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What's the Worst Case? The Methodology of Possibilistic Prediction

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Abstract

Frank Knight (1921) famously distinguished the epistemic modes of certainty, risk, and uncertainty in order to characterize situations where deterministic, probabilistic or possibilistic foreknowledge is available. Because our probabilistic knowledge is limited, i.e. because many systems, e.g. the global climate, cannot be described and predicted probabilistically in a reliable way, Knight’s third category, possibilistic foreknowledge, is not simply swept by the probabilistic mode. This raises the question how to justify possibilistic predictions-including the identification of the worst case. The development of such a modal methodology is particularly vital with respect to predictions of climate change. I show that a methodological dilemma emerges when possibilistic predictions are framed in traditional terms and argue that a more nuanced conceptual framework, distinguishing different types of possibility, should be used in order to convey our uncertain knowledge about the future. The new conceptual scheme, however, questions the applicability of standard rules of rational decision-making, thus generating new challenges.

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... • By emphasising plausible scenarios as more specific and meaningful outputs as opposed to mere possibilities, plausibility is located between probabilistic and possibilistic modes (arrow 2). Populating the interspaces between risk and uncertainty has in fact been a more general scientific debate (see Betz [2010] and his proposition of an 'intermediate mode' between probability and possibility). Concepts like Bayesian probabilities have been brought up as propositions to fill the void conceptually but are empirically questioned. ...
... According to Rescher (1976), following the guidelines to plausible reasoning will ultimately lead to intended or justified results. This is also echoed in newer philosophical debates that point to the need of establishing set principles and guidelines for possibilistic inferences from scenarios (Betz 2010). Also, within educational psychology, concepts and classroom instructions evidently help students to judge scientific statements as more plausible and to enhance their epistemic cognition (Lombardi et al 2015). ...
... One consequence can be to differentiate and flesh out quality criteria for scenarios that build on the empirical findings of this and other studies and are more user-oriented. This would correspond with the research demands of several scholars in recent years (Betz 2010;Kunkel et al 2016;Trutnevyte et al 2016). ...
Book
What does plausibility mean in relation to scenario planning and how do users of scenarios assess it? Despite the concept's ubiquity, its epistemological and empirical foundations remain unexplored in previous research. Ricarda Schmidt-Scheele offers an interdisciplinary perspective: she presents approaches from philosophy of sciences, cognitive psychology, narrative theory and linguistics, and tests key hypotheses in an experimental study. A conceptual map lays out indicators for scenario plausibility and explains how assessments vary across scenario methods. This helps researchers and practitioners to better understand the implications of their methodological choices in scenario development.
... Though differently framed, this approach has some resonance with the work of Wiek et al. (2013), who have suggested that the "plausibility" of scenario elements can be to some extent validated by looking at whether similar things have happened in the past. Implicitly, their definition of plausibility is similar to the 'consistent with what we know' approach of Betz (2010) and it is that sense in which the term 'plausible' is used here. This paper compares rates of diffusion of hydrogen fuel cell vehicles (FCVs) in scenarios with a set of historical alternative fuelled vehicle analogies. ...
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There is a large literature exploring possible hydrogen futures, using various modelling and scenario approaches. This paper compares the rates of transition depicted in that literature with a set of historical analogies. These analogies are cases in which alternative-fuelled vehicles have penetrated vehicle markets. The paper suggests that the literature has tended to be optimistic about the possible rate at which hydrogen vehicles might replace oil-based transportation. The paper compares 11 historical adoptions of alternative fuel vehicles with 24 scenarios from 20 studies that depict possible hydrogen futures. All but one of the hydrogen scenarios show vehicle adoption faster than has occurred for hybrid electric vehicles in Japan, the most successful market for hybrids. Several scenarios depict hydrogen transitions occurring at a rate faster than has occurred in any of the historic examples. The paper concludes that scenarios of alternative vehicle adoption should include more pessimistic scenarios alongside optimistic ones.
... As an example for improving current practice, ESS authors could learn from climate research which delivers a prototype for giving policy advice taking into account uncertainty communication [57]. Probabilistic statements for explorative scenarios rarely can be made since the capability to predict future developments is limited [58]. A common misinterpretation by ESS users is mistaking of businessas-usual scenarios as predictions of what will happen (instead of what can happen). ...
