Michael Strevens

To understand why a phenomenon occurs, it is not enough to possess a correct explanation of the phenomenon: you must grasp the explanation. In this formulation, “grasp” is a placeholder, standing for the psychological or epistemic relation that connects a mind to the explanatory facts in such a way as to produce understanding. This paper proposes and defends an account of the “grasping” relation according to which grasp of a property (to take one example of the sort of entity that turns up in explanations) is a matter of recognitional ability: roughly, a property is grasped to the extent to which the would-be understander is capable of recognizing instances of the property.

It is only in the last three centuries that the formidable knowledge-making machine we call modern science has transformed our way of life and our vision of the universe - two thousand years after the invention of law, philosophy, drama and mathematics. Why did we take so long to invent science? And how has it proved to be so powerful?The Knowledge Machine gives a radical answer, exploring how science calls on its practitioners to do something apparently irrational- strip away all previous knowledge - such as theological, metaphysical or political beliefs - and channel unprecedented energy into observation and experiment. In times of climate extremes, novel diseases and rapidly advancing technology, Strevens contends that we need more than ever to grasp the inner workings of our knowledge machine.

A paradigm-shifting work that revolutionizes our understanding of the origins and structure of science. Captivatingly written, interwoven with tantalizing illustrations and historical vignettes ranging from Newton's alchemy to quantum mechanics to the storm surge of Hurricane Sandy, Michael Strevens's wholly original investigation of science asks two fundamental questions: Why is science so powerful? And why did it take so long, two thousand years after the invention of philosophy and mathematics, for the human race to start using science to learn the secrets of nature? The Knowledge Machine's radical answer is that science calls on its practitioners to do something irrational: by willfully ignoring religion, theoretical beauty, and, especially, philosophy-essentially stripping away all previous knowledge-scientists embrace an unnaturally narrow method of inquiry, channeling unprecedented energy into observation and experimentation. Like Yuval Harari's Sapiens or Thomas Kuhn's 1962 classic, The Structure of Scientific Revolutions, The Knowledge Machine overturns much of what we thought we knew about the origins of the modern world.

Dynamic approaches to understanding probability in the non-fundamental sciences turn on certain properties of physical processes that are apt to produce “probabilistically patterned” outcomes. The dynamic properties on their own, however, seem not quite sufficient to explain the patterns; in addition, some sort of assumption about initial conditions must be made, an assumption that itself typically takes a probabilistic form. How should such a posit be understood? That is the problem of initial conditions. Reichenbach, in his doctoral dissertation, floated a Kantian solution to the problem. In this paper I provide a Reichenbachian alternative.

Every model leaves out or distorts some factors that are causally connected to its target phenomenon -- the phenomenon that it seeks to predict or explain. If we want to make predictions, and we want to base decisions on those predictions, what is it safe to omit or to simplify, and what ought a causal model to describe fully and correctly? A schematic answer: the factors that matter are those that make a difference to the target phenomenon. There are several ways to understand differencemaking. This paper advances a view as to which is the most relevant to the forecaster and the decision-maker. It turns out that the right notion of differencemaking for thinking about idealization in prediction is also the right notion for thinking about idealization in explanation; this suggests a carefully circumscribed version of Hempel's famous thesis that there is a symmetry between explanation and prediction.

What is going on under the hood in philosophical analysis, that familiar process that attempts to uncover the nature of such philosophically interesting kinds as knowledge, causation, and justice by the method of posit and counterexample? How, in particular, do intuitions tell us about philosophical reality? The standard, if unappealing, answer is that philosophical analysis is conceptual analysis—that what we learn about when we do philosophy is in the first instance facts about our own minds. Drawing on recent work on the psychology of concepts, this book proposes a new understanding of philosophical analysis, which I call inductive analysis. The thesis that philosophical analysis is inductive analysis explains how novel, substantive philosophical knowledge can be generated in the armchair. It also explains why attempts at philosophical analysis tend to fall short of providing a complete and uncontroversial definition, and supplies reasons not to lament this apparent shortcoming.

Robert Batterman and others have argued that certain idealizing explanations have an asymptotic form: they account for a state of affairs or behavior by showing that it emerges “in the limit”. Asymptotic idealizations are interesting in many ways, but is there anything special about them as idealizations? To understand their role in science, must we augment our philosophical theories of idealization? This paper uses simple examples of asymptotic idealization in population genetics to argue for an affirmative answer and proposes a general schema for asymptotic idealization, drawing on insights from Batterman’s treatment and from John Norton’s subsequent critique.

