IQ: Purpose and Dimensions

In this article I examine the problem of categorising dimensions of information quality (IQ), against the background of a serious engagement with the hypothesis that IQ is purpose-dependent. First, I examine some attempts to offer categories for IQ, and a specific problem that impedes convergence in such categorisations is diagnosed. Based on this new understanding, I suggest a new way of categorising both IQ dimensions and the metrics used in implementation of IQ improvement programmes according to what they are properties of. I conclude the paper by outlining an initial categorisation of some IQ dimensions and metrics in standard use to illustrate the value of the approach

Mechanisms, models and laws in understanding supernovae

There has been a burst of work in the last couple of decades on mechanistic explanation, as an alternative to the traditional covering-law model of scientific explanation. That work makes some interesting claims about mechanistic explanations rendering phenomena ‘intelligible’, but does not develop this idea in great depth. There has also been a growth of interest in giving an account of scientific understanding, as a complement to an account of explanation, specifically addressing a three-place relationship between explanation, world, and the scientific community. The aim of this paper is to use the contextual theory of scientific understanding to build an account of understanding phenomena using mechanistic explanations. This account will be developed and illustrated by examining the mechanisms of supernovae, which will allow synthesis of treatment of the life sciences and social sciences on the one hand, where many accounts of mechanisms were originally developed, and treatment of physics on the other hand, where the contextual theory drew its original inspiration

Mechanisms and the Evidence Hierarchy

Evidence-based medicine (EBM) makes use of explicit procedures for grading evidence for causal claims. Normally, these procedures categorise evidence of correlation produced by statistical trials as better evidence for a causal claim than evidence of mechanisms produced by other methods. We argue, in contrast, that evidence of mechanisms needs to be viewed as complementary to, rather than inferior to, evidence of correlation. In this paper we first set out the case for treating evidence of mechanisms alongside evidence of correlation in explicit protocols for evaluating evidence. Next we provide case studies which exemplify the ways in which evidence of mechanisms complements evidence of correlation in practice. Finally, we put forward some general considerations as to how the two sorts of evidence can be more closely integrated by EBM

Information channels and biomarkers of disease

Current research in molecular epidemiology uses biomarkers to model the different disease phases from environmental exposure, to early clinical changes, to development of disease. The hope is to get a better understanding of the causal impact of a number of pollutants and chemicals on several diseases, including cancer and allergies. In a recent paper Russo and Williamson (2012) address the question of what evidential elements enter the conceptualisation and modelling stages of this type of biomarkers research. Recent research in causality has examined Ned Hall’s distinction between two concepts of causality: production and dependence (Hall, 2004). In another recent paper, Illari (2011b) examined the relatively under-explored production approach to causality, arguing that at least one job of an account of causal production is to illuminate our inferential practices concerning causal linking. Illari argued that an informational account solves existing problems with traditional accounts. This paper follows up this previous work by investigating the nature of the causal links established in biomarkers research. We argue that traditional accounts of productive causality are unable to provide a sensible account of the nature of the causal link in biomarkers research, while an informational account is very promising

Using deep neural networks and similarity metrics to predict and control brain responses

In the last ten years there has been an increase in using artificial neural networks to model brain mechanisms, giving rise to a deep learning revolution in neuroscience. This chapter focuses on the ways convolutional deep neural networks (DCNNs) have been used in visual neuroscience. A particular challenge in this developing field is the measurement of similarity between DCNNs and the brain. We survey similarity measures neuroscientists use, and analyse their merit for the goals of causal explanation, prediction and control. In particular, we focus on two recent intervention-based methods of comparing DCNNs and the brain that are based on linear mapping (Bashivan et al., 2019, Sexton and Love, 2022), and analyse whether this is an improvement. While we conclude explanation has not been reached for reasons of underdetermination, progress has been made with regards to prediction and control

Models for prediction, explanation and control: recursive bayesian networks

The Recursive Bayesian Net (RBN) formalism was originally developed for modelling nested causal relationships. In this paper we argue that the formalism can also be applied to modelling the hierarchical structure of mechanisms. The resulting network contains quantitative information about probabilities, as well as qualitative information about mechanistic structure and causal relations. Since information about probabilities, mechanisms and causal relations is vital for prediction, explanation and control respectively, an RBN can be applied to all these tasks. We show in particular how a simple two-level RBN can be used to model a mechanism in cancer science. The higher level of our model contains variables at the clinical level, while the lower level maps the structure of the cell's mechanism for apoptosis