The value-free ideal in science has been criticised as both unattainable and undesirable. We argue that it can be defended as a practical principle guiding scientific research even if the unattainability and undesirability of a value-free end-state are granted. If a goal is unattainable, then one can separate the desirability of accomplishing the goal from the desirability of pursuing it. We articulate a novel value-free ideal, which holds that scientists should act as if science should be value-free, and we argue (...) that even if a purely value-free science is undesirable, this value-free ideal is desirable to pursue. (shrink)

The value-free ideal for science holds that values should not influence the core features of scientific reasoning. We defend the difference-to-inference model of value-permeation, which holds that value-permeation in science is problematic when values make a difference to the inferences made about a hypothesis. This view of value-permeation is superior to existing views, and it suggests a corresponding maxim—namely, that scientists should strive to eliminate differences to inference. This maxim is the basis of a novel value-free ideal for science. -/- (...) . (shrink)

Philosophers of science, particularly those working on science and values, often talk about the need for science to be socially responsible. However, what this means is not clear. In this paper, we review the contributions of philosophers of science to the debate over socially responsible science and explore the dimensions that a fruitful account of socially responsible science should address. Our review shows that offering a comprehensive account is difficult. We contend that broad calls for socially responsible science that fail (...) to attend to relevant dimensions are not the solution, as they preclude meaningful changes to research institutions and practices. We conclude that narrower, more explicit accounts are more likely to lead to substantive transformation. (shrink)

In the last two decades, a robust consensus has emerged among philosophers of science, whereby political, ethical, or social values must play some role in scientific inquiry, and that the ‘value-free ideal’ is thus a misguided conception of science. However, the question of how to distinguish, in a principled way, which values may legitimately influence science remains. This question, which has been dubbed the ‘new demarcation problem,’ has until recently received comparatively less attention from philosophers of science. In this paper, (...) I appeal to Rawls’s theory of justice (1971) on the basis of which I defend a Rawlsian solution to the new demarcation problem. As I argue, the Rawlsian solution places plausible constraints on which values ought to influence scientific inquiry, and, moreover, can be fruitfully applied to concrete cases to determine how the conflicting interests of stakeholders should be balanced. After considering and responding to the objection that Rawls’s theory of justice applies only to the “basic structure” of society, I compare the Rawlsian solution to some other approaches to the new demarcation problem, especially those that emphasize democratic criteria. (shrink)

We uncover a largely unnoticed and unaddressed problem in conservation research: arguments built within studies are sometimes defective in more fundamental and specific ways than appreciated, because they misrelate values and empirical matters. We call this the unraveled rope problem because just as strands of rope must be properly and intricately wound with each other so the rope supports its load, empirical aspects and value aspects of an argument must be related intricately and properly if the argument is to objectively (...) support its conclusion. By characterizing this problem with precision, our study differs from but complements existing studies of value issues in conservation science, in two ways. First, it focuses on key relationships between empirical issues and value issues, relations that have sometimes been obscured by focusing on these issues more independently of each other. Second, it focuses on these relationships within arguments and deploys a method of argument analysis and evaluation honed in other fields but under-utilized in conservation science. By combining our study's novel features, we detail six families of argument defect that exemplify the unraveled rope problem within existing literature. These defects sometimes manifest within basic research stages, rather than just in more applied downstream stages where roles for values are already obvious to many. As scientific reasoning and communication become increasingly important for addressing society's greatest environmental challenges, overcoming the unraveled rope problem will be essential to the success and integrity of conservation research. Therefore, we also outline potential solutions, preventative measures, and useful further work for conservation researchers. (shrink)

