John Matthewson

The global pandemic needs to mark a turning point for the peoples of Aotearoa New Zealand. How can we make sure that our culturally diverse nation charts an equitable and sustainable path through and beyond this new world? In a less affluent future, how can we ensure that all New Zealanders have fair access to opportunities? One challenge is to preserve the sense of common purpose so critical to protecting each other in the face of Covid-19. How can we centre what we have learnt about resilience within Māori and wider Pacific communities in our reforms? How can public understanding of Covid-19 science create a platform for the future social valuing of expertise? How can we ensure that the impact of Covid-19 in New Zealand results in a more sustainable, and inclusive workforce–for instance by expanding our perceptions of the value of our workers through promoting digital inclusion? To meet these challenges, we must reimagine our existing traditions of thought, breathing new life into perennial concepts and debates. Our paper indicates some of the ways that Philosophy is central to this collective reimagining, highlighting solutions to be found across our rich philosophical traditions.

Natural selection comes in degrees. Some biological traits are subjected to stronger selective force than others, selection on particular traits waxes and wanes over time, and some groups can only undergo an attenuated kind of selective process. This has downstream consequences for any notions that are standardly treated as binary but depend on natural selection. For instance, the proper function of a biological structure can be defined as what caused that structure to be retained by natural selection in the past. We usually think of proper functions in binary terms: storing bile is a function of the gall bladder, but making stones is not. However, if functions arise through natural selection, and natural selection comes in degrees, then a binary approach to proper functions is in tension with the biological facts. In order to resolve this tension, we need to revise our standard accounts of proper function. In particular, we may have to seriously consider the possibility that functions themselves come in degrees, in spite of the ramifications this will have for the way we speak about functions and related concepts such as dysfunction, disease, and teleosemantic content.

This article is about the role of abstraction in mechanistic explanations. Abstraction is widely recognised as a necessary concession to the practicalities of scientific work, but some mechanist philosophers argue that it is also a positive explanatory feature in its own right. I claim that in as much as these arguments are based on the idea that mechanistic explanation exhibits a trade-off between fine-grained detail and generality, they are unsuccessful. Detail and generality both appear to be important sources of explanatory power, but investigators do not need to make a choice between these desiderata, at least when an explanation incorporates further detail through the decomposition of the mechanism's parts.

We defend a view of the distinction between the normal and the pathological according to which that distinction has an objective, biological component. We accept that there is a normative component to the concept of disease, especially as applied to human beings. Nevertheless, an organism cannot be in a pathological state unless something has gone wrong for that organism from a purely biological point of view. Biology, we argue, recognises two sources of biological normativity, which jointly generate four “ways of going wrong” from a biological perspective. These findings show why previous attempts to provide objective criteria for pathology have fallen short: Biological science recognizes a broader range of ways in which living things can do better or worse than has previously been recognized in the philosophy of medicine.

Some ‘naturalist’ accounts of disease employ a biostatistical account of dysfunction, whilst others use a ‘selected effect’ account. Several recent authors have argued that the biostatistical account offers the best hope for a naturalist account of disease. We show that the selected effect account survives the criticisms levelled by these authors relatively unscathed, and has significant advantages over the BST. Moreover, unlike the BST, it has a strong theoretical rationale and can provide substantive reasons to decide difficult cases. This is illustrated by showing how life-history theory clarifies the status of so-called diseases of old age. The selected effect account of function deserves a more prominent place in the philosophy of medicine than it currently occupies. _1_ Introduction _2_ Biostatistical and Selected Effect Accounts of Function _3_ Objections to the Selected Effect Account _3.1_ Boorse _3.2_ Kingma _3.3_ Hausman _3.4_ Murphy and Woolfolk _4_ Problems for the Biostatistical Account _4.1_ Schwartz _5_ Analysis versus Explication _6_ Explicating Dysfunction: Life History Theory and Senescence _7_ Conclusion

Despite their best efforts, scientists may be unable to construct models that simultaneously exemplify every theoretical virtue. One explanation for this is the existence of tradeoffs: relationships of attenuation that constrain the extent to which models can have such desirable qualities. In this paper, we characterize three types of tradeoffs theorists may confront. These characterizations are then used to examine the relationships between parameter precision and two types of generality. We show that several of these relationships exhibit tradeoffs and discuss what consequences those tradeoffs have for theoretical practice

This paper presents an account of the biological populations that can undergo paradigmatic natural selection. I argue for, and develop Peter Godfrey-Smith’s claim that reproductive competition is a core attribute of such populations. However, as Godfrey-Smith notes, it is not the only important attribute. I suggest what the missing element is, co-opting elements of Alan Templeton’s notion of exchangeability. The final framework is then compared to two recent discussions regarding biological populations proposed by Roberta Millstein and Jacob Stegenga.

This paper is about mechanisms and models, and how they interact. In part, it is a response to recent discussion in philosophy of biology regarding whether natural selection is a mechanism. We suggest that this debate is indicative of a more general problem that occurs when scientists produce mechanistic models of populations and their behaviour. We can make sense of claims that there are mechanisms that drive population-level phenomena such as macroeconomics, natural selection, ecology, and epidemiology. But talk of mechanisms and mechanistic explanation evokes objects with well-defined and localisable parts which interact in discrete ways, while models of populations include parts and interactions that are neither local nor discrete in any actual populations. This apparent tension can be resolved by carefully distinguishing between the properties of a model and those of the system it represents. To this end, we provide an analysis that recognises the flexible relationship between a mechanistic model and its target system. In turn, this reveals a surprising feature of mechanistic representation and explanation: it can occur even when there is a mismatch between the mechanism of the model and that of its target. Our analysis reframes the debate, providing an alternative way to interpret scientists’ mechanism-talk , which initially motivated the issue. We suggest that the relevant question is not whether any population-level phenomenon such as natural selection is a mechanism, but whether it can be usefully modelled as though it were a particular type of mechanism

In his 1966 paper “The Strategy of model-building in Population Biology”, Richard Levins argues that no single model in population biology can be maximally realistic, precise and general at the same time. This is because these desirable model properties trade-off against one another. Recently, philosophers have developed Levins’ claims, arguing that trade-offs between these desiderata are generated by practical limitations on scientists, or due to formal aspects of models and how they represent the world. However this project is not complete. The trade-offs discussed by Levins had a noticeable effect on modelling in population biology, but not on other sciences. This raises questions regarding why such a difference holds. I claim that in order to explain this finding, we must pay due attention to the properties of the systems, or targets modelled by the different branches of science.Keywords: Trade-offs; Population biology; Scientific models; Generality; Precision.