Abstract: I highlight that the aim of using statistical mechanics to underpin irreversible processes is, strictly speaking, ambiguous. Traditionally, however, the task of underpinning irreversible processes has been thought to be synonymous with underpinning the Second Law of thermodynamics. I claim that contributors to the foundational discussion are best interpreted as aiming to provide a microphysical justification of the Minus First Law, despite the ways their aims are often stated. I suggest that contributors should aim at accounting for both the Minus First Law and Second Law.
Abstract: Scientific models are frequently discussed in philosophy of science. A great deal of the discussion is centred on approximation, idealisation, and on how these models achieve their representational function. Despite the importance, distinct nature, and high presence of toy models, they have received little attention from philosophers. This paper hopes to remedy this situation. It aims to elevate the status of toy models: by distinguishing them from approximations and idealisations, by highlighting and elaborating on several ways the Kac ring, a simple statistical mechanical model, is used as a toy model, and by explaining why toy models can be used to successfully carry out important work without performing a representational function.
This paper was featured as a research highlight in Nature Physics. This is the first time the work of a philosopher has featured as a research highlight.
Abstract: This paper highlights the limitations of typicality accounts of thermodynamic behaviour so as to promote an alternative line of research: understanding and accounting for the success of the techniques and equations physicists use to model the behaviour of systems that begin away from equilibrium. This paper also takes steps in this promising direction. It examines a technique commonly used to model the behaviour of an important kind of system: a Brownian particle that's been introduced to an isolated fluid at equilibrium. It also accounts for the success of the model, by identifying and grounding the technique's key assumptions.