I am a general philosopher of science with specific interests in climate science and environmental philosophy. My research analyzes scientific practice when it aims for social relevancy. I approach this topic by examining the status of computer simulation models, the role of values in scientific practice, and the place of the sciences in a democratic society. I focus especially on climate science, where my contributions examine how to best interpret climate-related data for use in democratic decision-making. A new project extends my research by investigating how social values should shape scientific advice on climate policy. Any adequate treatment of these topics requires an interdisciplinary perspective: my research not only employs the styles of analysis familiar to philosophers, but also involves collaborations with scientists, political scholars, and other humanists.


The Status of Climate Models and Measurements

Climate scientists often employ investigative techniques that are removed from the physical systems they study. Remote sensing, for example, relies on a long series of computer-aided inferences to indirectly infer trace gas concentrations, while climate simulations are thought to never interact with the systems they emulate. Philosophers and scientists commonly think that this lack of interaction with the physical world diminishes the potential power these kinds of investigations have to create knowledge. Through a careful examination of the modeling involved in remote sensing and simulation, I argue for a new understanding where the knowledge-making potential of these techniques is on par with traditional forms of measurement and experiment. To this end, I formulate an account of scientific investigation where epistemic power varies with the control and quantification of uncertainty. On this account, remote measurements have the potential to be just as powerful as their non-remote counterparts, as do some computer simulations, which can potentially serve as complex forms of empirical measurement.

In reevaluating philosophical approaches to scientific investigation, this project contributes to philosophy of computer simulation and the philosophy of measurement, two areas of increasing interest in philosophy of science. A significant advantage of this approach is that it can explain the displacement of traditional observational studies by computer-based investigations, a phenomenon that seems irrational on other accounts. .

Model Uncertainty and Climate-Related Decision-Making

Political agreement is difficult to achieve, especially when deep uncertainty exists. This research – part of a Mellon Foundation project – examines how society should cope with the uncertainties associated with climate-related information. Thus far, I have examined proposals that place the attribution of extreme weather events at the center of adaptation policy. These proposals claim that computer simulations can be used to blame particular weather events on anthropogenic factors, which in turn would identify victims of climate change, a status that should grant special benefits in the name of justice. While I share a desire for climate justice, my research finds that these proposals are misplaced, and likely overlook victims of climate change deserving recognition. I examine the methods used to attribute extreme weather to climate change, finding that their uncertainties are often understated. Furthermore, I argue that differentiating climate-influenced storms from “bad luck weather” is inappropriate for grounding climate victimhood. What is morally relevant for victimhood is not the origin of a storm, but the origin of harm; since attribution-based approaches track the former not the latter, they are likely to overlook climate victims deserving recognition. These critiques suggest that the purported utility of event attribution is exaggerated. Results of this research contribute to philosophy of science and public policy debates, and have been published in the interdisciplinary science journal Climatic Change. 

The next phase of this research will investigate claims that structural model uncertainties, that is uncertainty about what to represent and how to represent it, undermine the policy-relevancy of climate model results. Some philosophers and mathematicians claim these uncertainties are a “poison pill” that severely undermines the accuracy of climate model predictions. I will argue that the situation is not as dire as it seems: policy-relevancy can be secured by framing decision problems such that responses would be robust across a wide range of outcomes. This will help show that a potentially skeptical position has less bite than previously thought.

The Role of Scientific Expertise and Social Values in Democracy 

Recently, interest in the role of social values in science-informed decision-making has been revitalized. Philosophers have argued that scientists inevitably appeal to social values when making methodological choices during the production of decision-relevant information. If so, there is a democratic objection to the use of science in public decision-making: scientists overstep their authority by imbuing decision-relevant information with their social values, essentially circumventing elected representatives and steering decision-making through the back door. Overcoming this objection requires showing that scientific advice is democratically legitimate.

Building on my co-authored paper examining the current crisis of trust in expertise, my next project will investigate which values scientists can legitimately invoke when producing policy advice. I will argue that scientists have a social duty to accurately represent the current state of scientific knowledge when advising decision makers. I will show that consulting a diverse set of scientific experts and granting them discretion over the values employed is often the best way to discharge this duty. To illustrate my position, I will employ a case study of the elicitation of expert judgement used to compensate for uncertainties in climate model projections. I claim that extant accounts of social values in science, which primarily recommend that democratic bodies prescribe permissible values, would seriously impoverish the quality of decision-making on technical issues like climate projections where deep uncertainties exist. I will demonstrate that amalgamating judgments from expert scientists with a diverse set of social values can secure superior knowledge while displaying a kind of objectivity that would permit the resulting information to be legitimately deployed in democratic decision-making.


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