A conspicuous feature of contemporary modelling practices is the use of the same mathematical forms and modelling methods across different scientific domains. This model transfer raises many philosophical questions concerning, for example, the exact object of transfer, the relationship between the model and the target domain, the specific challenges such transfer confronts, and the ways in which model transfer relates to scientific progress. While the interest in studying model transfer has increased among philosophers of science in recent years, the phenomenon (...) has not yet received its due attention. The literature is scattered and much of it has addressed questions around the factors that might explain model transfer, such as whether the different target phenomena to which a model is applied eventually prove to be structurally and dynamically similar, or whether they have to share more fundamental ontological features that explain the interdisciplinary success of distinct models. This chapter surveys the existing literature on model transfer with the main goal of highlighting the main findings of existing discussions and pointing out open issues whose discussion would improve our understanding of this highly relevant phenomenon in science. (shrink)

The philosophy of science community mourns the loss of Margaret Catherine Morrison, who passed away on January 9, 2021, after a long battle with cancer. Margie, as she was known to all who knew her, was highly regarded for her influential contributions to the philosophy of science, particularly her studies of the role of models and simulations in the natural and social sciences. These contributions made her a world-leading philosopher of science, instrumental in shifting philosophers' attention from the structure of (...) scientific theories to the practice of science. Her sophisticated studies of the function of models in scientific practice drew on detailed knowledge of the theories and experiments of physics as well as the history of physics. In emphasizing the autonomy of scientific models and their interventional character, her insights had some affinity with Cartwright's and Hacking’s views on phenomenological laws, entity realism, the instrumentalist interpretation of scientific theories, and the disunity of science. But Morrison’s approach was distinguished by the conviction that the existence of unobservable entities cannot be defended independently of the theories that support their evidence, and that scientific practice cannot be adequately understood without examining the reasons for theory unification. (shrink)

Many accounts of scientific modelling assume that models can be decomposed into the contributions made by their accurate and inaccurate parts. These accounts then argue that the inaccurate parts of the model can be justified by distorting only what is irrelevant. In this paper, I argue that this decompositional strategy requires three assumptions that are not typically met by our best scientific models. In response, I propose an alternative view in which idealized models are characterized as holistically distorted representations that (...) are justified by allowing for the application of various modelling techniques. (shrink)

In this paper, I first argue against various attempts to justify idealizations in scientific models that explain by showing that they are harmless and isolable distortions of irrelevant features. In response, I propose a view in which idealized models are characterized as providing holistically distorted representations of their target system. I then suggest an alternative way that idealized modeling can be justified by appealing to universality.

Scientific models represent aspects of the empirical world. I explore to what extent this representational relationship, given the specific properties of models, can be analysed in terms of propositions to which truth or falsity can be attributed. For example, models frequently entail false propositions despite the fact that they are intended to say something "truthful" about phenomena. I argue that the representational relationship is constituted by model users "agreeing" on the function of a model, on the fit with data and (...) on the aspects of a phenomenon that are modelled. Model users weigh the propositions entailed by a model and from this decide which of these propositions are crucial to the acceptance and continued use of the model. Thus, models represent phenomena when certain propositions they entail are true, but this alone does not exhaust the representational relationship. Therefore, the constraints that produce the choice of the relevant propositions in a model must also be examined and their analysis contributes to understanding the relationship between models and phenomena. (shrink)

In this paper, we argue that rather than exclusively focusing on trying to determine if an idealized model fits a particular account of scientific explanation, philosophers of science should also work on directly analyzing various explanatory schemas that reveal the steps and justification involved in scientists’ use of highly idealized models to formulate explanations. We develop our alternative methodology by analyzing historically important cases of idealized statistical modeling that use a three-step explanatory schema involving idealization, mathematical operation, and explanatory interpretation.

The relationship between statistical mechanics and population genetics has a long history. Both take advantage of statistics to address the behavior of large groups of entities. The main objective of this article is to assess the obstacles population genetics is meeting in its claim to explain biological phenomena from the conceptual apparatus of statistical mechanics according to two recent articles. Several tools available to the latter are missing in the former. Thus, in the absence of an adequate justification of the (...) relevance of the analogy and without the satisfaction of certain basic assumptions, I argue that the explanatory strategy of the analogously based model is seriously compromised. La relation entre mécanique statistique et génétique des populations a une longue histoire. À l’instar de la première, la seconde tire profit des statistiques afin de traiter le comportement des grands ensembles d’entités. L’objectif principal du présent article est d’identifier les obstacles que rencontre la génétique des populations dans sa prétention d’explication des phénomènes biologiques à partir de l’appareillage conceptuel de la mécanique statistique selon deux articles récents. Or, plusieurs outils dont dispose cette dernière manque à la première. Ainsi, en l’absence d’une justification adéquate de la pertinence de l’analogie et sans la satisfaction de certaines hypothèses fondamentales, je soutiens que la stratégie explicative du modèle analogue est sérieusement compromise. (shrink)