinarity score compared to topics with relatively low policy citations. Figure 2 illustrates the difference in interdisciplinary distribution between the low and high policy relevance groups.

Figure 2: Interdisciplinary Distribution by Policy Relevance

Finally, using the paper vectors, we are developing a machine learning model to understand which combinations of author capabilities and team dynamics lead to high policy impact. The preliminary model has already found that for climate change-related research overall, teams involving researchers with expertise in economics, meteorology & atmospheric sciences, general & internal medicine, ecology, and horticulture tend to be associated with higher policy impact.

Moving forward, we are refining the model to predict the optimal team dynamics for high impact within specific policy topics. We are expanding the training data to include additional metrics and features, such as academic seniority, international collaboration, and research text. Furthermore, we will incorporate the distances between research fields to study the effects of deep interdisciplinarity, bringing together researchers from relatively less connected fields, on social impact.

As TE2024 brings together engineers and researchers from around the globe, this work exemplifies the potential of interdisciplinary collaboration in addressing global challenges and driving social change through innovative, data-driven approaches. Equipped with machine learning tools like this, researchers can develop targeted strategies to form data-informed transdisciplinary teams, optimized for maximum societal benefit and impact.