Scientific uncertainty and decision making

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 uncertaint

Can free evidence be bad? Value of informationfor the imprecise probabilist

This paper considers a puzzling conflict between two positions that are each compelling: it is irrational for an agent to pay to avoid `free' evidence before making a decision, and rational agents may have imprecise beliefs and/or desires. Indeed, we show that Good's theorem concerning the invariable choice-worthiness of free evidence does not generalise to the imprecise realm, given the plausible existing decision theories for handling imprecision. A key ingredient in the analysis, and a potential source of controversy, is the general approach taken for resolving sequential decision problems { we make explicit what the key alternatives are and defend our own approach. Furthermore, we endorse a resolution of the aforementioned puzzle { we privilege decision theories that merely permit avoiding free evidence over decision theories for which avoiding free evidence is uniquely admissible. Finally, we situate this particular result about free evidence within the broader `dynamic-coherence' literature

Models on the Move: Migration and Imperialism

We introduce `model migration' as a species of cross-disciplinary knowledge transfer whereby the representational function of a model is radically changed to allow application to a new disciplinary context. Controversies and confusions that often derive from this phenomenon will be illustrated in the context of econophysics and phylogeographic linguistics. Migration can be usefully contrasted with concept of `imperialism', that has been influentially discussed in the context of geographical economics. In particular, imperialism, unlike migration, relies upon extension of the original model via an expansion of the domain of phenomena it is taken to adequately described. The success of imperialism thus requires expansion of the justificatory sanctioning of the original idealising assumptions to a new disciplinary context. Contrastingly, successful migration involves the radical representational re-interpretation of the original model, rather than its extension. Migration thus requires `re-sanctioning' of new `counterpart idealisations' to allow application to an entirely different class of phenomena. Whereas legitimate scientific imperialism should be based on the pursuit of some form of ontological unification, no such requirement is need to legitimate the practice of model migration. The distinction between migration and imperialism will thus be shown to have significant normative as well as descriptive value

Models on the Move: Migration and Imperialism

We introduce `model migration' as a species of cross-disciplinary knowledge transfer whereby the representational function of a model is radically changed to allow application to a new disciplinary context. Controversies and confusions that often derive from this phenomenon will be illustrated in the context of econophysics and phylogeographic linguistics. Migration can be usefully contrasted with concept of `imperialism', that has been influentially discussed in the context of geographical economics. In particular, imperialism, unlike migration, relies upon extension of the original model via an expansion of the domain of phenomena it is taken to adequately described. The success of imperialism thus requires expansion of the justificatory sanctioning of the original idealising assumptions to a new disciplinary context. Contrastingly, successful migration involves the radical representational re-interpretation of the original model, rather than its extension. Migration thus requires `re-sanctioning' of new `counterpart idealisations' to allow application to an entirely different class of phenomena. Whereas legitimate scientific imperialism should be based on the pursuit of some form of ontological unification, no such requirement is need to legitimate the practice of model migration. The distinction between migration and imperialism will thus be shown to have significant normative as well as descriptive value

Pick the Sugar

This paper presents a decision problem called the holiday puzzle. The decision problem is one that involves incommensurable goods and sequences of choices. This puzzle points to a tension between three prima facie plausible, but jointly incompatible claims. I present a way out of the trilemma which demonstrates that it is possible for agents to have incomplete preferences and to be dynamically rational. The solution also suggests that the relationship between preference and rational permission is more subtle than standardly assumed

How to be an imprecise impermissivist

Rational credence should be coherent in the sense that your attitudes should not leave you open to a sure loss. Rational credence should be such that you can learn when confronted with relevant evidence. Rational credence should not be sensitive to irrelevant differences in the presentation of the epistemic situation. We explore the extent to which orthodox probabilistic approaches to rational credence can satisfy these three desiderata and find them wanting. We demonstrate that an imprecise probability approach does better. Along the way we shall demonstrate that the problem of “belief inertia” is not an issue for a large class of IP credences, and provide a solution to van Fraassen’s box factory puzzle

Modelling Inequality

Econophysics is a new and exciting cross-disciplinary research field that applies models and modelling techniques from statistical physics to economic systems. It is not, however, without its critics: prominent figures in more mainstream economic theory have criticised some elements of the methodology of econophysics. One of the main lines of criticism concerns the nature of the modelling assumptions and idealisations involved, and a particular target are 'kinetic exchange' approaches used to model the emergence of inequality within the distribution of individual monetary income. This paper will consider such models in detail, and assess the warrant of the criticisms drawing upon the philosophical literature on modelling and idealisation. Our aim is to provide the first steps towards informed mediation of this important and interesting interdisciplinary debate, and our hope is to offer guidance with regard to both the practice of modelling inequality, and the inequality of modelling practice

Laplace’s Demon and the Adventures of His Apprentices

The sensitive dependence on initial conditions (SDIC) associated with nonlinear models imposes limitations on the models’ predictive power. We draw attention to an additional limitation than has been underappreciated, namely, structural model error (SME). A model has SME if the model dynamics differ from the dynamics in the target system. If a nonlinear model has only the slightest SME, then its ability to generate decision-relevant predictions is compromised. Given a perfect model, we can take the effects of SDIC into account by substituting probabilistic predictions for point predictions. This route is foreclosed in the case of SME, which puts us in a worse epistemic situation than SDIC

Assessing the quality of regional climate information

There are now a plethora of data, models, and approaches available to produce regional and local climate information intended to inform adaptation to a changing climate. There is, however, no framework to assess the quality of these data, models, and approaches that takes into account the issues that arise when this information is produced. An evaluation of the quality of regional climate information is a fundamental requirement for its appropriate application in societal decision-making. Here, an analytical framework is constructed for the quality assessment of science-based statements and estimates about future climate. This framework targets statements that project local and regional climate at decadal and longer time scales. After identifying the main issues with evaluating and presenting regional climate information, it is argued that it is helpful to consider the quality of statements about future climate in terms of 1) the type of evidence and 2) the relationship between the evidence and the statement. This distinction not only provides a more targeted framework for quality, but also shows how certain evidential standards can change as a function of the statement under consideration. The key dimensions to assess regional climate information quality are diversity, completeness, theory, adequacy for purpose, and transparency. This framework is exemplified using two research papers that provide regional climate information and the implications of the framework are explored

Assessing the quality of state-of-the-art regional climate information: the case of the UK Climate Projections 2018

In this paper, we assess the quality of state-of-the-art regional climate information intended to support climate adaptation decision-making. We use the UK Climate Projections 2018 as an example of such information. Their probabilistic, global, and regional land projections exemplify some of the key methodologies that are at the forefront of constructing regional climate information for decision support in adapting to a changing climate. We assess the quality of the evidence and the methodology used to support their statements about future regional climate along six quality dimensions: transparency; theory; independence, number, and comprehensiveness of evidence; and historical empirical adequacy. The assessment produced two major insights. First, a major issue that taints the quality of UKCP18 is the lack of transparency, which is particularly problematic since the information is directed towards non-expert users who would need to develop technical skills to evaluate the quality and epistemic reliability of this information. Second, the probabilistic projections are of lower quality than the global projections because the former lack both transparency and a theory underpinning the method used to produce quantified uncertainty estimates about future climate. The assessment also shows how different dimensions are satisfied depending on the evidence used, the methodology chosen to analyze the evidence, and the type of statements that are constructed in the different strands of UKCP18. This research highlights the importance of knowledge quality assessment of regional climate information that intends to support climate change adaptation decisions