Formal Epistemology and Decision Theory

Roberto Fumagalli (King's College London)
In recent years, several authors have called to ground descriptive and normative decision theory on neuro-psychological measures of utility. In this paper, I combine insights from the best available neuro-psychological findings, leading philosophical conceptions of welfare and contemporary decision theory to rebut these prominent calls. I argue for two claims of general interest to philosophers, choice modellers and policy makers. First, severe conceptual, epistemic and evidential problems plague ongoing attempts to develop accurate and reliable neuro-psychological measures of utility. And second, even if these problems are solved, neuro-psychological measures of utility lack the potential to inform welfare analyses and policy evaluations.

Joe Roussos (London School of Economics), Roman Frigg (London School of Economics), Richard Bradley (London School of Economics)
Increasingly many policy decisions take input from collections of scientific models. Such decisions face significant, poorly understood uncertainty. We rework the recently developed "confidence" decision theory to tackle decision-making with model ensembles, showing how it can be used to construct nested sets of predictions of increasing specificity. We discuss the conditions under which particular sets are available to decision-makers. We illustrate the approach with a case study: an insurance pricing decision using hurricane models. The confidence approach has important consequences for hurricane insurance, and we generalise these to a wide variety of policymaking contexts.

Kun Zhang (Carnegie Mellon Univerisity), Jiji Zhang (Lingnan University), Clark Glymour (Carnegie Mellon Univerisity)
Causal discovery methods aim to recover the causal process that generated purely observational data. Despite its successes on a number of real problems, the presence of measurement error in the observed data can produce serious mistakes in the output of various causal discovery methods. Given the ubiquity of measurement error caused by instruments or proxies used in the measuring process, this problem is one of the main obstacles to reliable causal discovery. It is still unknown to what extent the causal structure of relevant variables can be identified in principle, let alone how to develop a practical algorithm to solve this problem. This study aims to take a step towards filling that void. We investigate assumptions that suffice for it to be possible in principle to identify causal relations from observed data. Inspired by our theoretical results, we also present a set of methods for causal discovery under measurement error.