The discovery of the Higgs particle required a signal of 5σ significance. The rigid application of that condition is a convention that disregards more specific aspects of the given experiment. In particular, it does not account for the characteristics of the look elsewhere effect in the individual experimental context. The paper relates this aspect of data analysis to the question as to what extent theoretical reasoning should be admitted to play a role in the assessment of the significance of empirical data.

The existence of “undermining futures” appears to show that a contradiction can be deduced from the conjunction of Humean supervenience (HS) about chance and the Principal Principle. A number of strategies for rescuing HS from this problem have been proposed recently. In this paper, a novel way of defending HS from the threat is presented, and it is argued that this defense has advantages not shared by others. In particular, it requires no revisionism about chance, and it is equally available to defenders of HS who hold HS to be necessary and those who hold it to be contingent.

I contrast our own evidence for the hypothesis of organic fossil origins with that available in previous centuries, suggesting that the most powerful contemporary evidence consists in a form of projective support whose distinctive features are not well captured by familiar hypothetico-deductive, abductive, or even more recent and more technically sophisticated (e.g., Bayesian) accounts of scientific confirmation. I suggest that such accounts either misrepresent or ignore something important about the heterogeneous ways in which scientific hypotheses can be supported by evidence, and I go on to suggest that the search for any single such account may be misguided in any case.

Myrvold (2003) has proposed an attractive Bayesian account of why theories that unify phenomena tend to derive greater epistemic support from those phenomena than do theories that fail to unify them. It is argued, however, that “unification” in Myrvold's sense is both too easy and too difficult for theories to achieve. Myrvold's account fails to capture what it is that makes unification sometimes count in a theory's favor.

There is sufficient evidence that intellectual property rights are corrupting medical research. One could respond to this from a moral or from an epistemic point of view. I take the latter route. Often in the sciences factual discoveries lead to new methodological norms. Medical research is an example. Surprisingly, the methodological change required will involve political change. Instead of new regulations aimed at controlling the problem, the outright socialization of research seems called for, for the sake of better science. I appeal to an analogy between socialized medicine and socialized research.

Many policy decisions take input from collections of scientific models. Such decisions face significant and often poorly understood uncertainty. We rework the so-called confidence approach to tackle decision-making under severe uncertainty with multiple models, and we illustrate the approach with a case study: insurance pricing using hurricane models. The confidence approach has important consequences for this case and offers a powerful framework for a wide class of problems. We end by discussing different ways in which model ensembles can feed information into the approach, appropriate to different collections of models.

My concern is with the possible implications of research in developmental psychology for understanding the workings of modern science. I agree both with Gopnik's general naturalistic orientation and with her more specific claims about scientists as cognitive agents. Neither the formal structure of propositions nor the social structure of scientific communities provides sufficient resources for the understanding we seek. So I agree that the empirical study of human cognition is not only relevant, but necessary, for understanding how science works (Giere 1988, 1989).

When classical mechanics is seen as the short-wavelength limit of quantum mechanics (i.e., as the limit of geometrical optics), it becomes clear just how serious and all-pervasive the measurement problem is. This formulation also leads us into the Bohm theory. But this theory has drawbacks: its nonuniqueness, in particular, and its nonlocality. I argue that these both reflect an underlying problem concerning information, which is actually a deeper version of the measurement problem itself.

The conceptual excitement of science often seems geared only to work in contemporary physics. Thus, philosophers regularly discuss current cosmology, relativity, or the foundations of microphysics. In these areas one's philosophy is stretched and strained far beyond what our ancestors might have anticipated. Historians of science have also focused attention on past events by remarking their analogies and similarities with perplexities in physics today.

But there are statements, hypotheses and theories of the past which are rewarding in themselves, without having to be referred to the agonies which now confound quantum theory and cosmology. Specifically, the First Law of Motion—the “Law of Inertia”—this has everything a logician of science could look for. Understanding the complexities and perplexities of this fundamental mechanical statement is in itself to gain insight into what theoretical physics in general really is. With this in view a study of the law is undertaken.

The nature of persistence of physical objects over time has been intensely debated in contemporary metaphysics. The two opposite views are widely known as “endurantism” (or “three-dimensionalism”) and “perdurantism” (“four-dimensionalism”). According to the former, objects are extended in three spatial dimensions and persist through time by being wholly present at any moment at which they exist. On the rival account, objects are extended both in space and time and persist by having “temporal parts,” no part being present at more than one time. Relativistic considerations seem highly relevant to this debate. But they have played little role in it so far. This paper seeks to remedy that situation. I argue that considerations based on the special theory of relativity and the notion of coexistence favor perdurantism over endurantism.

The statements traditionally labelled “necessary,” among them the valid theorems of mathematics and logic, are identified as “those whose truth is independent of experience.” The “truth” of a necessary statement has to be independent of the truth or falsity of experiential statements; a necessary statement can be neither confirmed nor refuted by empirical tests.

