This article presents a distinct sense of ‘mechanism’, which I call the functional sense of mechanism. According to this sense, mechanisms serve functions, and this fact places substantive restrictions on the kinds of system activities ‘for which’ there can be a mechanism. On this view, there are no mechanisms for pathology; pathologies result from disrupting mechanisms for functions. Second, on this sense, natural selection is probably not a mechanism for evolution because it does not serve a function. After distinguishing this sense from similar explications of ‘mechanism’, I argue that it is ubiquitous in biology and has valuable epistemic benefits.

Really statistical (RS) explanation is a hitherto neglected form of noncausal scientific explanation. Explanations in population biology that appeal to drift are RS explanations. An RS explanation supplies a kind of understanding that a causal explanation of the same result cannot supply. Roughly speaking, an RS explanation shows the result to be mere statistical fallout.

A piece of folklore enjoys some currency among philosophical Bayesians, according to which Bayesian agents who, intuitively speaking, spread their credence over the entire space of available hypotheses are certain to converge to the truth. The goals of the current discussion are to show that that kernel of truth in this folklore is in some ways fairly small and to argue that Bayesian convergence-to-the-truth results are a liability for Bayesianism as an account of rationality since they render a certain sort of arrogance rationally mandatory.

This article describes one style of functional analysis commonly used in the neurosciences called “task-bound functional analysis.” The concept of function invoked by this style of analysis is distinctive in virtue of the dependence relations it bears to transient environmental properties. It is argued that task-bound functional analysis cannot explain the presence of structural properties in nervous systems. An alternative concept of neural function is introduced that draws on the theoretical neuroscience literature, and an argument is given to show that this alternative concept may help to overcome the explanatory limitations of task-bound functional analysis.

For many years—and with some energy since Laudan’s “Confutation of Convergent Realism” (1981)—the scientific realist has sought to accommodate examples of false-yet-successful theories in the history of science. One of the most prominent strategies is to identify ‘success fueling’ components of false theories that themselves are at least approximately true (judging by our current understanding). In this article I develop both sides of the debate, introducing new challenges from the history of science as well as suggesting adjustments to the divide et impera realist strategy. A new ‘recipe’ for the prospective identification of (at least some) working/idle posits is considered.

Statistical decision theory, whether based on Bayesian principles or other concepts such as minimax or admissibility, relies on minimizing expected loss or maximizing expected utility. Loss and utility functions are generally treated as unit-less numerical measures of value for consequences. Here, we address the issue of the units in which loss and utility are settled and the implications that those units have on the rankings of potential decisions. When multiple currencies are available for paying the loss, one must take explicit account of which currency is used as well as the exchange rates between the various available currencies.

It is widely accepted that the notion of an inertial frame is central to Newtonian mechanics and that the correct space-time structure underlying Newton’s methods in Principia is neo-Newtonian or Galilean space-time. I argue to the contrary that inertial frames are not needed in Newton’s theory of motion, and that the right space-time structure for Newton’s Principia requires the notion of parallelism of spatial directions at different times and nothing more.

Highly idealized models, such as the Hawk-Dove game, are pervasive in biological theorizing. We argue that the process and motivation that leads to the introduction of various idealizations into these models is not adequately captured by Michael Weisberg’s taxonomy of three kinds of idealization. Consequently, a fourth kind of idealization is required, which we call hypothetical pattern idealization. This kind of idealization is used to construct models that aim to be explanatory but do not aim to be explanations.

We reassess Woodward’s counterfactual account of explanation in relation to regularity explananda. Woodward presents an account of causal explanation. We argue, by using an explanation of Kleiber’s law to illustrate, that the account can also cover some noncausal explanations. This leads to a tension between the two key aspects of Woodward’s account: the counterfactual aspect and the causal aspect. We explore this tension and make a case for jettisoning the causal aspect as constitutive of explanatory power in connection with regularity explananda.

We argue that many recent philosophical discussions about the reference of everyday concepts of intentional states have implicitly been predicated on descriptive theories of reference. To rectify this, we attempt to demonstrate how a causal theory can be applied to intentional concepts. Specifically, we argue that some phenomena in early social development (e.g., mimicry, gaze following, and emotional contagion) can serve as reference fixers that enable children to track others’ intentional states and, thus, to refer to those states. This allows intentional concepts to be anchored to their referents, even if folk psychological descriptions turn out to be false.

