This essay focuses on the place of the second law of thermodynamics in Wilhelm Ostwald's physical chemistry. After a brief introduction to his energetic theory, which was supposed to be a generalization of thermodynamics, I contrast Ostwald's understanding of the second law, which ignored entropy and irreversibility, with Max Planck's, which emphasized both. I then consider how Ostwald sought to develop physical chemistry without any concern for irreversibility and little concern for entropy, and I argue that he was mistaken.

Gibbs's phase rule treats mixtures by relating the number of independent variables governing their state to the numbers of phases and independent substances. For the case of a single substance, it provides a criterion of purity. But where more substances are involved, the notion of independent substance is less readily understood. Textbook writers sometimes use algebraic terminology in ways that are suggestive but cannot be taken as literally accurate. I suggest that a mereological interpretation applies to these cases, as it captures more concisely the insights underlying the use of algebraic terminology and illuminates the general notion of substance.

Varieties of chemical and phase equilibria are controlled by the minimum Gibbs energy principle, according to which the Gibbs energy for a system will have the minimum value at any given temperature and pressure. It is understood that the minimum is with respect to all nonequilibrium states at the same temperature and pressure. The abstract relation between Gibbs energy and the equilibrium constant is deduced from fundamental laws of thermodynamics. However, actual use of this relation calls for the Gibbs energy as a function of concentrations of the chemicals. Since thermodynamics is formulated without any reference to materials, how does one get that relation? This article provides the answer, and in the process shows that application of theory to experiments requires several intermediate layers where theory and experiment commingle.

In this essay I critically examine the role of entropy of mixing in articulating a macroscopic criterion for the sameness and difference of chemical substances. Consider three cases of mixing in which entropy change occurs: isotopic variants, spin isomers, and populations of atoms in different orthogonal quantum states. Using these cases I argue that entropy of mixing tracks differences between physical states, differences that may or may not correspond to a difference of substance. It does not provide a criterion for the sameness and difference of substance that is appropriate to chemistry.

Explanations in physics commonly appeal to data drawn from different length or time scales, as when a “top-down” macroscopic constraint such as rigidity is used to evade the complexities one would confront in attempting to model the situation in a purely “bottom-up” fashion. Such techniques commonly embody rather complex shifts in explanatory strategy.

In this paper, I discuss the prospects of nominalistic scientific realism (NSR) and show that it fails on many counts. In section 2, I discuss what is required for NSR to get off the ground. In section 3, I question the idea that theories have well-defined nominalistic content and the idea that causal activity is a necessary condition for commitment to the reality of an entity. In section 4, I challenge the notion of nominalistic adequacy of theories.

This article begins by distinguishing intrinsic and extrinsic contributions of mathematics to scientific representation. This leads to two investigations into how these different sorts of contributions relate to confirmation. I present a way of accommodating both contributions that complicates the traditional assumptions of confirmation theory. In particular, I argue that subjective Bayesianism does best in accounting for extrinsic contributions, while objective Bayesianism is more promising for intrinsic contributions.

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

Today's most sophisticated simulation studies of future climate employ not just one climate model but a number of models. I explain why this “ensemble” approach has been adopted—namely, as a means of taking account of uncertainty—and why a comprehensive investigation of uncertainty remains elusive. I then defend a middle ground between two camps in an ongoing debate over the transformation of ensemble results into probabilistic predictions of climate change, highlighting requirements that I refer to as ownership, justification, and robustness.

Increasing societal concerns regarding the potential deleterious effects of future climate change have galvanized efforts to manage the problem both through reduction of greenhouse gases (mitigation) and through development of plans to reduce the impacts of climate change that cannot be avoided (adaptation). These critical activities require making decisions under conditions of considerable uncertainty regarding future conditions in physical and human systems. As the focus on providing information about future climate for taking actions to cope with climate change, the science of uncertainty of climate change will develop more and more with the needs of decision makers in mind.

