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

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

The evidence that evidence-based medicine omits

According to current hierarchies of evidence for EBM, evidence of correlation (e.g., from RCTs) is always more important than evidence of mechanisms when evaluating and establishing causal claims. We argue that evidence of mechanisms needs to be treated alongside evidence of correlation. This is for three reasons. First, correlation is always a fallible indicator of causation, subject in particular to the problem of confounding; evidence of mechanisms can in some cases be more important than evidence of correlation when assessing a causal claim. Second, evidence of mechanisms is often required in order to obtain evidence of correlation (for example, in order to set up and evaluate RCTs). Third, evidence of mechanisms is often required in order to generalise and apply causal claims. While the EBM movement has been enormously successful in making explicit and critically examining one aspect of our evidential practice, i.e., evidence of correlation, we wish to extend this line of work to make explicit and critically examine a second aspect of our evidential practices: evidence of mechanisms

In defence of activities

In this paper, we examine what is to be said in defence of Machamer, Darden and Craver’s (MDC) controversial dualism about activities and entities (Machamer, Darden and Craver’s in Philos Sci 67:1–25, 2000). We explain why we believe the notion of an activity to be a novel, valuable one, and set about clearing away some initial objections that can lead to its being brushed aside unexamined. We argue that substantive debate about ontology can only be effective when desiderata for an ontology are explicitly articulated. We distinguish three such desiderata. The first is a more permissive descriptive ontology of science, the second a more reductive ontology prioritising understanding, and the third a more reductive ontology prioritising minimalism. We compare MDC’s entities-activities ontology to its closest rival, the entities-capacities ontology, and argue that the entities-activities ontology does better on all three desiderata

Models for Prediction, Explanation and Control : Recursive Bayesian Nets

THEORIA is a non-profit editorial venture. It is published by the University of the Basque Country under a Creative Commons Licence. It is part of the Open Journal System (OJS) and all its papers (from 2003 onwards) are freely available on-line.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.Peer reviewedFinal Published versio

Varieties of Mechanisms

The things that scientists and philosophers have called mechanisms are a diverse lot. In this Chapter we provide some taxonomic principles to help sort them out. We start with the permissive conception called minimal mechanism, which suggests that a mechanism consists of entities whose activities and interactions are organized so as to be responsible for some phenomenon. We then sort mechanistic varieties by considering the varieties of phenomena, varieties of entities and activities/interactions, varieties of organization and also varieties of origins (etiologies). We explore interdependencies within this taxonomic space, and consider why certain varieties of mechanisms are characteristic of certain scientific domains, how many of these varieties cross domains, and how different varieties are relevant to different kinds of metaphysical or methodological projects