Philosophical discussions of information and specificity in biology are now commonplace, but no consensus exists about whether the privileging of genetic causation in investigation and explanation is justified. However, little effort has been expended on understanding practices when scientists experimentally measure information or causal specificity. An example of this type of practice—measuring positional information in gene expression during pattern formation in embryogenesis—shows that biologists are unconcerned with comparing the amount of information in genes with that of other factors. Instead, they focus on whether the measured causal specificity explains the phenomenon under scrutiny.

When evaluating the success of causal theories of explanation the focus has typically been on the legitimacy of causal relations and on putative examples of explanations that we cannot capture in causal terms. Here I motivate the existence of a third kind of problem: the difficulty of accounting for explanatory disputes. Moreover, I argue that this problem remains even if the first two are settled and that it threatens to undercut one of the central motivations for causal accounts of explanation, namely, the causal account of the directionality of scientific explanation.

Economists often consult multiple models in order to combat model uncertainty in the face of misspecification. By examining modeling practices at the Bank of England, this paper identifies an important, but underappreciated modeling procedure. Sometimes an idealized model is manipulated to reproduce the results from another distinct auxiliary model, ones which it could not produce on its own. However, this procedure does not involve making the original model “more realistic,” insofar as this means adding in additional causal factors. This suggests that there are ways to make models more representationally adequate that do not involve de-idealization in the straightforward sense.

Among advocates and critics of the “extended evolutionary synthesis” (EES), “reciprocal causation” refers to the view that adaptive evolution is a bidirectional phenomenon, whereby organisms and environments impinge on each other through processes of niche construction and natural selection. I argue that reciprocal causation is incompatible with the view that natural selection is a metaphysically emergent causal process. The emergent character of selection places reciprocal causation on the horns of dilemma, and neither horn can rescue it. I conclude that proponents of the EES must abandon the claim that the process of natural selection features in cycles of reciprocal causation.

When solving a complex problem in a group, should group members always choose the best available solution that they are aware of? In this paper, I build simulation models to show that, perhaps surprisingly, a group of agents who individually randomly follow a better available solution than their own can end up outperforming a group of agents who individually always follow the best available solution. This result has implications for the feminist philosophy of science and social epistemology.

This article explores the use of model organisms in studying the cognitive phenomenon of decision-making. Drawing on the framework of biological control to develop a skeletal conception of decision-making, we show that two core features of decision-making mechanisms can be identified by studying model organisms, such as E. coli, jellyfish, C. elegans, lamprey, and so on. First, decision mechanisms are distributed and heterarchically structured. Second, they depend heavily on chemical information processing, such as that involving neuromodulators. We end by discussing the implications for studying distinctively human decision-making.

Confirmation is a graded notion: evidence can confirm a hypothesis to a greater or lesser degree. There has been debate about how to measure degree of confirmation. Starting from the observation that we would like evidence to be a discriminating indicator of truth, we conduct computer simulations to determine how well the various known measures of confirmation predict the extent to which a given piece of evidence fulfills that role, given a hypothesis of interest. The outcomes show that some measures are markedly better indicators of truth than others.

Modern physics, which had its beginnings in the inclined-plane experiments of Galileo, deals with the measurable aspects of the world about us. The laws and definitions of classical physics are, at least superficially, differential equations in which each variable represents the result of a particular kind of measurement. These variables are usually called physical quantites. Starting from a few general laws and definitions we can derive formally further relations between the physical quantities and their rates of change in space and time. If the original definitions are self-consistent and the original laws accurate, the relations we derive from them should enable us to predict what the numerical result of a certain measurement will be whenever certain other measurements have yielded specified numerical results.

The problem of theory choice and model selection is hard but still important when useful truths are underdetermined, perhaps not by all kinds of data but by the kinds of data we can have access to ethically or practicably—even if we have an infinity of such data. This article addresses a crucial instance of that problem: the problem of inferring causal structures from nonexperimental, nontemporal data without assuming the so-called causal Faithfulness condition or the like. A new account of epistemic evaluation is developed to solve that problem and justify a standard practice of causal inference in data science.

Morphology is the science of form. The word is used in various scientific disciplines, e.g. in geography, linguistics, etc., but it is most frequently employed to denote that part of Biology which deals with the forms of organisms. The mere description of spatial relations, however, scarcely constitutes a science. It may be hard to say by what property science is distinguished from other human endeavors, but that factual description does not suffice is generally admitted. To establish relations between facts may be taken as a preliminary answer. It would still remain to define these relations. The answer to that question, however, depends largely on the particular brand of philosophy to which an author happens to subscribe. Most answers have been formulated by focusing attention largely on physics and related sciences.