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Background The focus of the paper is on scenario studies that examine energy systems. This type of studies is usually based on formal energy models, from which energy policy recommendations are derived. In order to be valuable for strategic decision-making, the comprehensibility of these complex scenario studies is necessary. We aim at highlighting and mitigating the problematic issue of lacking transparency in such model-based scenario studies. Methods In the first part of the paper, the important concept of transparency in the context of energy scenarios is introduced. In the second part, we develop transparency criteria based on expert judgement. The set of selected criteria is structured into ‘General Information’, ‘Empirical Data’, ‘Assumptions’, ‘Modeling’, ‘Results’, and ‘Conclusions and Recommendations’. Based on these criteria, a transparency checklist is generated. ResultsThe proposed transparency checklist is not intended to measure the quality of energy scenario studies, but to deliver a tool which enables authors of energy scenario studies to increase the level of transparency of their work. The checklist thus serves as a standardized communication protocol and offers guidance for interpreting these studies. A reduced and a full version of the checklist are provided. The former simply lists the transparency criteria and can be adopted by authors with ease; the latter provides details on each criterion. We also illustrate how the transparency checklist may be applied by means of examples. Conclusions We argue that transparency is a necessary condition for a reproducible and credible scenario study. Many energy scenario studies are at present characterized by an insufficient level of transparency. In essence, the checklist represents a synthesizing tool for improving their transparency. The target group of this work is experts, in their role of authors and/or readers of energy scenario studies. By applying the transparency checklist, the authors of energy scenario studies signal their commitment to a high degree of transparency, in consonance with scientific standards.
... It also reflects the fact that as scenario analysis becomes more influential, society may dynamically respond to messages portrayed by such analysis. Even with better modelling approaches, enhanced computational power and refinement of input data, it is impossible to validate long-term scenario results [42][43][44]. In this sense, model-based scenario analysis benefits have focused on assessing a wide range of pathways and gaining insights from them, rather than aiming to narrow the ranges and to produce "accurate" predictions. ...
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Long-term energy scenarios (LTES) have been serving as an important planning tool by a wide range of institutions. This article focuses on how LTES have been used (and also devised in some cases) in the government sector, and specifically how the new challenges and opportunities brought by the aspiration for the clean energy transition change the way that governments use LTES. The information tends to remain tacit, and a gap exists in understanding the way to enhance LTES use and development at the government level. To address this gap, we draw on the experience from national institutions that are leading the improvement in official energy scenario planning to articulate a set of overarching best practices to (i) strengthen LTES development, (ii) effectively use LTES for strategic energy planning and (iii) enhance institutional capacity for LTES-based energy planning, all in the context of new challenges associated with the clean energy transition. We present implementation experience collected through the International Renewable Agency’s LTES Network activities to exemplify these best practices. We highlight that in the context of the broad and complex challenges of a clean energy transition driven by ambitious climate targets, the LTES-based energy planning methodologies need to evolve, reflecting the changing landscapes, and that more effective and extensive use of LTES in government needs to be further encouraged.
... As an example for improving current practice, ESS authors could learn from climate research which delivers a prototype for giving policy advice taking into account uncertainty communication [57]. Probabilistic statements for explorative scenarios rarely can be made since the capability to predict future developments is limited [58]. A common misinterpretation by ESS users is mistaking of businessas-usual scenarios as predictions of what will happen (instead of what can happen). ...
... For example, retrospective analysis of energy demand projections generally shows a poor match to reality [1][2][3][4]. Even with more modelling experience, higher quality input data, and improved computational resources, model results covering multiple decades cannot be validated, making it hard to create a feedback loop that links model improvements to more accurate projections [1,5,6]. Thus the goal of energy modelling should be insights that challenge our working assumptions and mental models rather than a limited set of quantitative predictions [7][8][9]. ...
... Of course, none of this is to imply that science will be able to assign precise probabilities to such extreme outcomes in all or even most cases; with rare events, quantifying their likelihood is difficult. In A c c e p t e d M a n u s c r i p t this context, climate assessments could benefit from exploration of alternative uncertainty frameworks, such as possibilistic prediction [e.g., Betz, 2010]. Lack of complete knowledge need not delay decisions, however. ...
