Starting from the standard quantum formalism for a single spin\frac{1}{2}$\frac{1}{2}$ system (e.g., an electron), this essay develops a model rich enough not only to afford an explication of symmetry breaking but also to frame questions about how to circumscribe physical possibility on behalf of theories that countenance symmetry breaking.

The Higgs mechanism is an essential but elusive component of the Standard Model of particle physics. Without it Yang-Mills gauge theories would have been little more than a warm-up exercise in the attempt to quantize gravity rather than serving as the basis for the Standard Model. This article focuses on two problems related to the Higgs mechanism clearly posed in Earman's recent papers (Earman 2003, 2004a, 2004b): what is the gauge-invariant content of the Higgs mechanism, and what does it mean to break a local gauge symmetry?

Carnap's inductive logic (or confirmation) project is revisited from an “increase in firmness” (or probabilistic relevance) point of view. It is argued that Carnap's main desiderata can be satisfied in this setting, without the need for a theory of “logical probability.” The emphasis here will be on explaining how Carnap's epistemological desiderata for inductive logic will need to be modified in this new setting. The key move is to abandon Carnap's goal of bridging confirmation and credence, in favor of bridging confirmation and evidential support.

I conceive of inductive logic as a project of explication. The explicandum is one of the meanings of the word `probability' in ordinary language; I call it inductive probability and argue that it is logical, in a certain sense. The explicatum is a conditional probability function that is specified by stipulative definition. This conception of inductive logic is close to Carnap's, but common objections to Carnapian inductive logic (the probabilities don't exist, are arbitrary, etc.) do not apply to this conception.

I begin by distinguishing two notions of model, the notion of a truth-making structure and the notion of a mathematical model (in one specific sense). I then argue that although the models of the semantic view have often been taken to be both truth-making structures and mathematical models, this is in part due to a failure to distinguish between two ways of truth-making; in fact, the talk of truth-making is best excised from the view altogether. The result is a version of the semantic view which is better supported by the direct evidence offered for it, better equipped to achieve its avowed aims, and, I think, closer to the intentions of the original proponents of the view in many ways, despite some of their own declarations to the contrary.

What does it mean to embed the phenomena in an abstract structure? Or to represent them by doing so? The semantic view of theories runs into a severe problem if these notions are construed either naively, in a metaphysical way, or too closely on the pattern of the earlier syntactic view. Constructive empiricism and structural realism will then share those difficulties. The problem will be posed as in Reichenbach's The Theory of Relativity and A Priori Knowledge, and realist reactions will be examined, but they will be argued to dissolve upon scrutiny.

The central concern of this article is whether the semantic approach has the resources to appropriately capture the core tenets of structural realism. Chakravartty (2001) has argued that a realist notion of correspondence cannot be accommodated without introducing a linguistic component, which undermines the approach itself. We suggest that this worry can be addressed by an appropriate understanding of the role of language in this context. The real challenge, however, is how to incorporate the core notion of ‘explanatory approximate truth’ in such a way that the emphasis on structure is retained.

This paper is structured around the three elements of the title. Section 2 claims that (a) structures need objects and (b) scientific structuralism should focus on in re structures. Therefore, pure structuralism is undermined. Section 3 discusses whether the world has ‘excess structure’ over the structure of appearances. The main point is that the claim that only structure can be known is false. Finally, Section 4 argues directly against ontic structural realism that it lacks the resources to accommodate causation within its structuralist slogan.

This paper explores varieties of scientific structuralism. Central to our investigation is the notion of ‘shared structure’. We begin with a description of mathematical structuralism and use this to point out analogies and disanalogies with scientific structuralism. Our particular focus is the semantic structuralist's attempt to use the notion of shared structure to account for the theory-world connection, this use being crucially important to both the contemporary structural empiricist and realist. We show why minimal scientific structuralism is, at the very least, a powerful methodological standpoint. Our investigation also makes explicit what more must be added to this minimal structuralist position in order to address the theory-world connection, namely, an account of representation.

Should philosophers of science be paying attention to developments in “nanoscience”? Undoubtedly, it is too early to tell for sure. The goal of this paper is to take a preliminary look. In particular, I look at the use of computational models in the study of nano-sized solid-state materials. What I find is that there are features of these models that appear on their face to be at odds with some basic philosophical intuitions about the relationships between different theories and between theories and their models. My conclusion is that developments in nanoscience are not an unlikely place for novel insights in the philosophy of science to emerge.