There is much debate on how social values should influence scientific research. However, the question of practical applicability of philosophers’ normative proposals has received less attention. Here, we test the attainability of Matthew J. Brown’s (2020) Moral Imagination ideal (MI ideal), which aims to help scientists to make warranted value-judgements through reflecting on goals, options, values, and stakeholders of research. Here, the tools of the MI ideal are applied to a climate modelling setting, where researchers are developing aerosol-cloud interaction (ACI) (...) parametrizations in an Earth System Model with the broader goal of improving climate sensitivity estimation. After the identification of minor obstacles to applying the MI ideal, we propose two ways to increase its applicability. First, its tools should be accompanied with more concrete guidance for identifying how social values enter more technical decisions in scientific research. Second, since research projects can have multiple goals, examining the alignment between broader societal aims of research and more technical goals should be part of the tools of the MI ideal. (shrink)

The argument from inductive risk is considered to be one of the strongest challenges for value-free science. A great part of its appeal lies in the idea that even an ideal epistemic agent—the “perfect scientist” or “scientist qua scientist”—cannot escape inductive risk. In this paper, I scrutinize this ambition by stipulating an idealized Bayesian decision setting. I argue that inductive risk does not show that the “perfect scientist” must, descriptively speaking, make non-epistemic value-judgements, at least not in a way that (...) undermines the value-free ideal. However, the argument is more successful in showing that there are cases where the “perfect scientist” should, normatively speaking, use non-epistemic values. I also show that this is possible without creating problems of illegitimate prescription and wishful thinking. Thus, while inductive risk does not refute value-freedom completely, it still represents a powerful critique of value-free science. (shrink)

I examine the construction and evaluation of machine learning binary classification models. These models are increasingly used for societal applications such as classifying patients into two categories according to the presence or absence of a certain disease like cancer and heart disease. I argue that the construction of ML classification models involves an optimisation process aiming at the minimization of the inductive risk associated with the intended uses of these models. I also argue that the construction of these models is (...) underdetermined by the available data, and that this makes it necessary for ML modellers to make social value judgments in determining the error costs used in ML optimization. I thus suggest that the assessment of the inductive risk with respect to the social values of the intended users is an integral part of the construction and evaluation of ML classification models. I also discuss the implications of this conclusion for the philosophical debate concerning inductive risk. (shrink)

Philosophical work on values in science is held back by widespread ambiguity about how values bear on sci entific choices. Here, I disambiguate several ways in which a choice can be value-laden and show that this disambiguation has the potential to solve and dissolve philosophical problems about values in science. First, I characterize four ways in which values relate to choices: values can motivate, justify, cause, or be impacted by the choices we make. Next, I put my proposed taxonomy to (...) work, using it to clarify one version of the argument from inductive risk. The claim that non-epistemic values must play a role in scientific choices that run inductive risk makes most sense as a claim about values being needed to justify such choices. The argument from inductive risk is not unique: many philosophical arguments about values in science can be more clearly understood and assessed by paying close attention to how values and choices are related. (shrink)

Projections of future climate change cannot rely on a single model. It has become common to rely on multiple simulations generated by Multi-Model Ensembles (MMEs), especially to quantify the uncertainty about what would constitute an adequate model structure. But, as Parker points out (2018), one of the remaining philosophically interesting questions is: “How can ensemble studies be designed so that they probe uncertainty in desired ways?” This paper offers two interpretations of what General Circulation Models (GCMs) are and how MMEs (...) made of GCMs should be designed. In the first interpretation, models are combinations of modules and parameterisations; an MME is obtained by “plugging and playing” with interchangeable modules and parameterisations. In the second interpretation, models are aggregations of expert judgements that result from a history of epistemic decisions made by scientists about the choice of representations; an MME is a sampling of expert judgements from modelling teams. We argue that, while the two interpretations involve distinct domains from philosophy of science and social epistemology, they both could be used in a complementary manner in order to explore ways of designing better MMEs. (shrink)

Simple idealized models seem to provide more understanding than opaque, complex, and hyper-realistic models. However, an increasing number of scientists are going in the opposite direction by utilizing opaque machine learning models to make predictions and draw inferences, suggesting that scientists are opting for models that have less potential for understanding. Are scientists trading understanding for some other epistemic or pragmatic good when they choose a machine learning model? Or are the assumptions behind why minimal models provide understanding misguided? In (...) this paper, using the case of deep neural networks, I argue that it is not the complexity or black box nature of a model that limits how much understanding the model provides. Instead, it is a lack of scientific and empirical evidence supporting the link that connects a model to the target phenomenon that primarily prohibits understanding. (shrink)

For computer simulation models to usefully inform climate risk management, uncertainties in model projections must be explored and characterized. Because doing so requires running the model many ti...

