Eric Winsberg

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 ascribes that capacity instead to models) and more emphasis on the role of theory in guiding (rather than determining) the construction of models

There are a variety of topics in the philosophy of science that need to be rethought, in varying degrees, after one pays careful attention to the ways in which computer simulations are used in the sciences. There are a number of conceptual issues internal to the practice of computer simulation that can benefit from the attention of philosophers. This essay surveys some of the recent literature on simulation from the perspective of the philosophy of science and argues that philosophers have a lot to learn by paying closer attention to the practice of simulation.

In computer simulations of physical systems, the construction of models is guided, but not determined, by theory. At the same time simulations models are often constructed precisely because data are sparse. They are meant to replace experiments and observations as sources of data about the world; hence they cannot be evaluated simply by being compared to the world. So what can be the source of credibility for simulation models? I argue that the credibility of a simulation model comes not only from the credentials supplied to it by the governing theory, but also from the antecedently established credentials of the model building techniques employed by the simulationists. In other words, there are certain sorts of model building techniques which are taken, in and of themselves, to be reliable. Some of these model building techniques, moreover, incorporate what are sometimes called “falsifications.” These are contrary-to-fact principles that are included in a simulation model and whose inclusion is taken to increase the reliability of the results. The example of a falsification that I consider, called artificial viscosity, is in widespread use in computational fluid dynamics. Artificial viscosity, I argue, is a principle that is successfully and reliably used across a wide domain of fluid dynamical applications, but it does not offer even an approximately “realistic” or true account of fluids. Artificial viscosity, therefore, is a counter-example to the principle that success implies truth – a principle at the foundation of scientific realism. It is an example of reliability without truth.

We present a Bayesian analysis of the epistemology of analogue experiments with particular reference to Hawking radiation. Provided such experiments can be externally validated via universality arguments, we prove that they are confirmatory in Bayesian terms. We then provide a formal model for the scaling behaviour of the confirmation measure for multiple distinct realisations of the analogue system and isolate a generic saturation feature. Finally, we demonstrate that different potential analogue realisations could provide different levels of confirmation. Our results thus provide a basis both to formalise the epistemic value of analogue experiments that have been conducted and to advise scientists as to the respective epistemic value of future analogue experiments.

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.

Over the last several years, there has been an explosion of interest and attention devoted to the problem of Uncertainty Quantification (UQ) in climate science—that is, to giving quantitative estimates of the degree of uncertainty associated with the predictions of global and regional climate models. The technical challenges associated with this project are formidable, and so the statistical community has understandably devoted itself primarily to overcoming them. But even as these technical challenges are being met, a number of persistent conceptual difficulties remain. So why is UQ so important in climate science? UQ, I would like to argue, is first and foremost a tool for communicating knowledge from experts to policy makers in a way that is meant to be free from the influence of social and ethical values. But the standard ways of using probabilities to separate ethical and social values from scientific practice cannot be applied in a great deal of climate modeling, because the roles of values in creating the models cannot be discerned after the fact—the models are too complex and the result of too much distributed epistemic labor. I argue, therefore, that typical approaches for handling ethical/social values in science do not work well here.

We call attention to an underappreciated way in which non-epistemic values influence evidence evaluation in science. Our argument draws upon some well-known features of scientific modeling. We show that, when scientific models stand in for background knowledge in Bayesian and other probabilistic methods for evidence evaluation, conclusions can be influenced by the non-epistemic values that shaped the setting of priorities in model development. Moreover, it is often infeasible to correct for this influence. We further suggest that, while this value influence is not particularly prone to the problem of wishful thinking, it could have problematic non-epistemic consequences in some cases.

This paper examines the relationship between simulation and experiment. Many discussions of simulation, and indeed the term "numerical experiments," invoke a strong metaphor of experimentation. On the other hand, many simulations begin as attempts to apply scientific theories. This has lead many to characterize simulation as lying between theory and experiment. The aim of the paper is to try to reconcile these two points of viewto understand what methodological and epistemological features simulation has in common with experimentation, while at the same time keeping a keen eye on simulation's ancestry as a form of scientific theorizing. In so doing, it seeks to apply some of the insights of recent work on the philosophy of experiment to an aspect of theorizing that is of growing philosophical interest: the construction of local models.

This paper discusses a crisis of accountability that arises when scientific collaborations are massively epistemically distributed. We argue that social models of epistemic collaboration, which are social analogs to what Patrick Suppes called a “model of the experiment,” must play a role in creating accountability in these contexts. We also argue that these social models must accommodate the fact that the various agents in a collaborative project often have ineliminable, messy, and conflicting interests and values; any story about accountability in a massively distributed collaboration must therefore involve models of such interests and values and their methodological and epistemic effects