The admission of genuinely necessary statements presents the empiricist with a troublesome problem. For an empiricist may be defined, in terms of the current idiom, as one who adheres to some version, however “weak,” of a principle of verifiability. One, that is, who claims that no statement can have cognitive meaning unless its truth depends, however indirectly, upon the truth of experiential statements; unless it can be provisionally confirrned or refuted by empirical tests. Allowing that some necessary statements have cognitive meaning, then, would be to provide a prima facie case against the validity of the principle of verifiability.

This paper attempts to outline some of the important concepts and ideas used in system analysis which is taken to be a general mode of analysis used in all sciences. Systems are seen from three perspectives: (1) that involving the relationship between system and environment, (2) that involving interaction between several systems, and (3) that involving one type of system composed of other types of systems. The writers also discuss the concepts “structure” and “equilibrium” as they apply to system analysis, the point being made that the use of these concepts in the social sciences has often been vague or even incorrect.

Sober (1984) has considered the problem of determining the evidential support, in terms of likelihood, for a hypothesis that is incomplete in the sense of not providing a unique probability function over the event space in its domain. Causal hypotheses are typically like this because they do not specify the probability of their initial conditions. Sober's (1984) solution to this problem does not work, as will be shown by examining his own biological examples of common cause explanation. The proposed solution will lead to the conclusion, contra Sober, that common cause hypotheses explain statistical correlations and not matchings between event tokens.

Part I is concerned with the tenet of modern Emperical Realism that while the theoretical concepts employed in science obtain their meanings entirely from the connections their usage establishes with the data language, the referents of such terms may be “unobservables,” that is, entities which cannot be discussed within the data language alone. Such a view avoids both the restrictive excesses of logical positivism and the epistemic laxity of transcendentalism; however, it also necessitates a break with classical semantics, for it follows from the empirical realistic position that a theoretical term must in principle be capable of simultaneously designating not just one entity, but indefinitely many.

Drawing upon the Carnapian explication of “analytic truth,” Part II examines a possible axiomatic basis for the empiricist theory of scientific meaningfulness to demonstrate that even if theoretical terms are able to designate entities inaccessible to the observation language, as held by Empirical Realism, so long as the meanings of theoretical terms derive from their connections with the observation language, the meaning content of a theory is exhausted by its observational consequences.

Optogenetic techniques are described as “revolutionary” for the unprecedented causal control they allow neuroscientists to exert over neural activity in awake-behaving animals. In this article, I demonstrate by means of a case study that optogenetic techniques will only illuminate causal links between the brain and behavior to the extent that their error characteristics are known and, further, that determining these error characteristics requires (1) comparison of optogenetic techniques with techniques having well-known error characteristics (methodological pluralism) and (2) consideration of the broader neural and behavioral context in which the targets of optogenetic interventions are situated (perspectival pluralism).

Recent work combining cognitive neuroscience with computational modeling suggests that distributed patterns of neural firing may represent probability distributions. This article asks, what makes it the case that distributed patterns of firing, as well as carrying information about (correlating with) probability distributions over worldly parameters, represent such distributions? In examples of probabilistic population coding, it is the way information is used in downstream processing so as to lead to successful behavior. In these cases content depends on factors beyond bare information, contra Brian Skyrms’s view that representational content can be fully characterized in information-theoretic terms.

This essay uses Györgi Buzsáki's use of EEG data to draw conclusions about brain function as an example to show that investigators sometimes draw conclusions from noisy data by analyzing the noise rather than by extracting a signal from it. The example makes vivid some important differences between McAllister's, Woodward's, and my ideas about how data are interpreted.

I present three reasons why philosophers of science should be more concerned about violations of causal faithfulness (CF). In complex evolved systems, mechanisms for maintaining equilibrium states are highly likely to violate CF. Even when such systems do not precisely violate CF, they may nevertheless generate precisely the same problems for inferring causal structure from probabilistic relationships in data as do genuine CF violations. Thus, potential CF violations are particularly germane to experimental science when we rely on probabilistic information to uncover causal structures since we cannot then use those structures to predict the right experiments to ‘catch out’ hidden causal relationships.

The article discusses a well-known type of counterexample to Causal Decision Theory (CDT), in which CDT recommends an option that probably causes the best outcome while itself being evidence that it causes the worst. Intuition disagrees. Many philosophers accept that this justifies either modifying CDT or dropping it altogether. I argue to the contrary that (a) if intuition is right about this case, then transitivity of preference must be violated in another, but (b) this violation is untenable. I conclude that CDT stands.

Much of the recent discussion of problematic aspects of quantum-mechanical measurement centers around that feature of quantum theory which is called “the projection postulate.” This is roughly the claim that a change of a certain sort occurs in the state of a physical system when a measurement is made on the system. In this paper an argument for the projection postulate due to von Neumann is considered. Attention is focused on trying to provide an understanding of the notion of “the state of a physical system” which is compatible with the argument von Neumann offers. An attempt is made to formulate the argument in terms of an objectivistic interpretation of probability concepts. It is seen that such an interpretation does not provide a suitable way of understanding the argument. An attempt is made to illustrate the source of this failure in terms of a non-quantum-mechanical example.