There is a growing consensus among philosophers of science that core parts of the scientific process involve non-epistemic values. This undermines the traditional foundation for public trust in science. In this article I consider two proposals for justifying public trust in value-laden science. According to the first, scientists can promote trust by being transparent about their value choices. On the second, trust requires that the values of a scientist align with the values of an individual member of the public. I (...) argue that neither of these proposals work and suggest an alternative that does better. When scientists must appeal to values in the course of their research, they should appeal to democratic values: the values of the public or its representatives. (shrink)

Kevin Elliott and others separate two common arguments for the legitimacy of societal values in scientific reasoning as the gap and the error arguments. This article poses two questions: How are these two arguments related, and what can we learn from their interrelation? I contend that we can better understand the error argument as nested within the gap because the error is a limited case of the gap with narrower features. Furthermore, this nestedness provides philosophers with conceptual tools for analyzing (...) more robustly how values pervade science. (shrink)

The argument from inductive risk has been embraced by many as a successful account of the role of values in science that challenges the value-free ideal. We argue that it is not obvious that the argument from inductive risk actually undermines the value-free ideal. This is because the inductive risk argument endorses an assumption held by proponents of the value-free ideal: that contextual values never play an appropriate role in determining evidence. We show that challenging the value-free ideal ultimately requires (...) rejecting this assumption. (shrink)

The aim of this article is to argue that ontological choices in scientific practice undermine common formulations of the value-free ideal in science. First, I argue that the truth values of scientific statements depend on ontological choices. For example, statements about entities such as species, race, memory, intelligence, depression, or obesity are true or false relative to the choice of a biological, psychological, or medical ontology. Second, I show that ontological choices often depend on non-epistemic values. On the basis of (...) these premises, I argue that it is often neither possible nor desirable to evaluate scientific statements independently of non-epistemic values. Finally, I suggest that considerations of ontological choices do not only challenge the value-free ideal but also help to specify positive roles of non-epistemic values in an often neglected area of scientific practice. (shrink)

Recent work by Frigg et. al. and Mayo-Wilson have called attention to a particular sort of error associated with attempts to model certain complex systems: structural modeling error. The assessment of the degree of SME in a model presupposes agreement between modelers about the best way to individuate natural systems, an agreement which can be more problematic than it appears. This problem, which we dub “the system individuation problem” arises in many of the same contexts as SME, and the two (...) often compound one another. This paper explores the common roots of the two problems in concerns about the precision of predictions generated by scientific models, and discusses how both concerns bear on the study of complex natural systems, particularly the global climate. (shrink)

The aim of this article is to argue that ontological choices in scientific practice undermine common formulations of the value-free ideal in science. First, I argue that the truth values of scientific statements depend on ontological choices. For example, statements about entities such as species, race, memory, intelligence, depression, or obesity are true or false relative to the choice of a biological, psychological, or medical ontology. Second, I show that ontological choices often depend on non-epistemic values. On the basis of (...) these premises, I argue that it is often neither possible nor desirable to evaluate scientific statements independently of non-epistemic values. Finally, I suggest that considerations of ontological choices do not only challenge the value-free ideal but also help to specify positive roles of non-epistemic values in an often neglected area of scientific practice. (shrink)

Ghostwriting scandals are pervasive in industry-funded biomedical research, and most responses to them have presumed that they represent a sharp transgression of the norms of scientific authorship. I argue that in fact, ghostwriting represents a continuous extension of current socially accepted authorship practices. I claim that the radically collaborative, decentralized, interdisciplinary research that forms the gold standard in medicine is in an important sense unauthored, and that this poses a serious problem in applied social epistemology. It is no easy matter (...) to find procedural or architectural solutions that can secure epistemic trust and accountability for collaborative publishing in biomedicine. (shrink)