Maxwell's deduction of the probability distribution over the velocity of gas molecules—one of the most important passages in physics (Truesdell)—presents a riddle: a physical discovery of the first importance was made in a single inferential leap without any apparent recourse to empirical evidence. Tychomancy proposes that Maxwell's derivation was not made a priori; rather, he inferred his distribution from non-probabilistic facts about the dynamics of intermolecular collisions. Further, the inference is of the same sort as everyday reasoning about the physical probabilities attached to such canonical chance setups as tossed coins or rolled dice. The structure of this reasoning is investigated and some simple rules for inferring physical probabilities from symmetries and other causally relevant properties of physical systems are proposed. Not only physics but evolutionary biology and population ecology, the science of measurement error, and climate modeling have benefited enormously from the same kind of reasoning, the book goes on to argue. Inferences from dynamics to probability are so obvious to us, however, that their methodological importance has been largely overlooked.

The dissertation concerns the use of physical probability in higher level scientific theories such as statistical mechanics and evolutionary biology. My focus is complex systems--systems containing large numbers of parts that move independently yet interact strongly, such as gases and ecosystems. Although the underlying dynamics of such systems are prohibitively complex, their macrolevel behavior can often be predicted given information about physical probabilities. ;The technique has the following form: probabilities are assigned to the movements of the elements of the system, and these probabilities are then aggregated to produce a simple macrolevel dynamics. For example, knowing that members of a given class of rabbits all have a probability of one tenth of dying in a given month, it is easy to infer that about one tenth of such rabbits will die in the course of a month. Such reasoning is paramount in both sciences mentioned above. ;My aim is to say what, if the technique is to yield results, these physical probabilities must be. Two problems in particular must be solved: Why are the probabilities stable? That is, why can statistics from one ecosystem be used to infer probabilities for another ecosystem? This question seems especially puzzling given the complexity of the underlying dynamics. Why are the probabilities independent? The independence is perplexing because of the strong causal interactions between various parts of most complex systems. ;I show that the best current view about higher level physical probabilities--the frequency view--is unable to solve these problems. I provide an alternative view, and solve both problems for substantially large classes of systems. The answers to the questions come not so much from considering the composition of my probabilities but rather from an examination of their interrelations. I mean both relations between probabilities at the same level of description and relations between probabilities at different levels

Science's priority rule rewards those who are first to make a discovery, at the expense of all other scientists working towards the same goal, no matter how close they may be to making the same discovery. I propose an explanation of the priority rule that, better than previous explanations, accounts for the distinctive features of the rule. My explanation treats the priority system, and more generally, any scheme of rewards for scientific endeavor, as a device for achieving an allocation of resources among different research programs that provides as much benefit as possible to society. I show that the priority system is especially well suited to finding an efficient allocation of resources in those situations, characteristic of scientific inquiry, in which any success in an endeavor subsequent to the first success brings little additional benefit to society.

Does the Bayesian theory of confirmation put real constraints on our inductive behavior? Or is it just a framework for systematizing whatever kind of inductive behavior we prefer? Colin Howson (Hume's Problem) has recently championed the second view. I argue that he is wrong, in that the Bayesian apparatus as it is usually deployed does constrain our judgments of inductive import, but also that he is right, in that the source of Bayesianism's inductive prescriptions is not the Bayesian machinery itself, but rather what David Lewis calls the ``Principal Principle''.

How to regard the weight we give to a proposition on the grounds of its being endorsed by an authority? I examine this question as it is raised within the epistemology of science, and I argue that “authority-based weight” should receive special handling, for the following reason. Our assessments of other scientists’ competence or authority are nearly always provisional, in the sense that to save time and money, they are not made nearly as carefully as they could be---indeed, they are typically made on the basis of only a small portion of the available evidence. Consequently, we need to represent the authority-based elements of our epistemic attitudes in such a way as to allow the later revision of those elements, in case we decide in the light of new priorities that a more conscientious assessment is warranted. I look to the literature in confirmation theory, statistics, and economics for a semiformal model of this revision process, and make a particular proposal of my own. The discussion also casts some light on the question of why certain aspects of science’s epistemic state are not made public

How can a model that stops short of representing the whole truth about the causal production of a phenomenon help us to understand the phenomenon? I answer this question from the perspective of what I call the simple view of understanding, on which to understand a phenomenon is to grasp a correct explanation of the phenomenon. Idealizations, I have argued in previous work, flag factors that are casually relevant but explanatorily irrelevant to the phenomena to be explained. Though useful to the would-be understander, such flagging is only a first step. Are there any further and more advanced ways that idealized models aid understanding? Yes, I propose: the manipulation of idealized models can provide considerable insight into the reasons that some causal factors are difference-makers and others are not, which helps the understander to grasp the nature of explanatory connections and so to better grasp the explanation itself