Scientific experts have confirmed that anthropogenic warming is underway, and some degree of adaptation is now unavoidable. However, the details of impacts on the scale of climate change at which humans would have to prepare for and adjust to them are still the subject of considerable research, inquiry, and debate. Planning for adaptation requires information on the scale over which human organizations and institutions have authority and capacity, yet the general circulation models lack forecasting skill at these scales, and attempts to “downscale” climate models are still in the early stages of development. Because we do not know what adaptations will be required, we cannot say whether they will be harder or easier—more expensive or less—than emissions control. Whatever improvements in regional predictive capacity may come about in the future, the lack of current predictive capacity on the relevant scale is a strong argument for why we must both control greenhouse gas emissions and prepare to adapt.

This article investigates the nature of entangled correlations in algebraic quantum field theory (AQFT). We define a notion of local disentanglement, expressing the possibility of destroying entanglement by means of local operations. Contrary to the case of ordinary quantum mechanics, local disentanglement cannot be achieved in general in relativistic quantum field theory. However, we show that if the split property holds, there exists a local operation that can destroy entanglement between spacelike-separated quantum field systems.

It is argued that in his critique of standard nonrelativistic quantum mechanics Einstein formulated three requirements as necessary for a physical theory to be compatible with the field-theorectical paradigm, and it is shown that local, relativistic, algebraic quantum field theory typically satisfies those criteria—although, there are still open questions concerning the status of operational separability of quantum systems localized in spacelike separated space-time regions. It is concluded that local algebraic quantum field theory can be viewed as a research program that Einstein suggested informally in 1948 and that was realized only later in mathematical physics.

This essay sketches an account of extrapolation in terms of analogical reasoning that generalizes the notion of comparative process tracing. The account offered here relies on something known as chain graphs, which are used to explicate the role of “fingerprints,” or distinctive markers, as a strategy for avoiding an underdetermination problem having to do with spurious analogies. Moreover, I show how the extrapolator's circle, one of the central challenges for extrapolation, is closely tied to distinctive markers and the Markov condition as it applies to chain graphs.

Comparative process tracing is the best analysis of extrapolation inferences in the philosophical and scientific literature so far. In this essay I examine some similarities and differences between comparative process tracing and former attempts to capture the logic of extrapolation, such as the analogical approach. I show that these accounts are not different in spirit, although comparative process tracing supersedes previous proposals in terms of analytical detail. I also examine some qualms about the possibility of drawing extrapolation inferences in the social sciences and conclude by suggesting that there may be cases of extrapolation without process tracing.

Climate change fingerprint studies investigate the causes of recent climate change. I argue that these studies have much in common with Steel's streamlined comparative process tracing, illustrating a mechanisms-based approach to extrapolation in which the mechanisms of interest are simulated rather than physically instantiated. I then explain why robustness and variety-of-evidence considerations turn out to be important for understanding the evidential value of climate change fingerprint studies.

The Complex-First Paradox consists in a set of collectively incompatible but individually well-confirmed propositions that regard the evolution, development, and cortical realization of the meanings of concrete nouns. Although these meanings are acquired earlier than those of other word classes, they are semantically more complex and their cortical realizations more widely distributed. For a neurally implemented syntax-semantics interface, it should thus take more effort to establish a link between a concept and its lexical expression. However, in ontogeny and phylogeny, capabilities demanding more effort, ceteris paribus, develop and evolve later than those demanding less effort. The paradox points to an explanatory deficit in linguistic theory.

Stars are remarkably absent from reflections on natural kinds and classifications, with gold, tiger, jade, and water getting all the philosophical attention. It is a pity, for interesting philosophical lessons can be drawn from stellar taxonomy as regards two central debates about natural kinds, to wit, the monism/pluralism debate and the realism/antirealism debate. I show in particular that stellar kinds will not please the essentialist monist, nor will it please the pluralist embracing promiscuous realism à la Dupré. I conclude on a more general note by questioning the relationship between taxonomic scientific practice and philosophical doctrines of natural kinds.

Naturalists tell us to rely on what science tells about the world and to eschew aprioristic philosophy. But foundational physics relies internally on modes of thinking that can only be called philosophical, and philosophical arguments rely upon what can only be called scientific inference. So what, then, could the naturalistic thesis really amount to?