Eric Winsberg

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

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?

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.

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.

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

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.

Statistical Mechanics (SM) involves probabilities. At the same time, most approaches to the foundations of SM—programs whose goal is to understand the macroscopic laws of thermal physics from the point of view of microphysics—are classical; they begin with the assumption that the underlying dynamical laws that govern the microscopic furniture of the world are (or can without loss of generality be treated as) deterministic. This raises some potential puzzles about the proper interpretation of these probabilities. It also raises, more generally, the question of what kinds, if any, of objective probabilities can exist in a deterministic world.

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.

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.

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.

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]

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.

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.

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.