Eric Winsberg

1. Introduction; Elisabeth A. Lloyd and Eric Winsberg.- Section 1: Confirmation and Evidence.- 2. The Scientific Consensus on Climate Change: How Do We Know We’re Not Wrong?; Naomi Oreskes.- 3. Satellite Data and Climate Models Redux.- 3a. Introduction to Chapter 3: Satellite Data and Climate Models; Elisabeth A. Lloyd.- Ch. 3b Fact Sheet to "Consistency of Modelled and Observed Temperature Trends in the Tropical Troposphere"; Benjamin D. Santer et al..- Ch. 3c Reprint of "Consistency of Modelled and Observed Temperature Trends in the Tropical Troposphere"; Benjamin D. Santer et al..- 4. The Role of ’Complex’ Empiricism in the Debates about Satellite Data and Climate Models; Elisabeth A. Lloyd.- 5. Reconciling Climate Model/Data Discrepancies: The Case of the Trees That Didn’t Bark; Michael Mann.- 6. Downscaling of Climate Information; Linda O. Mearns et al..- Section 2: Uncertainties and Robustness.- 7. The Significance of Robust Model Projections; Wendy S. Parker.- 8. Building Trust, Removing Doubt? Robustness Analysis and Climate Modeling; Jay Odenbaugh.- Section 3: Climate Models as Guides to Policy.- 9. Climate Model Confirmation: From Philosophy to Predicting Climate in the Real World; Reto Knutti.- 10. Uncertainty in Climate Science and Climate Policy; Jonathan Rougier and Michel Crucifix.- 11. Communicating Uncertainty to Policy Makers: The Ineliminable Role of Values; Eric Winsberg.- 12. Modeling Climate Policies: A Critical Look at Integrated Assessment Models; Mathias Frisch.- 13. Modelling Mitigation and Adaptation Policies to Predict their Effectiveness: The Limits of Randomized Controlled Trials; Alexandre Marcellesi and Nancy D. Cartwright.

Using an example of a computer simulation of the convective structure of a red giant star, this paper argues that simulation is a rich inferential process, and not simply a “number crunching” technique. The scientific practice of simulation, moreover, poses some interesting and challenging epistemological and methodological issues for the philosophy of science. I will also argue that these challenges would be best addressed by a philosophy of science that places less emphasis on the representational capacity of theories and more emphasis on the role of theory in guiding the construction of models.

All of the pundits, prognosticators, and policymakers are in agreement: research into the science and technology of the nano-scale is going to be one of the hot scientific topics of the 21st Century. According to the web page of the National Nanotechnology Initiative, moreover, this should make nanotechnology and nano-science “of great interest to philosophers.” Admittedly, the kind of philosophers being imagined by the authors of the initiative web page are most likely something like the nano-technological analogues of bio-ethicists—not the kind philosophers that typically convene at the meeting of the Philosophy of Science Association. But what about us?

This paper deals with the question of whether uncertainty regarding model structure, especially in climate modeling, exhibits a kind of “chaos.” Do small changes in model structure, in other words, lead to large variations in ensemble predictions? More specifically, does model error destroy forecast skill faster than the ordinary or “classical” chaos inherent in the real-world attractor? In some cases, the answer to this question seems to be “yes.” But how common is this state of affairs? Are there precise mathematical results that can help us answer this question? And is dependence on model structure “sensitive” in that arbitrarily small errors can destroy forecast skill? We examine some efforts in the literature to answer this last question in the affirmative and find them to be unconvincing.

One of the most interesting things about science and engineering at the nanoscale, from the point of view of the philosophy of science, is the frequent use they make of models constructed out of theories belonging to different levels of description. We usually take it for granted that every level of description falls under the domain of its own theory. For example, we generally presume there is some fundamental level of description. And with that presumption comes the hope that we will be able to find a general theory of how things work at that level. But we also often take it for granted that at every other level of description that interests us—whether it be at the level of subatomic particles, atoms, fundamental theory molecules, fluids or mechanical solids—there will be some non-fundamental theory available to us that will be practically serviceable for explaining, predicting, and controlling the various phenomena that live at that level of description.

In the philosophy of climate science, debate surrounding the issue of variety of evidence has mostly taken the form of attempting to connect these issues in climate science and climate modeling with philosophical accounts of what has come to be known as “robustness analysis.” I argue that an “explanatory” conception of robustness is the best candidate for understanding variety of evidence in climate science. I apply the analysis to both examples of model agreement, as well at to the convergence of evidence from both model and non-model sources.

Roman Frigg and others have developed a general epistemological argument designed to cast doubt on the capacity of a broad range of mathematical models to generate “decision relevant predictions.” In this article, we lay out the structure of their argument—an argument by analogy—with an eye to identifying points at which certain epistemically significant distinctions might limit the force of the analogy. Finally, some of these epistemically significant distinctions are introduced and defended as relevant to a great many of the predictive mathematical modeling projects employed in contemporary climate science.