Article
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Climate change is a risk management challenge for society, with uncertain but potentially severe outcomes affecting natural and human systems, across generations. Managing climate-related risks will be more difficult without a base of knowledge and practice aimed at identifying and evaluating specific risks, and their likelihood and consequences, as well as potential actions to promote resilience in the face of these risks. We suggest three improvements to the process of conducting climate change assessments to better characterize risk and inform risk management actions.
... A lengthy discussion on this subject is outside of the scope of this paper, but it is clearly impossible to empirically validate model projections in the sense of eliminating Knightian uncertainty, because there is no real information available about the future. That is to say, due to limited knowledge and computational capacity, all possible outcomes cannot be demonstrated and all impossible outcomes cannot be eliminated (Betz, 2010). While computational models are not crystal balls into the future, the act of constructing and using them is certainly useful for exploring the emergent phenomena found in complex systems. ...
Article
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Many existing technical feasibility and modelling studies in the energy field are criticised for their limited treatment of societal actors and socio-political dynamics, poor representation of the co-evolving nature of society and technology, and hence an inability to analyse socio-technical change. At the same time, prominent conceptual frameworks of socio-technical transitions that address these elements are often found to be difficult to operationalize in quantitative energy analyses that meet policy development requirements. However a new energy modelling paradigm has started to emerge for integrating both quantitative modelling and conceptual socio-technical transitions. This paper provides a taxonomy for this new model category: ‘socio-technical energy transition’ (STET) models. A review of existing STET models and their applications to the energy supply, buildings and transport sectors is provided. Following this review, the paper reflects on the extent to which these existing quantitative models captured the variety of factors covered in socio-technical transitions theory, highlights the challenges associated with their theoretical and behavioural validation, and proposes future development priorities for STET models.
... At the same time, such an amalgamation of plausibility indicators also stems from the fact that the scenario planning literature has provided little guidance on how to interpret scenarios. One consequence from this paper can be to differentiate and flesh out quality criteria for scenarios that build on the findings of this paper and other studies that are more user-oriented [126,133]. Selin [40] has aptly argued that instead of dealing with plausibility as a fixed quality criterion, it can function as "a worthwhile sparring partner that brings up interesting ( …) questions around evidence, trust, science and culture and decision-making". Hence, much can be learnt from further conceptual and empirical evaluations of scenario user judgments, but also from other systematic bodies of research into human judgment mechanisms. ...
Preprint
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Explorative energy scenarios do not present the most probable developments but provide a set of plausible pathways in order to highlight the uncertainty and complexity of decision-making contexts. Although plausibility is widely assumed as effectiveness criterion for scenario work, little is known about how the plausibility of a set of scenarios is perceived by potential users. In this paper, conceptions from philosophy of sciences, cognitive psychology, narrative theory and linguistics are discussed to identify key factors affecting the perceived plausibility of scenarios. A conceptual model is proposed that links users' perceptions to the narrative storytelling and internal structure of the scenario, the perceived credibility of scenario sources and methods and users' worldviews and cognitive styles. The model outlines why energy scenarios are discarded or seriously considered by a wider audience that was not involved in the scenario construction process. It helps to better understand how perceived plausibility relates to scenario usage in a non-linear way as a necessary but not sufficient condition for scenario usage, and provides practical implications for scenario producers.
... One response, adopted by Katzav (2014), is to dissolve the challenge by denying that being consistent with background knowledge is either necessary or sufficient for being a serious possibility. Betz (2010) previously responded to his own challenge by arguing that, although climate models cannot support possibilistic predictions, they can help us articulate or think about them for the first time. In his present contribution, Betz continues to hold that serious possibilities are those that are consistent with background knowledge while reconsidering whether climate models can be used to show that certain predictions are serious possibilities thus construed. ...
... At the same time, such an amalgamation of plausibility indicators also stems from the fact that the scenario planning literature has provided little guidance on how to interpret scenarios. One consequence from this paper can be to differentiate and flesh out quality criteria for scenarios that build on the findings of this paper and other studies that are more user-oriented [126,133]. Selin [40] has aptly argued that instead of dealing with plausibility as a fixed quality criterion, it can function as "a worthwhile sparring partner that brings up interesting ( …) questions around evidence, trust, science and culture and decision-making". Hence, much can be learnt from further conceptual and empirical evaluations of scenario user judgments, but also from other systematic bodies of research into human judgment mechanisms. ...
