Convergence: From Debatable Elasticity in Substance to Heuristic Emblem of Innovation
“Yet the hope of hitting on some definition which is at one and the same satisfactory and brief dies hard: much can be learned by seeing just how much elasticity is ultimately required of such portmanteau definitions” (Toulmin 18). In Forecasting and Understanding, Toulmin is speaking of science, but my thoughts while reading migrated to how Toulmin might size up convergence, the topic of much of my initial work in this program.
This week’s reading series is focused on the challenges associated with grafting complex modelling and prediction tools created for scientific ends onto public policy decision-making. Convergence may well fit within a possibly parallel construct since it is an idea created by scientists that regularly manifests suggestions for how federal science policy funding should be undertaken and allocated in order to address the uncaptured knowledge future at the intersection of engineering and quantitative biology.
Convergence is a term gaining interest among decision funders in Washington science circles. At most, convergence is a vision of what technoscience can, should and will be, according to the leading scientists who have access to comment through MIT, Science Magazine, and platforms available at the National Academies. In the least, convergence is just the latest version of interdisciplinary research in action, sometimes called “team Science”, transdisciplinary science, etc.
Rather than beginning by parsing the definitions offered in representative texts such as 1) Sharp & Langer “Promoting Convergence in Biomedical Science” Science (2011); 2) “NSF Dear Colleague Letter: Growing Convergence Research” (2018); and 3) “Convergence: Facilitating Transdisciplinary Integration of Life Sciences, Physical Sciences, Engineering, and Beyond” (Executive Summary) NASEM (2014), Toulmin might acknowledge the elasticity of these differing definitions and dig in further there. Toulmin, for instance, says of science that the varying versions of its definitions find “success in treating or classifying things which they scarcely pretend to explain” (Toulmin 21). Toulmin might choose to interrogate measure the elasticity of the combined body of definitions in order to understand the nature of the ideas at the core of these conceptual platforms.
Further, Toulmin may ask what are the “marks and merit of criteria for success” of convergence in play? Toulmin wants to know “the aims and purposes which a participant in it has to pursue” (Toulmin 20) While this is worth additional thought, initial barriers to play in the convergence area are a Doctor of Philosophy, (likely from the physical, natural, chemical, or computing sciences, rather than the social sciences). It also appears that the players are predominantly academic since those who are not academic are scholarly leaders at science thought palaces in Washington DC such as those aforementioned including the National Academies, The Journal of Science, and even the National Science Foundation. In order to engage successfully, such established players are required to believe that the future of science is at the intersection of disciplines; that we must think across silos both in how we work and how the next generation of scientific knowledge creators is trained. If MIT is included, these players must also believe in expanding beyond sectors to break down silos that even divide institutional missions, both physically and intellectually.
We can go further and test convergence against Toulmin’s high bar of conditional prediction, that combines both understanding and forecasting into a kind of enlightened prediction, (or retrodiction, an approach Toulmin seems to lean towards as he leans away from the predictivist tradition). Based on Newton’s work building on Kepler’s scientific approach to explaining planetary orbits, Toulmin states, “They showed us what might happen if certain conditions were fulfilled, not what must happen unqualifiedly. They thus drew attention to an intelligible pattern of relationships between apparently unrelated types of happening – the ebb-and-flow of tides, the appearances of comments, the fall of stones, and the motions of the planets” (Toulmin 34).
With much greater humility (and perhaps less critical to our understanding of how humanity fits into the universe...), perhaps Convergence is a similar approach to Kepler/Newton’s combined expertise that illuminates why academia and Washington are functioning as they do in relation to the flow of federal funding and scholarly output. Again, despite an unclear definition, maybe Convergence is doing some work in explaining what has come and perhaps forecasting what is to come. As long as the nature of the idea is not “as flexible as those flamingoes with which Alice tried to play croquet” (Toulmin 35) then continued observance and testing of convergence in this light may be valuable.
In order to bring in some of the other readings from this week, it is worth noting the value of the conclusion that Oreskes’ et al point out in Verification, Validation, and Confirmation of Numerical Models in the Earth Sciences of models – that the value of science, or data, or scientific ideas, like convergence, may not be in its definition or its ability to withstand rigorous scientific tests, but rather as a heuristic (Oreskes 641). For instance, convergence may be a heuristic for culture change in academia and among those traditional funders of basic and applied sciences, and may even be a kind of harbinger to others of the “innovative and forward-thinking nature” of an institution adopting the idea whole-cloth.
I should note here that I’m not clear on how to bring in your piece with Pielke or Pielke’s piece from this week, (though I welcome your thoughts). I could see an angle with Herrick’s piece on “Predictive Modeling of Acid Rain” related to the ambiguous nature of policy questions emerging from Congress and the White House that in turn craft recommendations of an ambiguous nature from scientists through the various professional associations and other pathways through which academics engage in questions of research funding and direction, though I’ll probably make myself stop here for now.
Additional Resources consulted
Wenceslao, J. Gonzalez. (2015). Philosophico-Methodological Analysis of Prediction and its Role in Economics. New York: Springer.