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  1. General-Purpose Institutional Decision-Making Heuristics: The Case of Decision-Making under Deep Uncertainty.David Thorstad - forthcoming - British Journal for the Philosophy of Science.
    Recent work in judgment and decisionmaking has stressed that institutions, like individuals, often rely on decisionmaking heuristics. But most of the institutional decisionmaking heuristics studied to date are highly firm- and industry-specific. This contrasts to the individual case, in which many heuristics are general-purpose rules suitable for a wide range of decision problems. Are there also general-purpose heuristics for institutional decisionmaking? In this paper, I argue that a number of methods recently developed for decisionmaking under deep uncertainty have a good (...)
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  • Making Confident Decisions with Model Ensembles.Joe Roussos, Richard Bradley & Roman Frigg - 2021 - Philosophy of Science 88 (3):439-460.
    Many policy decisions take input from collections of scientific models. Such decisions face significant and often poorly understood uncertainty. We rework the so-called confidence approach to tackle decision-making under severe uncertainty with multiple models, and we illustrate the approach with a case study: insurance pricing using hurricane models. The confidence approach has important consequences for this case and offers a powerful framework for a wide class of problems. We end by discussing different ways in which model ensembles can feed information (...)
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  • Tough enough? Robust satisficing as a decision norm for long-term policy analysis.Andreas L. Mogensen & David Thorstad - 2022 - Synthese 200 (1):1-26.
    This paper aims to open a dialogue between philosophers working in decision theory and operations researchers and engineers working on decision-making under deep uncertainty. Specifically, we assess the recommendation to follow a norm of robust satisficing when making decisions under deep uncertainty in the context of decision analyses that rely on the tools of Robust Decision-Making developed by Robert Lempert and colleagues at RAND. We discuss two challenges for robust satisficing: whether the norm might derive its plausibility from an implicit (...)
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  • Interpreting the Probabilistic Language in IPCC Reports.Corey Dethier - 2023 - Ergo: An Open Access Journal of Philosophy 10.
    The Intergovernmental Panel on Climate Change (IPCC) often qualifies its statements by use of probabilistic “likelihood” language. In this paper, I show that this language is not properly interpreted in either frequentist or Bayesian terms—simply put, the IPCC uses both kinds of statistics to calculate these likelihoods. I then offer a deflationist interpretation: the probabilistic language expresses nothing more than how compatible the evidence is with the given hypothesis according to some method that generates normalized scores. I end by drawing (...)
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  • Conceptualizing uncertainty: the IPCC, model robustness and the weight of evidence.Margherita Harris - 2021 - Dissertation, London School of Economics
    The aim of this thesis is to improve our understanding of how to assess and communicate uncertainty in areas of research deeply afflicted by it, the assessment and communication of which are made more fraught still by the studies’ immediate policy implications. The IPCC is my case study throughout the thesis, which consists of three parts. In Part 1, I offer a thorough diagnosis of conceptual problems faced by the IPCC uncertainty framework. The main problem I discuss is the persistent (...)
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