Article
Full-text available
Explorative energy scenarios do not present the most probable developments but provide a set of plausible pathways in order to highlight the uncertainty and complexity of decision-making contexts. Although plausibility is widely assumed as effectiveness criterion for scenario work, little is known about how the plausibility of a set of scenarios is perceived by users. This paper discusses conceptions from philosophy of sciences, cognitive psychology, narrative theory and linguistics to identify key factors affecting the perceived plausibility of scenarios. A conceptual model is proposed that links users' perceptions to the narrative storytelling and internal structure of the scenario, the perceived credibility of scenario sources and methods and users' worldviews and cognitive styles. The model outlines why energy scenarios are discarded or seriously considered by a wider audience that was not involved in the scenario construction process. The paper explains how perceived plau-sibility constitutes a necessary but not sufficient condition for scenario usage and provides practical implications for scenario producers.
... With "unknown unknowns" (Parker and Risbey, 2015) making it impossible to know the full event space and the corresponding probabilities with certainty, decision principles and tools have been proposed which consider these constraints. Betz (2010Betz ( , 2016 argues decision-makers need to focus more on their risk preferences when judging "worst-case" and "bestcase" scenarios of climate change, rather than its probabilities Similarly, thoughtfully integrating uncertainty explicitly in policy deliberations, both Bradley and Steele (2015) and Hirsch Hadorn et al. (2015) discuss decision strategies to analytically decide whether to accept, revise, or postpone adaptation and mitigation decisions. Roussos et al. (2020) consider three dimensions for more confident decisions using model ensembles: the models' output as probabilities; an expert judgement of confidence in these probabilities; as well as an actor's stakes and cautiousness. ...
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Various scholars have noted—and experienced—tribal tendencies between social-scientific “schools of thought” or “paradigms.” The intensity and fervor of such controversies has led some scientists to compare them with frictions between religious orders. In the research domain focused on the use of climate science for climate adaptation, such disputes revolve around the what “high-quality” climate knowledge and “good” adaptation is or should be. Emphasizing this diversity of orders of social science and the humanities, this article describes five distinct ways social scientists and humanities scholars have thought and written about climate adaptation: descriptivists aim to empirically portray climate adaptation as objectively as possible from an assumed subject-independent perspective; pragmatists' research wants to increase climate resilience through usable climate information; argumentivists strive for assessing the justification of climate scientific findings, as well as adaptation decision-making that is based on these findings; interpretivists seek to empirically redescribe how the use of climate science for adaptation is shaped by, and shapes, various other social processes and political actors; and critical scholars work toward revealing how pervasive powerful interests and marginalizing discourses shape adaptation projects negatively. By comparing these five orders' respective scientific, environmental and social aims and concerns, this article pinpoints to how epistemological, ontological and methodological priorities not only drive scientific controversies on issues such as what “high-quality knowledge” is, but also how interdependent orders' methodological choices are with their epistemological and ontological positions. However, this analysis also reveals that while some scholars implicitly stick to their order, others are comfortable to collaborate across such borders. Overall, the diverging aims, priorities, and methods are unlikely to be ever fully reconciled. A better understanding of why academics from different orders differ in the approaches they take and the issues they care about will likely lead to a larger appreciation of the differences of other orders' research and broaden our understanding of key dynamics in studying “good” climate adaptation and “high-quality” climate knowledge.
... But controversies aside, about one issue all scholars working on PP seem to agree: precaution is only warranted if a threat of harm constitutes a realistic possibility, rather than a far-fetched fantasy (e.g. Betz, 2010;Carter & Peterson, 2015;Gardiner, 2006;Hartzell-Nichols, 2017). We should not take precautionary measures in the face of any dreamed-up catastrophe, however fanciful. ...
