Scientific uncertainty and decision making

Dissertation, London School of Economics (2012)
  Copy   BIBTEX

Abstract

It is important to have an adequate model of uncertainty, since decisions must be made before the uncertainty can be resolved. For instance, flood defenses must be designed before we know the future distribution of flood events. It is standardly assumed that probability theory offers the best model of uncertain information. I think there are reasons to be sceptical of this claim. I criticise some arguments for the claim that probability theory is the only adequate model of uncertainty. In particular I critique Dutch book arguments, representation theorems, and accuracy based arguments. Then I put forward my preferred model: imprecise probabilities. These are sets of probability measures. I offer several motivations for this model of uncertain belief, and suggest a number of interpretations of the framework. I also defend the model against some criticisms, including the so-called problem of dilation. I apply this framework to decision problems in the abstract. I discuss some decision rules from the literature including Levi’s E-admissibility and the more permissive rule favoured by Walley, among others. I then point towards some applications to climate decisions. My conclusions are largely negative: decision making under such severe uncertainty is inevitably difficult. I finish with a case study of scientific uncertainty. Climate modellers attempt to offer probabilistic forecasts of future climate change. There is reason to be sceptical that the model probabilities offered really do reflect the chances of future climate change, at least at regional scales and long lead times. Indeed, scientific uncertainty is multi-dimensional, and difficult to quantify. I argue that probability theory is not an adequate representation of the kinds of severe uncertainty that arise in some areas in science. I claim that this requires that we look for a better framework for modelling uncertainty.

Links

PhilArchive



    Upload a copy of this work     Papers currently archived: 92,991

External links

Setup an account with your affiliations in order to access resources via your University's proxy server

Through your library

Similar books and articles

The Irrelevance of the Risk-Uncertainty Distinction.Dominic Roser - 2017 - Science and Engineering Ethics 23 (5):1387-1407.
Confidence in Probabilistic Risk Assessment.Luca Zanetti - forthcoming - Philosophy of Science:1-19.
Meta-Reasoning in Making Moral Decisions Under Normative Uncertainty.Tomasz Żuradzki - 2016 - In Dima Mohammed & Marcin Lewiński (eds.), Argumentation and Reasoned Action. College Publications. pp. 1093-1104.

Analytics

Added to PP
2021-09-27

Downloads
11 (#1,165,599)

6 months
2 (#1,259,303)

Historical graph of downloads
How can I increase my downloads?

Author's Profile

Seamus Bradley
London School of Economics (PhD)

Citations of this work

No citations found.

Add more citations

References found in this work

Truth and probability.Frank Ramsey - 2010 - In Antony Eagle (ed.), Philosophy of Probability: Contemporary Readings. New York: Routledge. pp. 52-94.
Humean Supervenience Debugged.David Lewis - 1994 - Mind 103 (412):473--490.
A nonpragmatic vindication of probabilism.James M. Joyce - 1998 - Philosophy of Science 65 (4):575-603.
Evidential Symmetry and Mushy Credence.Roger White - 2009 - Oxford Studies in Epistemology 3:161-186.

View all 66 references / Add more references