Studies in History and Philosophy of Science最新文献

The theory of Selected Effects (SE) is currently the most widely accepted etiological account of function in biology. It argues that the function of any trait is the effect that past traits of that type produced that contributed to its current existence. Its proper or etiological function is whatever effect was favoured by natural selection irrespective of the trait's current effects. By defining function with respect to the effects of natural selection, the theory claims to eschew the problem of backwards causality and to ground functional normativity on differential reproduction or differential persistence. Traditionally, many have criticised the theory for its inability to envisage any function talk outside selective reproduction, for failing to account for the introduction of new functions, and for treating function as epiphenomenal. This article unveils four additional critiques of the SE theory that highlight the source of its critical problems. These critiques follow from the fact that natural selection is not a form of work, but a passive filter that merely blocks or permits prior functioning traits to be reproduced. Natural selection necessarily assumes the causal efficacy of prior organism work to produce the excess functional traits and offspring from which only the best fitted will be preserved. This leads to four new incapacities of the SE theory, which will be here analysed: (i) it provides no criterion for determining what distinguishes a proper from an incidental function; (ii) it cannot distinguish between neutral, incidental, and malfunctioning traits, thus treating organism benefit as irrelevant; (iii) it fails to account for the physical work that makes persistence and reproduction possible, and (iv) in so doing, it falls into a vicious regress. We conclude by suggesting that, inspired by Mills and Beatty's propensity interpretation, the aporia of backward causation implicit in anticipatory accounts of function can also be avoided by a dispositional approach that defines function in terms of work that synchronously counters the ubiquitous tendency for organism entropy to increase in the context of far-from-equilibrium thermodynamics.

Francis Ysidro Edgeworth's unduly neglected monograph New and Old Methods of Ethics (1877) advances a highly sophisticated and mathematized account of social well-being in the utilitarian tradition of his 19th-century contemporaries. This article illustrates how his usage of the ‘calculus of variations’ was combined with findings from empirical psychology and economic theory to construct a consequentialist axiological framework. A conclusion is drawn that Edgeworth is a methodological predecessor to several important methods, ideas, and issues that continue to be discussed in contemporary social well-being studies.

Machine learning (ML) models recently led to major breakthroughs in predictive tasks in the natural sciences. Yet their benefits for the social sciences are less evident, as even high-profile studies on the prediction of life trajectories have shown to be largely unsuccessful – at least when measured in traditional criteria of scientific success. This paper tries to shed light on this remarkable performance gap. Comparing two social science case studies to a paradigm example from the natural sciences, we argue that, in addition to explanation, prediction is an important goal of social science – and we identify constraints that impede pure ML prediction from being successful in that field. As a remedy, we outline elements of an integrative modelling approach that combines explanatory models and predictive ML models.

Research in pharmacogenomics and precision medicine has recently introduced the concept of Polygenic Scores (PGSs), namely, indexes that aggregate the effects that many genetic variants are predicted to have on individual disease risk. The popularity of PGSs is increasing rapidly, but surprisingly little attention has been paid to the idealisations they make about phenotypic development. Indeed, PGSs rely on quantitative genetics models and methods, which involve considerable theoretical assumptions that have been questioned on various grounds. This comes with epistemological and ethical concerns about the use of PGSs in clinical decision-making. In this paper, I investigate to what extent idealisations in genetics models can impact the data gathering and clinical interpretation of genomics findings, particularly the calculation and predictive accuracy of PGSs. Although idealisations are considered ineliminable components of scientific models, they may be legitimate or not depending on the epistemic aims of a model. I thus analyse how various idealisations have been introduced in classical models and progressively readapted throughout the history of genetic theorising. Notably, this process involved important changes in the epistemic purpose of such idealisations, which raises the question of whether they are legitimate in the context of contemporary genomics.

In a historical account recently published in this journal Dhein argues that the current debate whether insects like bees and ants use cognitive maps (centralized map hypothesis) or other means of navigation (decentralized network hypothesis) largely reflects the classical debate between American experimental psychologists à la Tolman and German ethologists à la Lorenz, respectively. In this dichotomy we, i.e., the proponents of the network hypothesis, are inappropriately placed on the Lorenzian line. In particular, we argue that in contrast to Dhein's claim our concepts are not based on merely instinctive or peripheral modes of information processing. In general, on the one side our approaches have largely been motivated by the early biocybernetics way of thinking. On the other side they are deeply rooted in studies on the insect's behavioral ecology, i.e., in the ecological setting within which the navigational strategies have evolved and within which the animal now operates. Following such a bottom-up approach we are not “anti-cognitive map researchers” but argue that the results we have obtained in ants, and also the results of some decisive experiments in bees, can be explained and simulated without the need of invoking metric maps.