Preprint
A challenge faced by defenders of the precautionary principle is to clarify when the evidence that a harmful event might occur suffices to regard this prospect as a real possibility. Plausible versions of the principle must articulate some epistemic threshold, or de minimis requirement, which specifies when precautionary measures are justified. Critics have argued that formulating such a threshold is problematic in the context of the precautionary principle. First, this is because the precautionary principle appears to be ambiguous about the distinction between risk and uncertainty: should the principle merely be invoked when evidential probabilities are absent, or also when probabilities have low epistemic credentials? Secondly, defenders of the precautionary principle face an aggregation puzzle: in judging whether or not the de minimis requirement has been met, how should first-order evidential probabilities and their second-order epistemic standing be aggregated? This article argues that the ambiguity can be resolved, and the epistemological puzzle can be solved. Focusing on decisions in the context of climate uncertainty, I advance a version of the precautionary principle that serves as a plausible decision rule, to be adopted in situations where its main alternative – cost-benefit analysis – does not deliver.
... But controversies aside, about one issue all scholars working on PP seem to agree: precaution is only warranted if a threat of harm constitutes a realistic possibility, rather than a far-fetched fantasy (e.g. Betz, 2010;Carter & Peterson, 2015;Gardiner, 2006;Hartzell-Nichols, 2017). We should not take precautionary measures in the face of any dreamed-up catastrophe, however fanciful. ...
Article
Full-text available
A challenge faced by defenders of the precautionary principle is to clarify when the evidence that a harmful event might occur suffices to regard this prospect as a real possibility. Plausible versions of the principle must articulate some epistemic threshold, or de minimis requirement, which specifies when precautionary measures are justified. Critics have argued that formulating such a threshold is problematic in the context of the precautionary principle. First, this is because the precautionary principle appears to be ambiguous about the distinction between risk and uncertainty: should the principle merely be invoked when evidential probabilities are absent, or also when probabilities have low epistemic credentials? Secondly, defenders of the precautionary principle face an aggregation puzzle: in judging whether or not the de minimis requirement has been met, how should first-order evidential probabilities and their second-order epistemic standing be aggregated? This article argues that the ambiguity can be resolved, and the epistemological puzzle can be solved. Focusing on decisions in the context of climate uncertainty, I advance a version of the precautionary principle that serves as a plausible decision rule, to be adopted in situations where its main alternative—cost–benefit analysis—does not deliver.
... When it comes to sketching scenarios about what might realistically occur in the future, our aim is to approximate the real possibilities as close as we can. The totality of our background knowledge is relevant for this purpose: at minimum, our identification of realistic possibilities should be compatible with this background knowledge (Betz 2010). But arguably, this minimal constraint does not suffice: many propositions that are not obviously excluded by our background knowledge seem highly improbable, nonetheless. ...
Preprint
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The co-shaping of technology and values is a topic of increasing interest among philosophers of technology. Part of this interest pertains to anticipating future value change, or what Danaher (2021) calls the investigation of "axiological futurism". However, this investigation faces a challenge: "axiological possibility space" is vast, and we currently lack a clear account of how this space should be demarcated. It stands to reason that speculations about how values might change over time should exclude farfetched possibilities and be restricted to possibilities that can be dubbed to be realistic instead. But what does this realism criterion entail? This paper introduces the notion of realistic possibilities as a key conceptual advancement to the study of axiological futurism and offers suggestions as to how realistic possibilities of future value change might be identified. Additionally, I propose two slight modifications to the approach of axiological futurism. First, I argue that axiological futurism can benefit from a thoroughly historicised understanding of moral change. Secondly, I argue that when employed in normative contexts, the axiological futurist should seek to identify realistic possibilities that come along with substantial normative risks.
... In light of this challenge, some scientists and philosophers (e.g. Stainforth et al. 2007, Betz 2010, Katzav 2014 have argued that the most we should expect from current climate models is for them to be used as tools for articulating 'possibilities'. 27 Betz suggests that, under this view, 'progress would 27 Whether we should think of these possibilities as 'real possibilities' is itself a source of debate. ...
Thesis
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 (or anyone who believes the role of probability in inductive inference is to quantify the degree of belief to assign to a hypothesis given the evidence) 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.
... Cases when we know nothing about how likely the possible outcomes are (more than that their probabilities are above zero) are sometimes called decision-making under ignorance (Alexander 1975). (However, some authors reserve the term "ignorance" for decisions where some possible outcomes are unknown (Betz 2010)). ...