Starting with the view that methodological constraints depend upon the nature of the system investigated, a tripartite division between theoretical, semitheoretical, and empirical discoveries is made. Many nanosystems can only be investigated semitheoretically or empirically, and this aspect leads to some nanophenomena being weakly emergent. Self-assembling systems are used as an example, their existence suggesting that the class of systems that is not Kim-reducible may be quite large.

This paper starts by looking at the coincidence of surprising behavior on the nanolevel in both matter and simulation. It uses this coincidence to argue that the simulation approach opens up a pragmatic mode of understanding oriented toward design rules and based on a new instrumental access to complex models. Calculations, and their variation by means of explorative numerical experimentation and visualization, can give a feeling for a model's behavior and the ability to control phenomena, even if the model itself remains epistemically opaque. Thus, the investigation of simulation in nanoscience provides a good example of how science is adapting to a new instrument: computer simulation.

In this paper, I provide an account of scientific representation that makes sense of the notion both at the nanoscale and at the quantum level: the partial mappings account. The account offers an extension of a proposal developed by R. I. G. Hughes in terms of denotation, demonstration, and interpretation (DDI). I first argue that the DDI account needs some amendments to accommodate representation of nano and quantum phenomena. I then introduce a generalized framework with the notions of unsharp denotation, unsharp interpretation, and partial mappings. I conclude by arguing that the resulting view not only accommodates nano and quantum representations but also makes sense of representation by imaging in (scanning tunneling) microscopy.

How, according to Darwin, does chance variation affect evolutionary outcomes? In his 1866 book, On the Various Contrivances by which British and Foreign Orchids are Fertilised by Insects, Darwin developed an argument that played an important role in his overall case for evolution by natural selection, as articulated in later editions of the Origin. This argument also figured significantly in Darwin's reflections on the theological dimensions of evolution by natural selection.

Evolutionary models can explain the dynamics of populations, how genetic, genotypic, or phenotypic frequencies change with time. Models incorporating chance, or drift, predict specific patterns of change. These are illustrated using classic work on blood types by Cavalli-Sforza and his collaborators in the Parma Valley of Italy, in which the theoretically predicted patterns are exhibited in human populations. These data and the models display properties of ensembles of populations. The explanatory problem needs to be understood in terms of how likely an observed change, in either a population or an ensemble, would be under drift alone; this is fundamentally a matter of chance. Understood in this way, issues of drift and chance undercut most recent philosophical, but not biological, discussions of the role of “genetic drift.”

In a small handful of papers in theoretical population genetics, John Gillespie (2000a, 2000b, 2001) argues that a new stochastic process he calls “genetic draft” is evolutionarily more significant than genetic drift. This case study of chance in evolution explores Gillespie's proposed stochastic evolutionary force and sketches the implications of Gillespie's argument for philosophers’ explorations of genetic drift.

This article offers three contrasting cases of the use of neutrality and drift in molecular evolution. In the first, neutrality is assumed as a simplest case for modeling. In the second and third, concepts of drift and neutrality are developed within the context of population genetics testing and the development and application of the molecular clock.

The four case studies on chance in evolution provide a rich source for further philosophical analysis. Among the issues raised are the following: Are there different conceptions of chance at work, or is there a common underlying conception? How can a given concept of chance be distinguished from other chance concepts and from nonchance concepts? How can the occurrence of a given chance process be distinguished empirically from nonchance processes or other chance processes? What role does chance play in evolutionary theory? I argue that in order to answer these questions, a careful distinction between process and outcome must be made; however, the purpose of this essay is not to answer these questions definitively, but rather to elaborate on them and to provide a starting point for further discussion.

Case studies of diverse scientific fields show how scientists use a range of resources to generate new interpretative models and to establish their plausibility as explanations of a domain. They accomplish this by manipulating imagistic representations in particular ways. I show that scientists in different domains use the same basic transformations. Common features of these transformations indicate that general cognitive strategies of interpretation, simplification, elaboration, and argumentation are at work. Social and historical studies of science emphasize the diversity of local contexts of practice. However, the existence of common strategies shows that this diversity masks an important repertoire of cognitive strategies. Scientists use this repertoire to adapt their representations to meet the cognitive demands of different contexts of practice. This paper considers the implications of this finding for the notion of scientists as cognitive agents in distributed knowledge-producing systems.