A proof is given that there does not exist an event, that is not already in the past for some possible distant observer at the (our) moment that the latter is “now” for us. Such event is as “legally” past for that distant observer as is the moment five minutes ago on the sun for us (irrespective of the circumstance that the light of the sun cannot reach us in a period of five minutes). Only an extreme positivism: “that which cannot yet be observed does not yet exist”, can possibly withstand the conclusion concerned. Therefore, there is determinism, also in micro-physics.

Signaling games provide basic insights into some fundamental questions concerning the explanation of meaning. They can be analyzed in terms of rational choice theory and in terms of evolutionary game theory. It is argued that an evolutionary approach provides better explanations for the emergence of simple communication systems. To substantiate these arguments, I will look at models similar to those of Skyrms (2000) and Komarova and Niyogi (2004) and study their dynamical properties. My results will lend partial support to the thesis that evolution leads to communication. In general, states of partial communication may evolve with positive probability under standard evolutionary dynamics. However, unlike states of perfect communication, they are unstable relative to neutral drift.

A number of recent biologists have used multilevel selection theory to help explain the major transitions in evolution. I argue that in doing so, they have shifted from a ‘synchronic’ to a ‘diachronic’ formulation of the levels of selection question. The implications of this shift in perspective are explored, in relation to an ambiguity in the meaning of multilevel selection. Though the ambiguity is well-known, it has never before been discussed in the context of the major transitions.

Network neuroscience provides a systems approach to the study of the brain and enables the examination of interactions measured at different temporal and spatial scales. We review current methods to quantify the structure of brain networks and compare that structure across different clinical cohorts, cognitive states, and subjects. We further introduce the emerging mathematical concept of multilayer networks and describe the advantages of this approach to model changing brain dynamics over time. We conclude by offering several concrete examples of how multilayer network approaches to neuroimaging data provide novel insights into brain structure and evolving function.

Polarization is a topic of intense interest among social scientists, but there is significant disagreement regarding the character of the phenomenon and little understanding of underlying mechanics. A first problem, we argue, is that polarization appears in the literature as not one concept but many. In the first part of the article, we distinguish nine phenomena that may be considered polarization, with suggestions of appropriate measures for each. In the second part of the article, we apply this analysis to evaluate the types of polarization generated by the three major families of computational models proposing specific mechanisms of opinion polarization.

Akaike's framework for thinking about model selection in terms of the goal of predictive accuracy and his criterion for model selection have important philosophical implications. Scientists often test models whose truth values they already know, and they often decline to reject models that they know full well are false. Instrumentalism helps explain this pervasive feature of scientific practice, and Akaike's framework helps provide instrumentalism with the epistemology it needs. Akaike's criterion for model selection also throws light on the role of parsimony considerations in hypothesis evaluation. I explain the basic ideas behind Akaike's framework and criterion; several biological examples, including the use of maximum likelihood methods in phylogenetic inference, are considered.

The article is a reappraisal of the requirement of maximal specificity (RMS) proposed by the author as a means of avoiding “ambiguity” in probabilistic explanation. The author argues that RMS is not, as he had held in one earlier publication, a rough substitute for the requirement of total evidence, but is independent of it and has quite a different rationale. A group of recent objections to RMS is answered by stressing that the statistical generalizations invoked in probabilistic explanations must be lawlike, and by arguing that predicates fit for occurrence in lawlike statistical probability statements must meet two conditions, at least one of which is violated in each of the counterexamples adduced in the objections. These considerations suggest the conception that probabilistic-statistical laws concern the long-run frequency of some characteristic within a reference class as characterized by some particular “description” or predicate expression, and that replacement of such a description by a coextensive one may turn a statement that is lawlike into another that is not. Finally, to repair a defect noted by Grandy, the author's earlier formulation of RMS is replaced by a modified version.

Scientists depend on complex computational systems that are often ineliminably opaque, to the detriment of our ability to give scientific explanations and detect artifacts. Some philosophers have suggested treating opaque systems instrumentally, but computer scientists developing strategies for increasing transparency are correct in finding this unsatisfying. Instead, I propose an analysis of transparency as having three forms: transparency of the algorithm, the realization of the algorithm in code, and the way that code is run on particular hardware and data. This targets the transparency most useful for a task, avoiding instrumentalism by providing partial transparency when full transparency is impossible.

Recent philosophical attention to climate models has highlighted their weaknesses and uncertainties. Here I address the ways that models gain support through observational data. I review examples of model fit, variety of evidence, and independent support for aspects of the models, contrasting my analysis with that of other philosophers. I also investigate model robustness, which often emerges when comparing climate models simulating the same time period or set of conditions. Starting from Michael Weisberg's analysis of robustness, I conclude that his approach involves a version of reasoning from variety of evidence, enabling this robustness to be a confirmatory virtue

I argue that, contrary to some critics, the notion of conscious experience is a good candidate for denoting a distinct and scientifically interesting phenomenon in the brain. I base this claim mainly on an analysis of neuropsychological data concerning deficits resulting from various types of brain damage as well as some additional supporting empirical evidence. These data strongly point to the hypothesis that conscious experience expresses information that is available for global, integrated, and flexible behavior.

Although prediction has been largely absent from discussions of explanation for the past 40 years, theories of explanation can gain much from a reintroduction. I review the history that divorced prediction from explanation, examine the proliferation of models of explanation that followed, and argue that accounts of explanation have been impoverished by the neglect of prediction. Instead of a revival of the symmetry thesis, I suggest that explanation should be understood as a cognitive tool that assists us in generating new predictions. This view of explanation and prediction clarifies what makes an explanation scientific and why inference to the best explanation makes sense in science.

