Eric Winsberg

Collaborative authorship is the overwhelming norm in science. Yet philosophical issues that arise in this context have received little direct attention. The chapter examines several difficulties inherent in establishing authorship in the context of collaborative research. Using case studies, the chapter considers collaborative research that relies on multiple authors, collaborative research with a single author and many collaborators, and radically collaborative research that is distributed widely over disciplinary expertise, time, and space. The chapter argues that the first two types of collaborative research leave a standard understanding of authorship untouched, while the third yields a novel class of significant challenges for our common understanding of authorship.

This chapter argues that deflationism is in the first instance a claim about the explanatory role that the concept of truth and the truth-maker/truth-bearer relationship ought to play in philosophy and kindred disciplines—more specifically, the claim that there is no such role. We propose a strict criterion for what counts as a deflationary theory of truth and we use our strict criterion to distinguish between deflationist and pragmatic theories of truth. We also use it as a tool for sorting out the debate over deflationism and so-called non-factive discourse. We argue that deflationist commitments are orthogonal to debates over the metaphysical status of domains of discourse such as ethics and theoretical science, whose factive _bona fides_ have been called into question. The metaphysical consequences of deflationism are, we argue, quite minimal; we think that both sympathizers and detractors have taken deflationism as having more robust metaphysical consequences than it in fact has.

This paper examines scientific gatekeeping during the COVID-19 pandemic through two key episodes: the suppression of the Great Barrington Declaration’s critique of non-pharmaceutical interventions (NPIs) and the pre-mature, prejudicial dismissal of the lab leak hypothesis regarding SARS-CoV-2‘s origins. Drawing on public choice theory, I argue that scientist-bureaucrats’ gatekeeping behaviors were motivated not solely by epistemic goals or public good, but by three distinct incentives: enhancing public perception of their importance, increasing political influence, and protecting captured resources. Analysis of communications between key figures like Fauci, Collins, and Anderson, along with the history of early 21st century pandemic preparedness debates in the United States, reveals discrepancies between private uncertainties and public pronouncements. The paper proposes a “public choice philosophy of science” framework to understand how scientist-bureaucrats operate when their avowed goal is advancing public good rather than knowledge. This approach helps explain why scientific disagreement was suppressed during the pandemic despite its value for both scientific progress and public trust.

A collection of newly commissioned papers on themes from David Albert's Time and Chance (HUP, 2000), with replies by Albert. Introduction [Barry Loewer, Brad Weslake, and Eric Winsberg] I. Overview of Time and Chance 1. The Mentaculus: A Probability Map of the Universe [Barry Loewer] II. Philosophical Foundations 2. The Metaphysical Foundations of Statistical Mechanics: On the Status of PROB and PH [Eric Winsberg] 3. The Logic of the Past Hypothesis [David Wallace] 4. In What Sense Is the Early Universe Fine-Tuned? [Sean M. Carroll] 5. The Meta-Reversibility Objection [Christopher J. G. Meacham] 6. Typicality versus Humean Probabilities as the Foundation of Statistical Mechanics [Dustin Lazarovici] 7. The Past Hypothesis and the Nature of Physical Laws [Eddy Keming Chen] 8. On the Albertian Demon [Tim Maudlin] III. Underwriting the Asymmetries of Knowledge and Intervention 9. Reading the Past in the Present [Nick Huggett] 10. Causes, Randomness, and the Past Hypothesis [Mathias Frisch] 11. Time, Flies, and Why We Can’t Control the Past [Alison Fernandes] 12. The Concept of Intervention in Time and Chance [Sidney Felder] Conclusion [David Albert]

Research funders increasingly require integration of future climate projections across health, agriculture, fisheries, and development economics, creating perverse incentives: institutions demand what current climate science cannot reliably deliver. I use “perverse incentive” here in its standard economic sense: an incentive that unintentionally produces counterproductive behavior, rather than implying ill will on the part of funders. Climate models designed for global, long-term analysis are being misapplied for short-term, regional uses beyond their validated scope. This paper identifies three problems arising from this mismatch: maladaptation in scientific labor allocation, erosion of trustworthiness through representational overextension, and representational risk from harmful signaling and normalization of inappropriate methodological norms. Researchers include climate projections not because they are justified, but because they are required, transforming models from tools of inquiry into performances of compliance. This threatens both scientific integrity and the legitimacy of science underwritten by democratic norms. Three institutional reforms are proposed to realign incentives with epistemic responsibility and ensure climate science serves as a reliable policy foundation rather than mere signaling.