In this article we argue for the existence of ‘analogue simulation’ as a novel form of scientific inference with the potential to be confirmatory. This notion is distinct from the modes of analogical reasoning detailed in the literature, and draws inspiration from fluid dynamical ‘dumb hole’ analogues to gravitational black holes. For that case, which is considered in detail, we defend the claim that the phenomena of gravitational Hawking radiation could be confirmed in the case that its counterpart is detected within experiments conducted on diverse realizations of the analogue model. A prospectus is given for further potential cases of analogue simulation in contemporary science. 1 Introduction2 Physical Background2.1 Hawking radiation in semi-classical gravity2.2 Modelling sound in fluids2.3 The acoustic analogue model of Hawking radiation3 Simulation and Analogy in Physical Theory3.1 Analogical reasoning and analogue simulation3.2 Confirmation via analogue simulation3.3 Recapitulation4 The Sound of Silence: Analogical Insights into Gravity4.1 Experimental realization of analogue models4.2 Universality and the Hawking effect4.3 Confirmation of gravitational Hawking radiation5 Prospectus

This paper explores some connections between competing conceptions of scientific laws on the one hand, and a problem in the foundations of statistical mechanics on the other. I examine two proposals for understanding the time asymmetry of thermodynamic phenomenal: David Albert's recent proposal and a proposal that I outline based on Hans Reichenbach's “branch systems”. I sketch an argument against the former, and mount a defense of the latter by showing how to accommodate statistical mechanics to recent developments in the philosophy of scientific laws.

Simulations (both digital and analog) and experiments share many features. But what essential features distinguish them? I discuss two proposals in the literature. On one proposal, experiments investigate nature directly, while simulations merely investigate models. On another proposal, simulations differ from experiments in that simulationists manipulate objects that bear only a formal (rather than material) similarity to the targets of their investigations. Both of these proposals are rejected. I argue that simulations fundamentally differ from experiments with regard to the background knowledge that is invoked to argue for the “external validity” of the investigation.

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...

In his recent book, Time and Chance, David Albert claims that by positing that there is a uniform probability distribution defined, on the standard measure, over the space of microscopic states that are compatible with both the current macrocondition of the world, and with what he calls the “past hypothesis”, we can explain the time asymmetry of all of the thermodynamic behavior in the world. The principal purpose of this paper is to dispute this claim. I argue that Albert's proposal fails in his stated goal—to show how to use the time‐reversible dynamics of Newtonian physics to “underwrite the actual content of our thermodynamic experience” (Albert 2000, 159). Albert's proposal can satisfactorily explain why the overall entropy of the universe as a whole is increasing, but it does not and cannot explain the increasing entropy of relatively small, relatively short‐lived systems in energetic isolation without making use of a principle that leads to reversibility objections.

Violations of the Bell inequalities in EPR-Bohm type experiments have set the literature on the metaphysics of microscopic systems to flirting with some sort of metaphysical holism regarding spatially separated, entangled systems. The rationale for this behavior comes in two parts. The first part relies on the proof, due to Jon Jarrett [2] that the experimentally observed violations of the Bell inequalities entail violations of the conjunction of two probabilistic constraints. Jarrett called these two constraints locality and completeness. We prefer the terminology of locality and factorizability.[3] The first part of the rationale for metaphysical holism urges that only Jarrett’s locality allows for “peaceful coexistence” between any model of EPR-Bohm type experiments and special relativity. Factorizability, it is suggested, must be jettisoned.

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.

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]

In a large and impressive body of published work, Quayshawn Spencer has meticulously articulated and defended a metaphysical project aimed at resuscitating a biological conception of race—one free from many of the pitfalls of biological essentialism. If successful, such a project would be highly rewarding, since it would provide a compelling response to philosophers who have denied the genuine existence of race while avoiding the very dangers that they sought to avoid. The aim of this paper is to subject those moves to careful scrutiny and thereby appraise the prospects for a new biologism about race.

The ArgumentIn 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. In this paper I argue that this process of transformation has its own epistemology. I also argue that this kind of epistemology is unfamiliar to most philosophy of science, which has traditionally concerned itself with the justification of theories, not with their application. Finally, I urge that the nature of this epistemology suggests that the end results of some simulations do not bear a simple, straightforward relation to the theories from which they stem.

There continues to be a vigorous public debate in our society about the status of climate science. Much of the skepticism voiced in this debate suffers from a lack of understanding of how the science works - in particular the complex interdisciplinary scientific modeling activities such as those which are at the heart of climate science. In this book Eric Winsberg shows clearly and accessibly how philosophy of science can contribute to our understanding of climate science, and how it can also shape climate policy debates and provide a starting point for research. Covering a wide range of topics including the nature of scientific data, modeling, and simulation, his book provides a detailed guide for those willing to look beyond ideological proclamations, and enriches our understanding of how climate science relates to important concepts such as chaos, unpredictability, and the extent of what we know.

Should philosophers of science be paying attention to developments in "nanoscience"? Undoubtedly, it is too early to tell for sure. The goal of this paper is to take a preliminary look. In particular, I look at the use of computational models in the study of nano-sized solid-state materials. What I find is that there are features of these models that appear on their face to be at odds with some basic philosophical intuitions about the relationships between different theories and between theories and their models. My conclusion is that developments in nanoscience are not an unlikely place for novel insights in the philosophy of science to emerge.

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.