Dissertation, London School of Economics (2020)
Authors |
|
Abstract |
Policymakers who seek to make scientifically informed decisions are constantly confronted by scientific uncertainty and expert disagreement. This thesis asks: how can policymakers rationally respond to expert disagreement and scientific uncertainty? This is a work of non-ideal theory, which applies formal philosophical tools developed by ideal theorists to more realistic cases of policymaking under scientific uncertainty.
I start with Bayesian approaches to expert testimony and the problem of expert disagreement, arguing that two popular approaches— supra-Bayesianism and the standard model of expert deference—are insufficient. I develop a novel model of expert deference and show how it can deal with many of these problems raised for them.
I then turn to opinion pooling, a popular method for dealing with disagreement. I show that various theoretical motivations for pooling functions are irrelevant to realistic policymaking cases. This leads to a cautious recommendation of linear pooling. However, I then show that any pooling method relies on value judgements, that are hidden in the selection of the scoring rule.
My focus then narrows to a more specific case of scientific uncertainty: multiple models of the same system. I introduce a particular case study involving hurricane models developed to support insurance decision-making. I recapitulate my analysis of opinion pooling in the context of model ensembles, confirming that my hesitations apply. This motivates a shift of perspective, to viewing the problem as a decision theoretic one. I rework a recently developed ambiguity theory, called the confidence approach, to take input from model ensembles. I show how it facilitates the resolution of the policymaker’s problem in a way that avoids the issues encountered in previous chapters. This concludes my main study of the problem of expert disagreement.
In the final chapter, I turn to methodological reflection. I argue that philosophers who employ the mathematical methods of the prior chapters are modelling. Employing results from the philosophy of scientific models, I develop the theory of normative modelling. I argue that it has important methodological conclusions for the practice of formal epistemology, ruling out popular moves such as searching for counterexamples.
|
Keywords | expert disagreement policymaking ambiguity model ensembles deference |
Categories | (categorize this paper) |
Options |
![]() ![]() ![]() ![]() |
Download options
References found in this work BETA
Simulation and Similarity: Using Models to Understand the World.Michael Weisberg - 2013 - Oxford University Press.
Nature's Capacities and Their Measurement.Nancy Cartwright - 1989 - Oxford, England: Oxford University Press.
View all 71 references / Add more references
Citations of this work BETA
Beyond Uncertainty: Reasoning with Unknown Possibilities.Katie Steele & H. Orri Stefánsson - 2021 - Cambridge University Press.
Similar books and articles
Making Confident Decisions with Model Ensembles.Joe Roussos, Richard Bradley & Roman Frigg - 2021 - Philosophy of Science 88 (3):439-460.
Moral Experts, Deference & Disagreement.Jonathan Matheson, Nathan Nobis & Scott McElreath - 2018 - In Nathan Nobis, Scott McElreath & Jonathan Matheson (eds.), Moral Expertise. Springer Verlag.
Experts in Uncertainty: Opinion and Subjective Probability in Science.Roger M. Cooke (ed.) - 1991 - Oxford, England: Oxford University Press.
Moral Realism and Expert Disagreement.Prabhpal Singh - 2020 - Trames: A Journal of the Humanities and Social Sciences 24 (3):441-457.
Believing to Belong: Addressing the Novice-Expert Problem in Polarized Scientific Communication.Helen De Cruz - 2020 - Social Epistemology 34 (5):440-452.
When Expert Disagreement Supports the Consensus.Finnur Dellsén - 2018 - Australasian Journal of Philosophy 96 (1):142-156.
Overcoming Expert Disagreement In A Delphi Process. An Exercise In Reverse Epistemology.Lalumera Elisabetta - 2015 - Humana Mente 8 (28):87-103.
Moral Experts, Deference & Disagreement.Nathan Nobis, Scott McElreath & Jonathan Matheson - 2018 - In Jamie Carlin Watson & Laura K. Guidry-Grimes (eds.), Moral Expertise: New Essays From Theoretical and Clinical Bioethics. Springer Verlag.
Understanding and Assessing Uncertainty of Observational Datasets for Model Evaluation Using Ensembles.Marius Zumwald, Benedikt Knüsel, Christoph Baumberger, Gertrude Hirsch Hadorn, David Bresch & Reto Knutti - 2020 - WIREs Climate Change 10:1-19.
Scientists as Experts: A Distinct Role?Torbjørn Gundersen - 2018 - Studies in History and Philosophy of Science Part A 69:52-59.
Overcoming Expert Disagreement In A Delphi Process. An Exercise In Reverse Epistemology.Elisabetta Lalumera - 2015 - Humana Mente 8 (28).
Scientific Consensus and Expert Testimony in Courts: Lessons From the Bendectin Litigation.Boaz Miller - 2016 - Foundations of Science 21 (1):15-33.
Divergent Perspectives on Expert Disagreement: Preliminary Evidence From Climate Science, Climate Policy, Astrophysics, and Public Opinion.James R. Beebe, Maria Baghramian, Luke Drury & Finnur Dellsén - 2019 - Environmental Communication 13:35-50.
Analytics
Added to PP index
2020-11-02
Total views
42 ( #267,480 of 2,498,296 )
Recent downloads (6 months)
7 ( #102,413 of 2,498,296 )
2020-11-02
Total views
42 ( #267,480 of 2,498,296 )
Recent downloads (6 months)
7 ( #102,413 of 2,498,296 )
How can I increase my downloads?
Downloads