ABSTRACT: There are many proposals in the literature on how to “manage values.” Many of these proposals have in common the assumption that the relevant values in science can be “packaged for transfer”: they can be put in an envelope for scientists to hand to stakeholders or policymakers, or for members of the public or ethical experts to hand to scientists. The central aim of this paper is to argue that packaging values for transfer is a practical impossibility. The central argument of the paper concerns the best stance to take on how values in science should be conceptualized. Specifically, I argue that we need to return to a decision-theoretic definition of values (as, I argue, is strongly suggested by a close reading of Rudner, Jeffrey, and Hempel.) Further, I argue for a picture of these values that is nonpsychologistic, stance relative, and always and everywhere entangled with credences. I call my account of the nature of values in science the Putnam-Hempel account. Unfortunately, the Putnam-Hempel account forces us to see that any proposal that depends on packaging values for transfer will ultimately suffer from great difficulties.

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.

Scientific modelling is a value-laden process: the decisions involved can seldom be made using ‘scientific’ criteria alone, but rather draw on social and ethical values. In this paper, we draw on a body of philosophical literature to analyze a COVID-19 vaccination model, presenting a case study of social and ethical value judgments in health-oriented modelling. This case study urges us to make value judgments in health-oriented models explicit and interpretable by non-experts and to invite public involvement in making them.

Scientific modelling is a value-laden process: the decisions involved can seldom be made using 'scientific' criteria alone, but rather draw on social and ethical values. In this paper, we draw on a body of philosophical literature to analyze a COVID-19 vaccination model, presenting a case study of social and ethical value judgments in health-oriented modelling. This case study urges us to make value judgments in health-oriented models explicit and interpretable by non-experts and to invite public involvement in making them.

In this paper, we explore the extent to which issues of simulation model validation take on novel characteristics when the models in question become particularly complex. Our central claim is that complex simulation models in general, and global models of climate in particular, face a form of confirmation holism. This holism, moreover, makes analytic understanding of complex models of climate either extremely difficult or even impossible. We argue that this supports a position we call convergence skepticism: the belief that the existence of a plurality of different models making a plurality of different forecasts of future climate is likely to be a persistent feature of global climate science.

This Element introduces the philosophical literature on models, with an emphasis on normative considerations relevant to models for decision-making. Chapter 1 gives an overview of core questions in the philosophy of modeling. Chapter 2 examines the concept of model adequacy for purpose, using three examples of models from the atmospheric sciences to describe how this sort of adequacy is determined in practice. Chapter 3 explores the significance of using models that are not adequate for purpose, including the purpose of informing public decisions. Chapter 4 provides a basic framework for values in modelling, using a case study to highlight the ethical challenges in building models for decision making. It concludes by establishing the need for strategies to manage value judgments in modelling, including the potential for public participation in the process.

Sovereign is he who provides the exception.…The exception is more interesting than the rule. The rule proves nothing; the exception proves everything. In the exception the power of real life breaks through the crust of a mechanism that has become torpid by repetition.In spring 2020, in response to the COVID-19 crisis, world leaders imposed severe restrictions on citizens’ civil, political, and economic liberties. These restrictions went beyond less controversial and less demanding social distancing measures seen in past epidemics. Many states and countries imposed universal lockdowns. Lockdowns, as we define them here, require people to stay home; in some countries and places, citizens must have ad hoc licenses to...

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.

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.’

How diverse is philosophy? In this paper we explore recent data on the racial, ethnic, and gender diversity of philosophy students and faculty in the United States. We have found that women are underrepresented in philosophy at all levels from first-year intention to major through senior faculty. The past four years have seen an increase in the percentage of women philosophy majors at the undergraduate level, but it remains to be seen if this recent increase in the percentage of women will eventually also show at more advanced stages. Temporal trends since 2000 also show substantial increases in the racial and ethnic diversity of philosophy majors at all levels of education from first-year undergraduate through PhD, with substantial declines in the percentage of non-Hispanic White philosophy majors and substantial increases in all other commonly measured racial/ethnic groups except for Native American / Alaska Native. Despite generally increasing diversity, people who identify as Hispanic (any race), or non-Hispanic Native American, Alaska Native, or Black remain substantially underrepresented in philosophy at all levels compared to their presence in the U.S. population, and in some cases also as compared to other majors.

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.

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.

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”.