Over the past three decades, the discourse on the mechanistic approach to scientific modelling and explanation has notably sidestepped the topic of simplicity and fit within the process of model selection. This paper aims to rectify this disconnect by delving into the topic of simplicity and fit within the context of mechanistic explanations. More precisely, our primary objective is to address whether simplicity metrics hold any significance within mechanistic explanations. If they do, then our inquiry extends to the suitability of (...) the Akaike Information Criterion (AIC), the Bayesian Information Criterion (BIC), and related criteria in determining the optimal balance of fit and simplicity of mechanistic models. As our main claims, we argue that mechanistic models inherently lend themselves to considerations of simplicity, and that the AIC and BIC and related criteria are applicable to some submodels of certain kinds of mechanistic models. However, these criteria and related criteria designed for curve fitting and causal modelling are of little help for a comparative assessment of full mechanistic models, and a fundamentally different approach is needed to make determinations of this kind. (shrink)

In MemoriamElena Mamchur8 July, 1935–14 December, 2023. The principle of ‘maximum inheritance’, put forward by Elena Mamchur, presupposes its effect in scientific cognition even during ‘radical cha...

Should the scientific realist admit that their realism involves what Chakravartty has called an epistemic stance? I argue that the realist should accept the need for a realist stance that licenses the use of inference to the best explanation. However, unlike Chakravartty, I maintain that the realist should insist that their realist stance is rationally obligatory. This requires an anti-voluntarism about stances that involves theoretical reasons for adopting one stance rather than another. I present one account of what these reasons (...) might be that is tied to principles that identify some strong connections between evidence, knowledge and genuine explanation. (shrink)

Natural selection is a general process that operates in different populations. To characterise natural selection as a mechanism within the framework of the new mechanistic philosophy, it is required to identify a pertinent phenomenon for which natural selection is responsible. Firstly, every case identified by evolutionary biologists as instances of natural selection must align with this mechanistic characterisation. Secondly, natural selection should genuinely be responsible for the attributed phenomenon. While philosophers often posit producing adaptation as the quintessential phenomenon, Pérez-González and (...) Luque challenge this perspective, contending that not all instances of natural selection are cases where natural selection produces adaptation. This paper further supports Pérez-González and Luque’s objection and demonstrates that natural selection is also not responsible for the increased frequency of traits with a greater expected number of offspring. Furthermore, the paper argues that even if there is a phenomenon that allows for the mechanistic characterisation of natural selection to encompass all instances, it is improbable that natural selection assumes responsibility for it. Consequently, characterising natural selection as a mechanism emerges as an implausible stance. (shrink)

Artificial neural networks and supervised learning have become an essential part of science. Beyond using them for accurate input-output mapping, there is growing attention to a new feature-oriented approach. Under the assumption that networks optimised for a task may have learned to represent and utilise important features of the target system for that task, scientists examine how those networks manipulate inputs and employ the features networks capture for scientific discovery. We analyse this approach, show its hidden caveats, and suggest its (...) legitimate use. We distinguish three things that scientists call a ‘feature’: parametric, diagnostic, and real-world features. The feature-oriented approach aims for real-world features by interpreting the former two, which also partially rely on the network. We argue that this approach faces a problem of non-uniqueness: there are numerous discordant parametric and diagnostic features and ways to interpret them. When the approach aims at novel discovery, scientists often need to choose between those options, but they lack the background knowledge to justify their choices. Consequentially, features thus identified are not promised to be real. We argue that they should not be used as evidence but only used instrumentally. We also suggest transparency in feature selection and the plurality of choices. (shrink)