The literature on causal discovery has focused on interventions that involve randomly assigning values to a single variable. But such a randomized intervention is not the only possibility, nor is it always optimal. In some cases it is impossible or it would be unethical to perform such an intervention. We provide an account of ‘hard’ and ‘soft’ interventions and discuss what they can contribute to causal discovery. We also describe how the choice of the optimal intervention(s) depends heavily on the particular experimental setup and the assumptions that can be made.

Among recent developments in the anthropological sciences, hardly any have found so much attention and led to so much controversy as have the views advanced by the late Benjamin Whorf.

The hypothesis offered by Whorf is,

“that the commonly held belief that the cognitive processes of all human beings possess a common logical structure which operates prior to and independently of communication through language, is erroneous. It is Whorf's view that the linguistic patterns themselves determine what the individual perceives in this world and how he thinks about it. Since these patterns vary widely, the modes of thinking and perceiving in groups utilizing different linguistic systems will result in basically different world views” (Fearing, 1954).

“We are thus introduced to a new principle of relativity which holds that all observers are not led by the same physical evidence to the same picture of the universe, unless their linguistic backgrounds are similar. … We cut up and organize the spread and flow of events as we do largely because, through our mother tongue, we are parties to an agreement to do so, not because nature itself is segmented in exactly that way for all to see” (Whorf, 1952, p. 21)

Species pluralism gives us reason to doubt the existence of the species category. The problem is not that species concepts are chosen according to our interests or that pluralism and the desire for hierarchical classifications are incompatible. The problem is that the various taxa we call ‘species’ lack a common unifying feature.

In a recent essay (1) Professor Taylor criticizes the criteria used by Rosenblueth, Wiener and Bigelow in 1943 (2) to distinguish purposeful from non-purposeful behavior. He also criticizes our definition of behavior, our concept of the vague as opposed to the general, our use of the word correlation, and our statement that a system may reach a final condition. Indeed, there seems to be little, if anything, in our paper to which he does not emphatically object.

He maintains that the notions of purpose and teleology are not only useless for the understanding of mechanical behavior, but wholly incongruous when applied to this behavior. He further affirms that our use of the term purpose “bears no similarity whatever to the meaning which is ordinarily attached to it.” He does not state, however, his own notions of purpose and teleology; and the meaning which he considers to be ordinarily attached to the term purpose. This omission weakens his criticism considerably.

Among the various meanings in which the word ‘probability’ is used in everyday language, in the discussion of scientists, and in the theories of probability, there are especially two which must be clearly distinguished. We shall use for them the terms ‘probability1’ and ‘probability2'. Probability1 is a logical concept, a certain logical relation between two sentences (or, alternatively, between two propositions); it is the same as the concept of degree of confirmation. I shall write briefly “c” for “degree of confirmation,” and “c(h, e)” for “the degree of confirmation of the hypothesis h on the evidence e“; the evidence is usually a report on the results of our observations. On the other hand, probability2 is an empirical concept; it is the relative frequency in the long run of one property with respect to another. The controversy between the so-called logical conception of probability, as represented e.g. by Keynes, and Jeffreys, and others, and the frequency conception, maintained e.g. by v. Mises and Reichenbach, seems to me futile. These two theories deal with two different probability concepts which are both of great importance for science. Therefore, the theories are not incompatible, but rather supplement each other.

One of the most interesting programs in the foundations of quantum mechanics is the realist quantum logic approach associated with Putnam, Bub, Demopoulos and Friedman (and which is the focus of my own research.) I believe that realist quantum logic is our best hope for making sense of quantum mechanics, but I have come to suspect that the usual version may not be the correct one. In this paper, I would like to say why and to propose an alternative.

Beatty, Brandon, and Sober agree that biological generalizations, when contingent, do not qualify as laws. Their conclusion follows from a normative definition of law inherited from the Logical Empiricists. I suggest two additional approaches: paradigmatic and pragmatic. Only the pragmatic represents varying kinds and degrees of contingency and exposes the multiple relationships found among scientific generalizations. It emphasizes the function of laws in grounding expectation and promotes the evaluation of generalizations along continua of ontological and representational parameters. Stability of conditions and strength of determination in nature govern projectibility. Accuracy, ontological level, simplicity, and manageability provide additional measures of usefulness.

This paper questions the prevailing historical understanding that scientific racism “retreated” in the 1950s when anthropology adopted the concepts and methods of population genetics and race was recognized to be a social construct and replaced by the concept of population. More accurately, a “populational” concept of race was substituted for a “typological one”—this is demonstrated by looking at the work of Theodosius Dobzhansky circa 1950. The potential for contemporary research in human population genetics to contribute to racism needs to be considered with respect to the ability of the typological-population distinction to arbitrate boundaries between racist society and nonracist, even anti-racist, science. I point out some ethical limits of “population thinking” in doing so.

Scientific articles exemplify standard functional units constraining argumentative structures. Severe space limitations demand every paragraph and illustration contribute to establishing the paper's claims. Philosophical testing and confirmation models should take into account each paragraph, table, and illustration. Hypothetico-Deductive, Bayesian Inductive, and Inference-to-the-Best-Explanation models do not, garbling the logic of papers. Micro-analysis of the fundamental paper in plate tectonics reveals an argumentative structure commonplace in science but ignored by standard philosophical accounts that cannot be dismissed as mere rhetorical embellishment. Papers with illustrations often display a second argumentative structure differing from the text's. Constraints on adequate testing and confirmation analyses are adduced.