This paper discusses how to deal with research that threatens to cause harm to society—in particular, whether and in what cases bans and moratoria are appropriate. First, it asks what normative resources philosophy of science may draw on to answer such questions. In an effort to presuppose only resources acknowledgeable across different comprehensive worldviews, it is claimed that the aim of credibility provides a good basis for normative reflection. A close analysis reveals an inner tension inherent in the pursuit of (...) credibility, referred to as the paradox of credibility: Although the aim of credibility involves the goal of being trusted, the immediate pursuit of the goal of being trusted as much as possible can run counter to and undermine the pursuit of credibility. From this inner tension, considerations are derived on what it means to uncompromisingly strive for trustworthiness. When applied to the problem of harmful research, it becomes apparent that it is important to distinguish between different types of cases. Some cases allow science to prevent harm while relinquishing credibility in the associated research area. In contrast, other cases demand preserving subject-specific scientific credibility in order to manage potential harm. (shrink)

Scientific models can be performative: they can causally affect the phenomena they are intended to represent. The existing literature offers two responses. The appraisal view emphasizes that performativity can sometimes be a good-making model attribute, e.g., when predictions steer the public’s behavior in desirable ways. The mitigation view seeks to endogenize agents’ behavioral response to model-issued forecasts to get rid of performativity instead. This paper argues that neither approach is fully compelling: the appraisal view encounters severe concerns about moral values (...) illegitimately encroaching on how modelers construct and use models, while the mitigation view fails to acknowledge that endogenization is itself a choice that involves substantive value-judgments relating to the desirability of certain social outcomes. (shrink)

Climatologists have recently introduced a distinction between projections as scenario-based model results on the one hand and predictions on the other hand. The interpretation and usage of both terms is, however, not univocal. It is stated that the ambiguities of the interpretations may cause problems in the communication of climate science within the scientific community and to the public realm. This paper suggests an account of scenarios as props in games of make-belive. With this account, we explain the difference between (...) projections that should be make-believed and other model results that should be believed. (shrink)

The assessments issued by the Intergovernmental Panel on Climate Change (IPCC) aim to provide policy-makers with an objective source of information about the various causes of climate change, the projected consequences for the environment and human affairs, and the options for adaptation and mitigation. But what, in this context, is meant by ‘objective’? In practice, in an effort to address internal and external criticisms, the IPCC has regularly revised its methodological procedures; some of these procedures seem to meet the requirements (...) of objectivity, at least as understood in a specific sense, but the relationship between objectivity and value-neutrality requires further investigation. The aim of this paper is to offer an appropriate philosophical account of objectivity, reconcilable with the fact that the IPCC is not value-free. I argue that Sandra Harding’s notion of strong objectivity is particularly well suited to this goal, and I examine the extent to which the current IPCC procedures match her account. (shrink)

Recent rival attempts in the philosophy of science to put forward a general theory of the properties that all (and only) natural kinds across the sciences possess may have proven to be futile. Instead, I develop a general methodological framework for how to philosophically study kinds. Any kind has to be investigated and articulated together with the human aims that motivate referring to this kind, where different kinds in the same scientific domain can answer to different concrete aims. My core (...) contention is that non-epistemic aims, including environmental, ethical, and political aims, matter as well. This is defended and illustrated based on several examples of kinds, with particular attention to the role of social-political aims: species, race, gender, as well as personality disorders and oppositional defiant disorder as psychiatric kinds. Such non-epistemic aims and values need not always be those personally favoured by scientists, but may have to reflect values that matter to relevant societal stakeholders. Despite the general agenda to study ‘kinds,’ I argue that philosophers should stop using the term ‘natural kinds,’ as this label obscures the relevance of humans interests and the way in which many kinds are based on contingent social processes subject to human responsibility. (shrink)