In a series of recent papers, Simon Saunders, Fred Muller, and Michael Seevinck have collectively argued, against the folklore, that some nontrivial version of Leibniz’s principle of the identity of indiscernibles is upheld in quantum mechanics. They argue that all particles—fermions, paraparticles, anyons, even bosons—may be weakly discerned by some physical relation. Here I show that their arguments make illegitimate appeal to nonsymmetric, that is, permutation-noninvariant, quantities and that therefore their conclusions do not go through. However, I show that alternative, symmetric quantities may be found to do the required work. I conclude that the Saunders-Muller-Seevinck heterodoxy can be saved.

In this article, we provide a case study examining how integrative systems biologists build simulation models in the absence of a theoretical base. Lacking theoretical starting points, integrative systems biology researchers rely cognitively on the model-building process to disentangle and understand complex biochemical systems. They build simulations from the ground up in a nest-like fashion, by pulling together information and techniques from a variety of possible sources and experimenting with different structures in order to discover a stable, robust result. Finally, we analyze the alternative role and meaning theory has in systems biology expressed as canonical template theories like Biochemical Systems Theory.

According to Woodward’s causal model of explanation, explanatory information is relevant for manipulation purposes and indicates by means of invariant causal relations how to change the value of certain target explanandum variables by intervening on others. Therefore, the depth of an explanation is evaluated through the size of the domain of invariance of the generalization involved. In this article, I argue that Woodward’s account of explanatory relevance is still unsatisfactory and claim that the depth of an explanation should be explicated in terms of the size of the domain of circumstances which it designates as leaving the explanandum unchanged.

Modularity is one of the most important concepts used to articulate a theory of cognitive architecture. Over the last 30 years, the debate in many areas of the cognitive sciences and in philosophy of psychology about what modules are, and to what extent our cognitive architecture is modular, has made little progress. After providing a diagnosis of this lack of progress, this article suggests a remedy. It argues that the theoretical framework of network science can be brought to bear on the traditional modularity debate, facilitating our progress in articulating a good theory of the human cognitive architecture.

Regularity is the thesis that all contingent propositions should be assigned probabilities strictly between zero and one. I will prove on cardinality grounds that if the domain is large enough, a regular probability assignment is impossible, even if we expand the range of values that probabilities can take, including, for instance, hyperreal values, and significantly weaken the axioms of probability.

Fitelson demonstrates that the validity of various central arguments within Bayesian confirmation theory depends on which confirmation measure is adopted. This article adds to the results set out by Fitelson, expanding on them in two principal respects. First, it considers more confirmation measures. Second, it shows that there are important arguments within Bayesian confirmation theory and that there is no confirmation measure that renders them all valid. Finally, the article reviews the ramifications that this “strengthened problem of measure sensitivity” has for Bayesian confirmation theory and discusses whether it points at pluralism about notions of confirmation.

How does Newton approach the challenge of mechanizing gravity and, more broadly, natural philosophy? By adopting the simple machine tradition’s mathematical approach to a system’s covarying parameters of change, he retains natural philosophy’s traditional goal while specifying it in a novel way as the search for impressed forces. He accordingly understands the physical world as a divinely created machine possessing intrinsically mathematical features and mathematical methods as capable of identifying its real features. The gravitational force’s physical cause remains an outstanding problem, however, as evidenced by Newton’s onetime reference to active principles as the “genuine principles of the mechanical philosophy.”

Evolution is often characterized as a tinkerer creating efficient but messy solutions. We analyze the nature of the problems that arise when trying to explain and understand cognitive phenomena created by this haphazard design process. We present a theory of explanation and understanding and apply it to a case problem—solutions generated by genetic algorithms. By analyzing the nature of solutions that genetic algorithms present to computational problems, we show, first, that evolutionary designs are often hard to understand because they exhibit nonmodular functionality and, second, that breaches of modularity wreak havoc on our strategies of causal and constitutive explanation.

The article revisits the large n (sample size) problem as it relates to the Jeffreys-Lindley paradox to compare the frequentist, Bayesian, and likelihoodist approaches to inference and evidence. It is argued that what is fallacious is to interpret a rejection of as providing the same evidence for a particular alternative , irrespective of n; this is an example of the fallacy of rejection. Moreover, the Bayesian and likelihoodist approaches are shown to be susceptible to the fallacy of acceptance. The key difference is that in frequentist testing the severity evaluation circumvents both fallacies but no such principled remedy exists for the other approaches.