My philosophical research is about scientific methodology, and in particular how scientists build mathematical models to acquire knowledge about complex systems in the world. Typical questions I am interested in exploring are: How do scientists choose and identify phenomena of interest? What kind of assumptions are used to build models of phenomena, and how are they justified?
I use examples from contemporary sciences and their history to answer these questions. My dissertation, for example, draws from case studies in atmospheric science and oceanography to illustrate the challenges that are tied to identifying phenomena, and what kind of assumptions are made in the process of isolating target systems from their environment.
I am also interested in the social and political aspects of climate science, such as issues at the intersection of epistemology, environmental ethics and public policy. At the University of Leeds, for example, I am working with an interdisciplinary research group from the Center for Climate Change Economics and Policy on developing a conceptual framework to analyze the quality of climate information for adaptation. This framework draws on insights from philosophy of science, environmental social science and physical climate science.
Baldissera Pacchetti, M. (2018). A role for spatiotemporal scales in modeling. Studies in History and Philosophy of Science Part A, 67, 14-21.
Work in Progress
“Spatiotemporal scales and structural uncertainty”
In this paper I explore the epistemic consequences of the use of scale related assumptions in characterizing phenomena for what some philosophers and climate scientists have called ‘Structural Uncertainty’, which is uncertainty about the form of the equations that represent the phenomenon of interest. I argue that structural uncertainty can arise when there is no clear empirical justification for the use of scale related assumptions. The lack of justification may arise because either the data does not clearly display scale separation, which allows scientists to justify excluding certain smaller scale physics, or because the phenomenon is thought to occur on many different scales.
“Trust and values at the science-policy interface”
I argue that theories of trust can clarify the role of value judgements in the interaction between scientists and policy makers regarding climate science and uncertainty. Theories of trust are a social science tool for analyzing the trust relations between individuals (interpersonal trust) and between organizations (organizational trust). After describing the key differences between the procedural and structural characteristics of science and policy making, I explore some of the main ideas of theories of trust. Different forms of trust (procedural, affinitive, dispositional, rational) describe the trust relationship that develops between policy makers and scientists. I suggest that these forms of trust help identify what value judgements enter the decision-making process at the science-policy interface. A breakdown in trust can damage the relationship between scientists and policy makers, and I discuss a breakdown in procedural trust, a form of trust that arises from the trustor’s reliance on the rules of knowledge production of the trustee. The trustor is usually an individual, and the trustee can be either an individual or an institution. The breakdown can result from a misalignment of value judgments in knowledge co-production, which is a misalignment in epistemic value judgements, and from differences in incentive structures for scientists and policy makers. The difference in incentive structure can influence epistemic and ethical value judgements of both scientists and policy makers. Finally, I suggest that deep uncertainty is a special case of breakdown in procedural trust that arises from a misalignment of value judgements about what counts as reliable information.
“Assessing the epistemic quality of climate information for adaptation” (with Suraje Dessai, Seamus Bradley and Dave Stainforth)
There are now a plethora of data, models and approaches available to produce climate information intended to inform adaptation to a changing climate. There is, however, no analytical framework to assess the epistemic issues concerning the quality of these data, models and approaches. An evaluation of the quality of climate information is a fundamental requirement for its appropriate application in societal decision-making. By integrating insights from the philosophy of science, environmental social science and physical climate science, we construct an analytical framework for “science-based statements about future climate” that allows for an assessment of their quality for adaptation planning. We target statements about local and regional climate with a lead time of one to one hundred years. Our framework clarifies how standard quality descriptors in the literature, such as “robustness”, “adequacy”, “completeness” and “transparency” rely on both the type of evidence and the relationship between the evidence and the statement. This clarification not only provides a more precise framework for quality, but also allows us to show how certain evidential standards may change as a function of the purpose of a statement. We argue that the most essential metrics to assess quality are: Robustness, Theory, Completeness, Adequacy for purpose, Transparency. Our framework goes further by providing guidelines on when quantitative statements about future climate are warranted and potentially decision-relevant, when these statements would be more valuable taking other forms (e.g. qualitative statements), and when statements about future climate are not warranted at all.
“Revisiting the distinction between predictions and projections” (with Suraje Dessai, Seamus Bradley and Dave Stainforth)
Abstract available upon request.