The objectives of this project are:
1. To examine policy decision making under conditions of severe uncertainty: situations in which we lack complete information about the probabilities of possible future states of the world, about what actions will be available and what their outcomes will be, and about the desirability of these outcomes.
2. To study scientific models that are both imperfect and non-linear, especially those of the climate and of climate change, with a view to:
(i) understanding what limits they imply on our ability to generate forecasts that can be used by policy-makers; and
(ii) investigating ways in which such models can be fashioned to provide policy-relevant information.
3. To study the implications for climate policy-making of the inherent limitations we face in making predictions about relevant climate variables, in relation both to our ability to assess the impact of possible interventions and to our ethical assessment of them, and to propose techniques for dealing with these limitations.
4. To develop philosophical expertise in the field of decision-making under severe uncertainty by providing doctoral training and supervision.
The project was initiated with a large AHRC grant (running from March 2013 to March 2016) and now carries on with funding from CPNSS. It is carried out in collaboration with economists and climate scientists in other departments at LSE (and elsewhere).
(a) Research Papers
- Helgeson, Casey, Richard Bradley and Brian Hill (2018), "Combining Probability with Qualitative Degree-of-Certainty Assessment", Climatic Change 149 (3-4): 517-525
- Helgeson, Casey (2018) "Structuring Decisions Under Deep Uncertainty", Topoi (forthcoming)
- Bradley, Richard (2017), Decision Theory with a Human Face, Cambridge University Press.
- Bradley, Richard, Casey Helgeson and Brian Hill (2017), "Climate Change Assessments: Confidence, Probability and Decision", Philosophy of Science 84(3), 500-522.
- Thompson, Erica, Roman Frigg and Casey Helgeson (2016), "Expert Judgment for Climate Change Adaptation", Philosophy of Science 83(5), 1110-1121.
- Frigg, Roman, David A. Stainforth and Leonard A. Smith (2015), "An Assessment of the Foundational Assumptions in High-Resolution Climate Projections: The Case of UKCP09", Synthese 192(12), 3979–4008.
- Frigg, Roman, Seamus Bradley, Hailiang Du and Leonard A. Smith, (2014), "Laplace’s Demon and the Adventures of His Apprentices", Philosophy of Science 81(1), 31–59.
(b) Surveys and Introductions
- Bradley, Richard and Katie Steele (2015), “Making Climate Decisions”, Philosophy Compass10/11, 799–810.
- Frigg, Roman, Erica Thompson and Charlotte Werndl (2015), “Philosophy of Climate Science Part I: Observing Climate Change”, Philosophy Compass 10(12), 953-964.
- — (2015) “Philosophy of Climate Science Part II: Modelling Climate Change”, Philosophy Compass 10(12), 965-977.
- ‘Objective Counterfactual Analysis: e-evaluating risk from North Atlantic Hurricanes’, Axa Research. Tom Philp and Adrian Champion (PIs), Michael Maran, Kevin Hodges, Roman Frigg, Richard Bradley (CIs) (£30k), March 2019-February 2021.
- NERC-AHRC-ESRC Research grant (NE/P016367/1) for Tsunami Risk for the Western Indian Ocean: Steps towards the Integration of Science into Policy and Practice, Jan –July 2017.
- EPSRC network grant for M2D: Models to Decisions (participating institution), since 2017.
- EPSRC network grant for CRUISSE: Challenging Radical Uncertainty in Science, Society and the Environment (participating institution), since 2017.
- AHRC grant ‘Managing Severe Uncertainty’ (AH/J006033/1), Richard Bradley (PI), Roman Frigg, Katie Steele, Alex Voorhoeve and Charlotte Werndl (£725,000).
- Panel on “Challenges in Trust in Models and Decision Making”, M2D second annual conference on Decision Making Under Uncertainty, Newton Institute, Cambridge, 11–14 June 2018
- Panel on “The Role of Philosophy and Social Sciences in Models and Decision Making”, M2D first annual conference on Decision Making Under Uncertainty, Exeter, 11–14 July 2017
- AXA-XLCatlin: Working on the use of catastrophe models to support insurance pricing.