The unanimity theory is an account of property-level causation requiring that causes raise the probability of their effects in specified test situations. Richard Otte (1981) and others have presented counterexamples in which one property is probabilistically sufficient for at least one other property. Given the continuing discussion (e.g., Cartwright 1989; Cartwright and Dupré 1988; Eells 1988a,b), many apparently think that these problems are minor. By considering the impact of Otte's cases on recent versions of the theory, by raising several new examples, and by criticizing natural replies, I argue that the problems for the unanimity theory are severe.

A common way of testing the inner sense theory of introspection exploits the possibility of damage to inner sense. Such damage is expected to lead to first-personal deficits/impairments of one kind or another. I raise various problems for this way of testing the theory. The main difficulty, I argue, stems from the existence of the method subserving confabulation.

The multiple-models approach, which has its origins in Levins’s work, is gaining broader acceptance among philosophers. Levins asserted that there is a trade-off between modeling desiderata, which justified the multiple-models approach through two separate justificatory paths. Some attention has been paid to the trade-off thesis, culminating in a paper by Matthewson and Weisberg. However, no attention has been paid to how the trade-off is supposed to justify the multiple-models approach. I argue that a trade-off between generality and precision cannot support one of Levins’s justificatory paths, and I consider what that might mean for the multiple-models approach.

Richard Jeffrey and Michael Goldstein have both introduced systematic approaches to the structure of opinion changes. For both approaches there are theorems which indicate great generality and width of scope. The main questions addressed here will be to what extent the basic forms of representation are intertranslatable, and how we can conceive of such programs in general.

Functional explanation in the social sciences is the focal point for conflict between individualistic and social modes of explanation. While the agent thought she was acting for reasons, the functional explanation seems to reveal the hidden strings of the puppet master. This essay argues that the conflict is merely apparent. The erotetic model of explanation is used to analyze the forms of intentional action and functional explanations. Two explanations conflict if either the presuppositions of their respective why-questions conflict or the typical answers identified by their relevance criteria conflict. While a functional explanation may have the same topic and foil as an intentional action explanation, both the why-questions and their typical answers are compatible.

Scientists are people. They are not “people, but…”. They have not escaped and cannot escape the “democentric” predicament. They are members of a society, no matter how hard they strain to be individualistic or exclusively clannish. If they succeed in being members of a small clan or a club they can be credited possibly with having more or less shrunk only the apparent size of their society, without escaping in any significant manner the social field implied in being “people”. If they attempt a greater shrinkage than that, they court complete frustration. To verify that, try, if you will, to imagine a thoroughly solitary scientist on a desert isle. Is he not alone and at last an illustration of the ungrammatical and even logically fictitious singular of the word “people”? No, he is definitely and hopelessly not alone, unless he first forgets even as habit everything he has learned from people. And, if he does that, he is no longer a scientist or even the rudiments of one. Indeed he must be reduced at least to the status of an animal, and an extremely unsocial one at that, because by the terms of our imaginary experiment, he must not join any other society, even if it were of but “animal-people”, e.g. a wolf-pack or an ant-hill culture, where he might learn something from others. Such strictness in argument is introduced only to drive home the fact that what constitutes “people” is derived from the dimmest animal past, where language is still unspoken and is constituted of simple actions taken together, literally concepts. The “con” in “concept” tends to be forgotten. We underscore it.

We wish here to consider the theory of a population or system made up of individuals whose number and size change with time. As usual, the description of these changes will be referred to as the kinetics, whereas the description of the special circumstances under which unchanging (equilibrium) conditions subsist will be called the statics of the population. A third category, the conditions for a steady state, i.e., when the variables inside the system do not change, but linked variables outside (or at the boundary) do, will be described by what we shall call the rheostatics of the system. All three, statics, rheostatics, and kinetics are subdivisions of the general dynamics of the system.

The problem of this paper is the discovery of criteria to identify conventions and the description of some of the more general properties of conventions with particular reference to scientific inquiry. Numerous specialized methodological investigations testify to the importance of conventions in inquiry, but none provide a general account of their operation. This paper aims to provide a necessary, if not sufficient, basis for such an account. The discussion falls into three parts: (I) common senses of the term ‘convention’, (II) a provisional formulation of conditions under which a certain type of behavior, which we shall call conventional, occurs, as well as a description of its characters, and (III) a brief consideration of one context of inquiry in which it appears.

Stanley Stevens draws a useful distinction among ordinal scales, interval scales, and ratio scales. Most recent discussions of confirmation measures have proceeded on the ordinal level of analysis. In this article, I give a more quantitative analysis. In particular, I show that the requirement that our desired confirmation measure be at least an interval measure naturally yields necessary conditions that jointly entail the log-likelihood measure. Thus, I conclude that the log-likelihood measure is the only good candidate interval (or ratio) measure.