The paper studies the nature of understanding in condensed matter physics (CMP), mediated by the successful employment of its models. I first consider two obvious candidates for the criteria of model-based understanding, Van Fraassen's sense of empirical adequacy and Hacking's instrumental utility , and conclude that both are unsatisfactory. Inspired by Hasok Chang's recent proposal to reformulate realism as the pursuit of ontological plausibility in our system of knowledge, we may require the model under consideration to be understood (or intelligible) (...) before claiming model-based understanding. Here the understanding of a model typically consists of the following: figuring out at least one plausible (preferably realistic) physical mechanism for the model, determining the theoretical consequences of the model by mathematically probing it and developing our physical intuitions about the model. I consider the q-state Potts model to illustrate. After having understood a model, we may employ the model to understand its target phenomena in the world. This is done by matching one of the interpretative models of the model with the central features of the phenomena. The matching should be motivated (ideally both theoretically and empirically) in the sense that we have good reason to believe that the central features of the phenomena can be thought of as having more or less the same structure as postulated by the interpretative model. In conclusion, I propose a two-stage account of model-based understanding in CMP: (1) understanding of a model and (2) matching a target phenomenon with a well-motivated interpretative model of the model. Empirical success and instrumental utility both play their roles in the evaluation of how successful the model is, but are not the essential part of model-based understanding. (shrink)

Lawrence Sklar in his book, Physics and Chance (1993), proposes a sophisticated account of reduction of thermodynamics (TD) by statistical mechanics (SM). I argue that Sklar's analysis of the alleged reduction of TD by SM is problematic in several respects. I consider a few counterexamples to show that none of what Sklar takes to be the central features of successful reduction in science (unification and identification) holds in the case of TD and SM. I suggest the broader conclusion that a (...) more useful way of understanding the relationship between TD and SM is as collaboration and competition among alternative methodologies rather than reduction of one theory to another. (shrink)

In this paper we give a full account of the work of William Thomson on absolute temperature, which to this day provides the theoretical underpinnings for the most rigorous measurements of temperature. When Thomson fashioned his concepts of ‘absolute’ temperature, his main concern was to make the definition of temperature independent of the properties of particular thermometric substances . He tried out a succession of definitions based on the thermodynamics of ideal heat engines; most notably, in 1854 he gave the (...) ratio of two temperatures as the ratio of quantities of heat taken in and given out at those temperatures in a Carnot cycle. But there were difficulties with using such definitions for experimental work, since it was not possible even to approximate an ideal Carnot engine in reality. More generally, it is not trivial to connect an abstract concept with concrete operations in order to make physical measurements possible. In the end, Thomson argued that an ideal gas thermometer would indicate his absolute temperature, and that the deviation of actual gas thermometers from the ideal could be estimated by means of the Joule‐Thomson effect. However, the measurement of the Joule‐Thomson effect itself required measurements of temperature, so there was a problem of circularity. (shrink)

This paper aims: (1) to show that Lawrence Sklar`s recent attempt to reduce thermodynamics(TD) to statistical mechanics(SM) is fallacious in several respects; and (2) to suggest a broader conclusion that a more useful way of understanding the relationship between TD and SM is as collaboration and competition among alternative methodologies rather than reduction of one theory to another. To argue for (1), I discuss two cases (the distinction of intensive/extensive variables in TD and the existence of phase transitions) where TD (...) is more accurate than statistical mechnaics and thus corrects SM. I also discuss the case of temperature in order to argue for both (1) and (2). (shrink)