We call attention to an underappreciated way in which non-epistemic values influence evidence evaluation in science. Our argument draws upon some well-known features of scientific modeling. We show that, when scientific models stand in for background knowledge in Bayesian and other probabilistic methods for evidence evaluation, conclusions can be influenced by the non-epistemic values that shaped the setting of priorities in model development. Moreover, it is often infeasible to correct for this influence. We further suggest that, while this value influence (...) is not particularly prone to the problem of wishful thinking, it could have problematic non-epistemic consequences in some cases. (shrink)

This article critically examines a recent philosophical debate on the role of values in climate change forecasts, such as those found in assessment reports of the Intergovernmental Panel on Climate Change. On one side, several philosophers insist that the argument from inductive risk, as developed by Rudner and Douglas among others, applies to this case. AIR aims to show that ethical value judgments should influence decisions about what is sufficient evidence for accepting scientific hypotheses that have implications for policy issues. (...) Advocates of extending AIR to climate science claim that values are deeply... (shrink)

While it is widely acknowledged that science is not “free” of non-epistemic values, there is disagreement about the roles that values can appropriately play. Several have argued that non-epistemic values can play important roles in modeling decisions, particularly in addressing uncertainties ; Risbey 2007; Biddle and Winsberg 2010; Winsberg : 111-137, 2012); van der Sluijs 359-389, 2012). On the other hand, such values can lead to bias ; Bray ; Oreskes and Conway 2010). Thus, it is important to identify when (...) it is legitimate to appeal to non-epistemic values in modeling decisions. An approach is defended here whereby such value judgments are legitimate when they promote democratically endorsed epistemological and social aims of research. This framework accounts for why it is legitimate to appeal to non-epistemic values in a range of modeling decisions, while addressing concerns that the presence of such values will lead to bias or give scientists disproportionate power in deciding what values ought to be endorsed. (shrink)

This paper discusses a crisis of accountability that arises when scientific collaborations are massively epistemically distributed. We argue that social models of epistemic collaboration, which are social analogs to what Patrick Suppes called a “model of the experiment,” must play a role in creating accountability in these contexts. We also argue that these social models must accommodate the fact that the various agents in a collaborative project often have ineliminable, messy, and conflicting interests and values; any story about accountability in (...) a massively distributed collaboration must therefore involve models of such interests and values and their methodological and epistemic effects. (shrink)

Non-epistemic values pervade climate modelling, as is now well documented and widely discussed in the philosophy of climate science. Recently, Parker and Winsberg have drawn attention to what can be termed “epistemic inequality”: this is the risk that climate models might more accurately represent the future climates of the geographical regions prioritised by the values of the modellers. In this paper, we promote value management as a way of overcoming epistemic inequality. We argue that value management can be seriously considered (...) as soon as the value-free ideal and inductive risk arguments commonly used to frame the discussions of value influence in climate science are replaced by alternative social accounts of objectivity. We consider objectivity in Longino's sense as well as strong objectivity in Harding's sense to be relevant options here, because they offer concrete proposals that can guide scientific practice in evaluating and designing so-called multi-model ensembles and, in fine, improve their capacity to quantify and express uncertainty in climate projections. (shrink)

Many philosophers have challenged the ideal of value-free science on the grounds that social or moral values are relevant to inferential thresholds. But given this view, how precisely and to what extent should scientists adjust their inferential thresholds in light of nonepistemic values? We suggest that signal detection theory provides a useful framework for addressing this question. Moreover, this approach opens up further avenues for philosophical inquiry and has important implications for philosophical debates concerning inductive risk. For example, the signal (...) detection theory framework entails that considerations of inductive risk and inferential-threshold placement cannot be conducted in isolation from base-rate information. (shrink)

Inmaculada de Melo-Martín and Kristen Intemann consider whether, from the perspective of the argument from inductive risk, ethical and political values might be logically, epistemically, pragmatically, or ethically necessary in the “core” of scientific reasoning. In each case, they argue that there are significant conceptual problems. In this comment, employing regulatory uses of high-throughput toxicology at the US Environmental Protection Agency as a case study, I respond to some of their claims about the notion of “pragmatic necessity.” I conclude that, (...) while an inductive risk framework has some significant limitations, it is still conceptually and rhetorically valuable. (shrink)