Chapter
Due to its high demands on information input, traditional decision theory is inadequate to deal with many real-life situations. If, for instance, probabilities or values are undetermined, the standard method of maximizing expected values cannot be used. The difficulties are aggravated if further information is lacking or uncertain, for instance information about what options are available and what their potential consequences may be. However, under such conditions, methods from philosophical analysis and in particular argumentation analysis can be used to systematize our deliberations. Such methods are also helpful if the framing of the decision problem is contested. The argumentative turn in policy analysis is a widened rationality approach that scrutinises inferences from what is known and what is unknown in order to substantiate decision-supporting deliberations. It includes and recognises the normative components of decisions and makes them explicit to help finding reasonable decisions with democratic legitimacy.
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Climate models don’t give us probabilistic forecasts. To interpret their results, alternatively, as serious possibilities seems problematic inasmuch as climate models rely on contrary-to-fact assumptions: why should we consider their implications as possible if their assumptions are known to be false? The paper explores a way to address this possibilistic challenge. It introduces the concepts of a perfect and of an imperfect credible world, and discusses whether climate models can be interpreted as imperfect credible worlds. That would allow one to use models for possibilistic prediction and salvage widespread scientific practice.
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The Intergovernmental Panel on Climate Change (IPCC) has, in its most recent Assessment Report (AR5), articulated guidelines for evaluating and communicating uncertainty that include a qualitative scale of confidence. We examine one factor included in that scale: the “degree of agreement.” Some discussions of the degree of agreement in AR5 suggest that the IPCC is employing a consensus-oriented social epistemology. We consider the application of the degree of agreement factor in practice in AR5. Our findings, though based on a limited examination, suggest that agreement attributions do not so much track the overall consensus among investigators as the degree to which relevant research findings substantively converge in offering support for IPCC claims. We articulate a principle guiding confidence attributions in AR5 that centers not on consensus but on the notion of support. In concluding, we tentatively suggest a pluralist approach to the notion of support.
Article
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An overview of the German philosophy of science community is given for the years 1992–2012, based on a survey in which 159 philosophers of science in Germany participated. To this end, the institutional background of the German philosophy of science community is examined in terms of journals, centers, and associations. Furthermore, a qualitative description and a quantitative analysis of our survey results are presented. Quantitative estimates are given for: (a) academic positions, (b) research foci, (c) philosophers’ of science most important publications, and (d) externally funded projects, where for (c) all survey participants had indicated their five most important publications in philosophy of science. In addition, the survey results for (a)–(c) are also qualitatively described, as they are interesting in their own right. With respect to (a), we estimated the gender distribution among academic positions. Concerning (c), we quantified philosophers’ of science preference for (i) journals and publishers, (ii) publication format, (iii) language, and (iv) coauthorship for their most important publications. With regard to research projects, we determined their (i) prevalence, (ii) length, and (iii) trend (an increase in number?) as well as their most frequent (iv) research foci and (v) funding organizations. We also distinguished between German-based and non-German-based journals, publishers, and funding institutions, making it thereby possible to evaluate the involvement of the German philosophy of science community in the international research landscape. Finally, we discuss some implications of our findings.
Chapter
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Die epistemischen Herausforderungen des Planens sind in den vorausgehenden Kapiteln ausführlich behandelt worden. Das folgende soll den Blick auf Aufgaben, Akteure, Systeme und Prozesse der Planung richten. Deren Bedeutung wird aus dem Umstand ersichtlich, dass Planung, selbst wenn sie sich ihrer Sache vollkommen sicher wäre und alle notwendigen Mittel zur Hand hätte, immer noch scheitern könnte – an Mängeln der Planungsorganisation, unzureichender Koordination (teil-) autonomer Akteure, Widerstand von Betroffenen, Missverständnissen und Kommunikationsfehlern, kurz: sozialen Ressourcen und Faktoren, die Werthaltungen, Interessen, Problemwahrnehmungen, Einstellungen zur Aufgabe und insbesondere das Interaktionssystem der am Planungsprozess Beteiligten umfassen.