Throughout the Covid-19 pandemic, people have been eager to learn what factors, and especially what public health policies, cause infection rates to wax and wane. But figuring out conclusively what causes what is difficult in complex systems with nonlinear dynamics, such as pandemics. We review some of the challenges that scientists have faced in answering quantitative causal questions during the Covid-19 pandemic, and suggest that these challenges are a reason to augment the moral dimension of conversations about causal inference. We take a lesson from Martha Nussbaum—who cautions us not to think we have just one question on our hands when we have at least two—and apply it to modeling for causal inference in the context of cost-benefit analysis.

This paper argues that the classification of propositional attitudes into the de re, de dicto, and de se is incomplete. De se attitudes are widely agreed to be closely connected to de re attitudes. But there is a species of belief that is linked to agent-centered action in the way that de se beliefs are, but is also associated with entities, places, and especially times, under a description. These mark out a fourth kind. One way to think about what makes them distinctive is that, despite being ‘essentially indexical’ they can be retained across different contexts of evaluation without having diagonal content. The paper also discusses the connection between such beliefs and the kinds of utterances (like ‘Don't put off for tomorrow what you can do today’) that are contested as examples of what David Kaplan called ‘monsters.’

In a series of recent papers, two of which appeared in this journal, a group of philosophers, physicists, and climate scientists have argued that something they call the `hawkmoth effect' poses insurmountable difficulties for those who would use non-linear models, including climate simulation models, to make quantitative predictions or to produce `decision-relevant probabilites.' Such a claim, if it were true, would undermine much of climate science, among other things. Here, we examine the two lines of argument the group has used to support their claims. The first comes from a set of results in dynamical systems theory associated with the concept of `structural stability.' The second relies on a mathematical demonstration of their own, using the logistic equation, that they present using a hypothetical scenario involving two apprentices of Laplace's omniscient demon. We prove two theorems that are relevant to their claims, and conclude that both of these lines of argument fail. There is nothing out there that comes close to matching the charac.

Climate science evaluates hypotheses about the climate using computer simulations and complex models. The models that drive these simulations, moreover, represent the efforts of many different agents, and they arise from a compounding set of methodological choices whose effects are epistemically inscrutable. These facts, I argue in this chapter, make it extremely difficult for climate scientists to estimate the degrees of uncertainty associated with these hypotheses that are free from the influences of past preferences—preferences both with regard to importance of one prediction over another and with regard to avoidance of false positive over false negatives and vice versa. This leaves an imprint of non-epistemic values in the nooks and crannies of climate science.

Recently, research into the possibilities of developing solar radiation management and other geoengineering technologies has gained new momentum. Just last year, Cambridge University announced the opening of a “Centre for Climate Repair” as part of the university’s Carbon Neutral Futures Initiative. Recent modeling work gives hope that SRM could confer more benefits than previously thought. But opposition to even conducting research into SRM remains strong. I use the case study of SRM to develop a framework, based on a theorem by I.J. Good, for thinking about the benefits and costs of acquiring new evidence and for thinking about the conditions under which new evidence could be harmful. I argue that the expected benefits of supporting public research in SRM technologies outweigh the expected costs and harms.

As we advance into the twenty-first century, the evidence of climate change is all around us. In the introduction to this volume, we discuss some of the successes of climate science in understanding and attributing the causes of these changes, as well as some of the challenges it faces in addressing questions for which we do not yet have the answers. We focus on the role of climate models and the philosophical and conceptual problems facing climate modelers and climate modeling. We then give the reader an outline of what they will find in each of the 15 Chapters in the volume. The book is divided into three major parts: Part 1 “Confirmation and Evidence”; Part 2 “Uncertainties and Robustness”; and Part 3 “Climate Models as Guides to Policy”.

In its reconstruction of scientific practice, philosophy of science has traditionally placed scientific theories in a central role, and has reduced the problem of mediating between theories and the world to formal considerations. Many applications of scientific theories, however, involve complex mathematical models whose constitutive equations are analytically unsolvable. The study of these applications often consists in developing representations of the underlying physics on a computer, and using the techniques of computer simulation in order to learn about the behavior of these systems. In many instances, these computer simulations are not simple number-crunching techniques. They involve a complex chain of inferences that serve to transform theoretical structures into specific concrete knowledge of physical systems. At the heart of this chain of inferences is the process of building what I call a hierarchy of models. This hierarchy includes a mechanical model, a dynamical model, ad hoc models , a computational model, and finally, a model of the phenomena. ;I argue that this process of transformation is also a process of knowledge creation, and that it has its own epistemology. It is an epistemology that has been ignored by most philosophy of science, which has traditionally concerned itself with the justification of theories, not with their application. I also argue that the complex and motley nature of this epistemology suggests that the end results of simulations often do not bear a simple, straightforward relation to the theories from which they derive. Accordingly, I urge philosophers of science to examine more carefully the process of 'theory articulation.' Theory articulation is a relatively neglected aspect of scientific practice, but it plays a role that is often as crucial, as complex, and as creative as experiment. Finally, I examine in detail the last model in the hierarchy, the model of the phenomena. I argue that these models provide understanding of the systems they model. The character of the understanding they provide, however, is not well accounted for in the philosophical literature on scientific explanation