We give an overview of the practice of developing and using complex climate models, as seen from experiences in a major climate modelling center and through participation in the Coupled Model Intercomparison Project. We discuss the construction and calibration of models; their evaluation, especially through use of out-of-sample tests; and their exploitation in multi-model ensembles to identify biases and make predictions. We stress that adequacy or utility of climate models is best assessed via their skill against more naïve predictions. The (...) framework we use for making inferences about reality using simulations is naturally Bayesian, and has many points of contact with more familiar examples of scientific epistemology. While the use of complex simulations in science is a development that changes much in how science is done in practice, we argue that the concepts being applied fit very much into traditional practices of the scientific method, albeit those more often associated with laboratory work. (shrink)

Current debates over inductive risk and the role of values in science have largely revolved around the question of the moral responsibilities of scientists: Do scientists have the duty to consider the potential non-epistemic consequences of theories they advocate and, if yes, what values should be taken into account in decision-making? The paper discusses two different – though potentially complementary – responses to this question: a) H. Douglas’s view that scientists should avoid causing reckless or negligent harm to others as (...) a result of the decisions they make and b) K. Elliott’s Multiple Goals Criterion. Drawing from the case of potential coronavirus transmission by sharing the Holy Communion that recently divided Greek society and medical experts, it shows the tensions emerging between autonomy and the moral responsibilities of scientists, when the boundaries of science are blurred and the epistemic goal of truth is inconsistent with alternative goals. It argues that the balance attempted between scientific principles and religious beliefs was unattainable and concludes that the need to distinguish between epistemic and non-epistemic values, which is traditionally related to the ideal of value free science, should be reconsidered and even prioritized among the responsibilities of scientists. (shrink)

What can one say to the self-deceived? And – perhaps more importantly – who can say it? The attribution of self-deception depends heavily on the criteria for what is thought to be beyond dispute. For Galeotti, misperception of reality is a product of psychological and emotional pressure resulting in ‘emotionally overloaded wishes’, and her solution thus involves the construction of what an ‘impartial’ and ‘dispassionate’ observer would conclude when presented with the same evidence. Drawing on her examples of foreign policy (...) decision-making, I discuss two objections. First, I ask whether being ‘dispassionate’ is enough get one off the hook from the sorts of value judgements that must be made in assessing evidence in complex situations. Second, I address the role of disagreement and dissent, and suggest that what is required are not actors with a lack of emotionally overloaded wishes, but actors with different goals and wishes. Thus, while Galeotti emphasizes solutions drawing on ideals of impartiality, we might more productively look for solutions that engage multiple forms of partiality. (shrink)

This paper investigates the case of enzyme classification to evaluate different ideals for regulating values in science. I show that epistemic and non-epistemic considerations are inevitably and untraceably entangled in enzyme classification, and argue that this has significant implications for the two main kinds of views on values in science, namely, Epistemic Priority Views and Joint Satisfaction Views. More precisely, I argue that the case of enzyme classification poses a problem for the usability and descriptive accuracy of these two views. (...) The paper ends by suggesting that these two views provide different but complementary perspectives, and that both are useful for evaluating values in science. (shrink)

Practice-centric and theory-centric approaches in philosophy of science are described and contrasted. The contrast is developed through an examination of their different treatments of the underdetermination problem. The practice-centric approach is illustrated by a summary of comparative research on approaches in the biology of behavior. The practice-centric approach is defended against charges that it encourages skepticism regarding the sciences.