Chapter
Intended as a practical guide for decision analysts, this chapter provides an introduction to reasoning under great uncertainty. It seeks to incorporate standard methods of risk analysis in a broader argumentative framework by re-interpreting them as specific (consequentialist) arguments that may inform a policy debate—side by side along further (possibly non-consequentialist) arguments which standard economic analysis does not account for. The first part of the chapter reviews arguments that can be advanced in a policy debate despite deep uncertainty about policy outcomes, i.e. arguments which assume that uncertainties surrounding policy outcomes cannot be (probabilistically) quantified. The second part of the chapter discusses the epistemic challenge of reasoning under great uncertainty, which consists in identifying all possible outcomes of the alternative policy options. It is argued that our possibilistic foreknowledge should be cast in nuanced terms and that future surprises—triggered by major flaws in one’s possibilistic outlook—should be anticipated in policy deliberation.
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Philosophers of science have recently debated whether second-order uncertainty in climate change forecasts demonstrates the applicability of the argument from inductive risk (AIR) to this case. This paper defends a generalized, normative, and structural interpretation of AIR to address challenges raised in this literature. The interpretation of AIR proposed is generalized by including the possibility that scientists may suspend judgment rather than accept or reject a hypothesis. In addition, it distinguishes between descriptive and normative versions of AIR, and provides reasons for preferring the latter. Finally, it emphasizes advantages of applying AIR at a structural rather than individual level.
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Precautionary Principles are often said to be appropriate for decision-making in contexts of uncertainty such as climate policy. Contexts of uncertainty are contrasted to contexts of risk depending on whether we have probabilities or not. Against this view, I argue that the risk-uncertainty distinction is practically irrelevant. I start by noting that the history of the distinction between risk and uncertainty is more varied than is sometimes assumed. In order to examine the distinction, I unpack the idea of having probabilities, in particular by distinguishing three interpretations of probability: objective, epistemic, and subjective probability. I then claim that if we are concerned with whether we have probabilities at all-regardless of how low their epistemic credentials are-then we almost always have probabilities for policy-making. The reason is that subjective and epistemic probability are the relevant interpretations of probability and we almost always have subjective and epistemic probabilities. In contrast, if we are only concerned with probabilities that have sufficiently high epistemic credentials, then we obviously do not always have probabilities. Climate policy, for example, would then be a case of decision-making under uncertainty. But, so I argue, we should not dismiss probabilities with low epistemic credentials. Rather, when they are the best available probabilities our decision principles should make use of them. And, since they are almost always available, the risk-uncertainty distinction remains irrelevant.
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Climate predictions - and the computer models behind them - play a key role in shaping public opinion and our response to the climate crisis. Some people interpret these predictions as 'prophecies of doom' and some others dismiss them as mere speculation, but the vast majority are only vaguely aware of the science behind them. This book gives a balanced view of the strengths and limitations of climate modeling. It covers historical developments, current challenges, and future trends in the field. The accessible discussion of climate modeling only requires a basic knowledge of science. Uncertainties in climate predictions and their implications for assessing climate risk are analyzed, as are the computational challenges faced by future models. The book concludes by highlighting the dangers of climate 'doomism', while also making clear the value of predictive models, and the severe and very real risks posed by anthropogenic climate change.
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The 2008 Climate Change Act and ‘adaptation reporting power’ afforded to the UK Government have generated demand for information on future climate risks. The UK Climate Projections 2009 (UKCP09) are the latest generation of national climate change scenarios. This Commentary reflects on the enduring scientific and political contribution of UKCP09 as evidenced by academic literature, adaptation reports and media perspectives. It is contested that the more explicit treatment of uncertainty by the UKCP09 projections compared with the predecessor (UKCIP02) was a significant step forward. User unfamiliarity with the probabilistic format can be managed through support and guidance, so the complexity of UKCP09 need not be limiting. Some may question the value added by climate modelling (and downscaling) to adaptation planning given present fiscal retrenchment. However, the benefit of UKCP09 could be greater than expected, especially if new ways of responding to climate uncertainty emerge.
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In part one, I identify the core logical structure of the precautionary principle and distinguish it from the various key concepts that appear in the many different formulations of the principle. I survey these concepts and suggest a program of further conceptual analysis. In part two, I examine a particular version of the precautionary principle dubbed "the catastrophe principle" and criticize it in light of its similarities to the principles at work in Pascal's Wager. I conclude with some suggestions for advocates of the precautionary principle who wish their formulation to avoid the pitfalls confronting the catastrophe principle.
