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

Research programs regularly compete to achieve the same goal, such as the discovery of the structure of DNA or the construction of a TEA laser. The more the competing programs share information, the faster the goal is likely to be reached, to society's benefit. But the "priority rule"—the scientific norm mandating that the first program to reach the goal in question receive all the credit for the achievement—provides a powerful disincentive for programs to share information. How, then, is the clash between social and self interest resolved in scientific practice? This paper investigates what Robert Merton called science's "communist" norm, which mandates universal sharing of knowledge, and uses mathematical models of discovery to argue that a communist regime may be on the whole advantageous and fair to all parties, and so might be implemented by a social contract that all scientists would be willing to sign.

The concept of causation plays a central role in many philosophical theories, and yet no account of causation has gained widespread acceptance among those who have investigated its foundations. Theories based on laws, counterfactuals, physical processes, and probabilistic dependence and independence relations (the list is by no means exhaustive) have all received detailed treatment in recent years---{}and, while no account has been entirely successful, it is generally agreed that the concept has been greatly clari{}ed by the attempts. In this magni{}cent book, Woodward aims to give a uni{}ed account of causation and causal explanation in terms of the notion of a manipulation (or intervention, terms which can be read interchangeably). Not only does he produce in my view the most illuminating and comprehensive account of causation on o{}er, his theory also opens a great many avenues for future work in the area, and has rami{}cations for many other areas of philosophy. Making Things Happen ought to be of interest not only to philosophers of causation and philosophers of science, but to any philosopher whose concerns involve assumptions about the nature of causation, laws, or explanation

According to principles of probability coordination, such as Miller's Principle or Lewis's Principal Principle, you ought to set your subjective probability for an event equal to what you take to be the objective probability of the event. For example, you should expect events with a very high probability to occur and those with a very low probability not to occur. This paper examines the grounds of such principles. It is argued that any attempt to justify a principle of probability coordination encounters the same difficulties as attempts to justify induction. As a result, no justification can be found.

This paper justifies the inference of probabilities from symmetries. I supply some examples of important and correct inferences of this variety. Two explanations of such inferences -- an explanation based on the Principle of Indifference and a proposal due to Poincaré and Reichenbach -- are considered and rejected. I conclude with my own account, in which the inferences in question are shown to be warranted a posteriori, provided that they are based on symmetries in the mechanisms of chance setups.

It seems very important to us whether or not a generalization offers counter-factual support—but why? Surely what happens in other possible worlds can neither help nor hurt us? This paper explores the question whether counter-factual support does, nevertheless, have some practical value. (The question of theoretical value will be addressed but then put aside.) The following thesis is proposed: the counterfactual-supporting generalizations are those for which there exists a compact and under normal circumstances knowable basis determining the fine-grained pattern of actual variation between the properties associated by the generalization (e.g., for the generalization Fs tend to be G, the exact circumstances under which any particular F is G); further, the better we understand the basis and the scope of the support offered, the greater our knowledge of fine-grained variation. We care about counterfactual support because we care about actual fine-grained variation

It is argued that the relation of instance confirmation has a role to play in scientific methodology that complements, rather than competing with, a modern account of inductive support such as Bayesian confirmation theory. When an instance confirms a hypothesis, it provides inductive support, but it also provides two things that other inductive supporters normally do not: first, a connection to “empirical data” that makes science epistemically special, and second, inductive support not only for the hypothesis as a whole, but for its parts. Further, when it is conceived in the right way, instance confirmation can duck the arguments most often thought to refute it. A causal account of instantiation, thus of instance confirmation, is offered that looks to deliver on all of the foregoing promises.

A recent paper by David Lewis, "Causation as Influence", proposes a new theory of causation. I argue against the theory, maintaining that (a) the relation asserted by a claim of the form "C was a cause of E" is distinct from the relation of causal influence, (b) the former relation depends very much, contra Lewis, on the individuation conditions for the event E, and (c) Lewis's account is unsatisfactory as an analysis of either kind of relation. The counterexamples presented here provide, I suggest, some insight into the reasons for the failure of counterfactual accounts of causal relations.