Michael Strevens

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

The two major modern accounts of explanation are the causal and unification accounts. My aim in this paper is to provide a kind of unification of the causal and the unification accounts, by using the central technical apparatus of the unification account to solve a central problem faced by the causal account, namely, the problem of determining which parts of a causal network are explanatorily relevant to the occurrence of an explanandum. The end product of my investigation is a causal account of explanation that has many of the advantages of the unification account.

Sciences of complex systems thrive on compositional theories – toolkits that allow the construction of models of a wide range of systems, each consisting of various parts put together in different ways. To be tractable, a compositional theory must make shrewd choices about the parts and properties that constitute its basic ontology. One such choice is to decompose a system into spatiotemporally discrete parts. Compositional theories in the high-level sciences follow this rule of thumb to a certain extent, but they also make essential use of what I call distributed ontologies: divisions of the system into entities or states of affairs each depending on sets of fundamental-level facts that to a large extent overlap. The point is developed using the example of statistical theories in which the probabilities are ontologically distributed.

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.

Why are causal generalizations in the higher-level sciences “inexact”? That is, why do they have apparent exceptions? This paper offers one explanation: many causal generalizations cite as their antecedent—the \(F\) in \(Fs\,\, {\textit{are}}\,\, G\) —a property that is not causally relevant to the consequent, but which is rather “entangled” with a causally relevant property. Entanglement is a relation that may exist for many reasons, and that allows of exceptions. Causal generalizations that specify entangled but causally irrelevant antecedents therefore tolerate exceptions

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.

David Lewis, Michael Thau, and Ned Hall have recently argued that the Principal Principle—an inferential rule underlying much of our reasoning about probability—is inadequate in certain respects, and that something called the ‘New Principle’ ought to take its place. This paper argues that the Principle Principal need not be discarded. On the contrary, Lewis et al. can get everything they need—including the New Principle—from the intuitions and inferential habits that inspire the Principal Principle itself, while avoiding the problems that originally caused them to abandon that principle.

Science as we know it is “dappled”. Its picture of the world is a mosaic in which different aspects of the world, different systems, are represented by narrow-scope theories or models that are largely disconnected from one another. The best explanation for this disunity in our representation of the world, Nancy Cartwright has proposed, is a disunity in the world itself: rather than being governed by a small set of strict fundamental laws, events unfold according to a patchwork of principles covering different kinds of systems or segments of reality, each with something less than full omnipotence and with the possibility of anomic indeterminism at the boundaries. This paper attempts to undercut Cartwright’s argument for a dappled world by showing that the motley nature of science, both now and even at the completion of empirical inquiry, can equally well be explained by proponents of the “fundamentalist” view that the universe’s initial conditions and fundamental physical laws determine everything that ever happens.

This paper offers a metaphysics of physical probability in (or if you prefer, truth conditions for probabilistic claims about) deterministic systems based on an approach to the explanation of probabilistic patterns in deterministic systems called the method of arbitrary functions. Much of the appeal of the method is its promise to provide an account of physical probability on which probability assignments have the ability to support counterfactuals about frequencies. It is argued that the eponymous arbitrary functions are of little philosophical use, but that they can be substituted for facts about frequencies without losing the ability to provide counterfactual support. The result is an account of probability in deterministic systems that has a “propensity-like” look and feel, yet which requires no supplement to the standard modern empiricist tool kit of particular matters of fact and principles of physical dynamics.

Scientific understanding, this paper argues, can be analyzed entirely in terms of a mental act of “grasping” and a notion of explanation. To understand why a phenomenon occurs is to grasp a correct explanation of the phenomenon. To understand a scientific theory is to be able to construct, or at least to grasp, a range of potential explanations in which that theory accounts for other phenomena. There is no route to scientific understanding, then, that does not go by way of scientific explanation.

Science is epistemically special, or so I will assume: it is better able to produce knowledge about the workings of the world than other knowledge-directed pursuits. Further, its superior epistemic powers are due to its being in some sense especially empirical: in particular, science puts great weight on a form of inductive reasoning that I call empirical con rmation. My aim in this paper is to investigate the nature of science’s “empiricism”, and to provide a preliminary explanation of the connection between empirical confirmation and epistemic efficacy. I will try to convince you that the place to find an account of empirical confirmation is the dusty, long-neglected instantialist account of scientific inference offered by mid-century logical empiricists. Some revision of instantialism will be required. As for what is advantageous in empirical confirmation, I propose that it is an unusual degree of independence from background belief.

Philip Kitcher has advocated and advanced an influential antireductionist picture of science on which the higher-level sciences pursue their aims largely independently of the lower-level sciences -- a view of the sciences as autonomous. Explanatory autonomy as Kitcher understands it is incompatible with explanatory reductionism, the view that a high-level explanation is inevitably improved by providing a lower-level explanation of its parts. This paper explores an alternative conception of autonomy based on another major theme of Kitcher's philosophy of science: the importance of the division of cognitive labor. That the sciences are in practice autonomous is compatible with reductionism, I argue, if we understand the high-level sciences' systematic explanatory disregard of lower-level details of implementation as practically, rather than intellectually, motivated.

Recent work on children’s inferences concerning biological and chemical categories has suggested that children (and perhaps adults) are essentialists— a view known as psychological essentialism. I distinguish three varieties of psychological essentialism and investigate the ways in which essentialism explains the inferences for which it is supposed to account. Essentialism succeeds in explaining the inferences, I argue, because it attributes to the child belief in causal laws connecting category membership and the possession of certain characteristic appearances and behavior. This suggests that the data will be equally well explained by a non-essentialist hypothesis that attributes belief in the appropriate causal laws to the child, but makes no claim as to whether or not the child represents essences. I provide several reasons to think that this non-essentialist hypothesis is in fact superior to any version of the essentialist hypothesis

A physical system has a chaotic dynamics, according to the dictionary, if its behavior depends sensitively on its initial conditions, that is, if systems of the same type starting out with very similar sets of initial conditions can end up in states that are, in some relevant sense, very different. But when science calls a system chaotic, it normally implies two additional claims: that the dynamics of the system is relatively simple, in the sense that it can be expressed in the form of a mathematical expression having relatively few variables, and that the the geometry of the system’s possible trajectories has a certain aspect, often characterized by a strange attractor.

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.

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.

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.

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

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.

A theme of much work taking an ““economic approach”” to the study of science is the interaction between the norms of individual scientists and those of society at large. Though drawing from the same suite of formal methods, proponents of the economic approach offer what are in substantive terms profoundly different explanations of various aspects of the structure of science. The differences are illustrated by comparing Strevens's explanation of the scientific reward system (the ““priority rule””) with Max Albert's explanation of the prevalence of ““high methodological standards”” in science. Some objections to the economic approach as a whole are then briefly considered

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

Why do we represent the world around us using causal generalizations, rather than, say, purely statistical generalizations? Do causal representations contain useful additional information, or are they merely more efficient for inferential purposes? This paper considers the second kind of answer: it investigates some ways in which causal cognition might aid us not because of its expressive power, but because of its organizational power. Three styles of explanation are considered. The first, building on the work of Reichenbach in "The Direction of Time", points to causal representation as especially efficient for predictive purposes in a world containing certain pervasive patterns of conditional independence. The second, inspired by work of Woodward and others, finds causal representation to be an excellent vehicle for representing all-important relations of manipulability. The third, based in part on my own work, locates the importance of causal cognition in the special role it reserves for information about underlying mechanisms. All three varieties of explanation show promise, but particular emphasis is placed on the third.