The United Kingdom Climate Impacts Programme’s UKCP09 project makes high-resolution projections of the climate out to 2100 by post-processing the outputs of a large-scale global climate model. The aim of this paper is to describe and analyse the methodology used and then urge some caution. Given the acknowledged systematic, shared errors of all current climate models, treating model outputs as decision-relevant projections can be significantly misleading. In extrapolatory situations, such as projections of future climate change, there is little reason to (...) expect that post-processing of model outputs can correct for the consequences of such errors. This casts doubt on our ability, today, to make trustworthy probabilistic projections at high resolution out to the end of the century. (shrink)

In recent papers and a book, Heather Douglas has expanded on the well-known argument from inductive risk, thereby launching an influential contemporary critique of the value-free ideal for science. This paper distills Douglas’s critique into four major claims. The first three claims provide a significant challenge to the value-free ideal for science. However, the fourth claim, which delineates her positive proposal to regulate values in science by distinguishing direct and indirect roles for values, is ambiguous between two interpretations, and both (...) have weaknesses. Fortunately, two elements of Douglas’s work that have previously received much less emphasis provide resources for developing a more promising approach for regulating values in science. (shrink)

This article examines the relevance of survey data of scientists’ attitudes about science and values to case studies in philosophy of science. We describe two methodological challenges confronting such case studies: 1) small samples, and 2) potential for bias in selection, emphasis, and interpretation. Examples are given to illustrate that these challenges can arise for case studies in the science and values literature. We propose that these challenges can be mitigated through an approach in which case studies and survey methods (...) are viewed as complementary, and use data from the Toolbox Dialogue Initiative to illustrate this claim. (shrink)

When attendant to the agency of models and the general context in which they perform, climate models can be seen as instrumental policy tools that may be evaluated in terms of their adequacy for purpose. In contrast, when analysed independently of their real-world usage for informing decision-making, the tendency can be to prioritise their representative role rather than their instrumental role. This paper takes as a case study the development of the UK Climate Projections 2009 in relation to its probabilistic (...) treatment of uncertainties and the implications of this approach for adaptation decision-making. It is considered that the move towards ensemble-based probabilistic climate projections has the benefit of encouraging organisations to reshape their adaptation strategies and decisions towards a risk-based approach, where they are confronted definitively with climate modelling uncertainties and drawn towards a more nuanced understanding of how climate impacts could affect their operations. This is further illustrated through the example of the built environment sector, where it can be seen that the probabilistic approach may be of limited salience for the urban heat island in the absence of a corresponding effort towards a more place-based analysis of climate vulnerabilities. Therefore, further assessment of the adequacy-for-purpose of climate models might also consider the usability of climate projections at the urban scale. (shrink)

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 (...) ambiguity surrounding the concepts of ‘confidence’ and ‘likelihood’; I argue that the lack of a conceptually valid interpretation of these concepts compatible with the IPCC uncertainty guide’s recommendations has worrying implications for both the IPCC authors’ treatment of uncertainties and the interpretability of the information provided in the AR5. Finally, I show that an understanding of the reasons behind the IPCC’s decision to include two uncertainty scales can offer insights into the nature of this problem. In Part 2, I review what philosophers have said about model-based robustness analysis. I assess several arguments that have been offered for its epistemic import and relate this discussion to the context of climate model ensembles. I also discuss various measures of independence in the climate literature, and assess the extent to which these measures can help evaluate the epistemic import of model robustness. In Part 3, I explore the notion of the ‘weight of evidence’ typically associated with Keynes. I argue that the Bayesian is bound to struggle with this notion, and draw some lessons from this fact. Finally, I critically assess some recent proposals for a new IPCC uncertainty framework that significantly depart from the current one. (shrink)

I address options for providing scientific policy advice and explore the relation between scientific knowledge and political, economic and moral values. I argue that such nonepistemic values are essential for establishing the significance of questions and the relevance of evidence, while, on the other hand, such social choices are the prerogative of society. This tension can be resolved by recognizing social values and identifying them as separate premises or as commissions while withholding commitment to them, and by elaborating a plurality (...) of policy packages that envisage the implementation of different social goals. There are limits to upholding the value-free ideal in scientific research. But by following the mentioned strategy, science can give useful policy advice by leaving the value-free ideal largely intact. Such scientific restraint avoids the risk of appearing to illegitimately impose values on the public and could make the advice given more trustworthy. (shrink)