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The first GCM climate change projections to include dynamic vegetation and an interactive carbon cycle produced a very significant amplification of global warming over the 21st century. Under the IS92a business as usual emissions scenario CO2 concentrations reached about 980ppmv by 2100, which is about 280ppmv higher than when these feedbacks were ignored. The major contribution to the increased CO2 arose from reductions in soil carbon because global warming is assumed to accelerate respiration. However, there was also a lesser contribution from an alarming loss of the Amazonian rainforest. This paper describes the phenomenon of Amazonian forest dieback under elevated CO2 in the Hadley Centre climate-carbon cycle model.
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This article agrees with Philip Kitcher that we should aim for a well‐ordered science, one that answers the right questions in the right ways. Crucial to this is to address questions of use: Which scientific account is right for which system in which circumstances? This is a difficult question: evidence that may support a scientific claim in one context may not support it in another. Drawing on examples in physics and other sciences, this article argues that work on the warrant of theories in philosophy of science needs to change. Emphasis should move from the warrant of theories in the abstract to questions of evidence for use.
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The continued increase in the atmospheric concentration of carbon dioxide due to anthropogenic emissions is predicted to lead to significant changes in climate. About half of the current emissions are being absorbed by the ocean and by land ecosystems, but this absorption is sensitive to climate as well as to atmospheric carbon dioxide concentrations, creating a feedback loop. General circulation models have generally excluded the feedback between climate and the biosphere, using static vegetation distributions and CO2 concentrations from simple carbon-cycle models that do not include climate change. Here we present results from a fully coupled, three-dimensional carbon-climate model, indicating that carbon-cycle feedbacks could significantly accelerate climate change over the twenty-first century. We find that under a 'business as usual' scenario, the terrestrial biosphere acts as an overall carbon sink until about 2050, but turns into a source thereafter. By 2100, the ocean uptake rate of 5 Gt C yr(-1) is balanced by the terrestrial carbon source, and atmospheric CO2 concentrations are 250 p.p.m.v. higher in our fully coupled simulation than in uncoupled carbon models, resulting in a global-mean warming of 5.5 K, as compared to 4 K without the carbon-cycle feedback.
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The magnitude and impact of future global warming depends on the sensitivity of the climate system to changes in greenhouse gas concentrations. The commonly accepted range for the equilibrium global mean temperature change in response to a doubling of the atmospheric carbon dioxide concentration, termed climate sensitivity, is 1.5-4.5 K (ref. 2). A number of observational studies, however, find a substantial probability of significantly higher sensitivities, yielding upper limits on climate sensitivity of 7.7 K to above 9 K (refs 3-8). Here we demonstrate that such observational estimates of climate sensitivity can be tightened if reconstructions of Northern Hemisphere temperature over the past several centuries are considered. We use large-ensemble energy balance modelling and simulate the temperature response to past solar, volcanic and greenhouse gas forcing to determine which climate sensitivities yield simulations that are in agreement with proxy reconstructions. After accounting for the uncertainty in reconstructions and estimates of past external forcing, we find an independent estimate of climate sensitivity that is very similar to those from instrumental data. If the latter are combined with the result from all proxy reconstructions, then the 5-95 per cent range shrinks to 1.5-6.2 K, thus substantially reducing the probability of very high climate sensitivity.
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There is a scientific consensus regarding the reality of anthropogenic climate change. This has led to substantial efforts to reduce atmospheric greenhouse gas emissions and thereby mitigate the impacts of climate change on a global scale. Despite these efforts, we are committed to substantial further changes over at least the next few decades. Societies will therefore have to adapt to changes in climate. Both adaptation and mitigation require action on scales ranging from local to global, but adaptation could directly benefit from climate predictions on regional scales while mitigation could be driven solely by awareness of the global problem; regional projections being principally of motivational value. We discuss how recent developments of large ensembles of climate model simulations can be interpreted to provide information on these scales and to inform societal decisions. Adaptation is most relevant as an influence on decisions which exist irrespective of climate change, but which have consequences on decadal time-scales. Even in such situations, climate change is often only a minor influence; perhaps helping to restrict the choice of 'no regrets' strategies. Nevertheless, if climate models are to provide inputs to societal decisions, it is important to interpret them appropriately. We take climate ensembles exploring model uncertainty as potentially providing a lower bound on the maximum range of uncertainty and thus a non-discountable climate change envelope. An analysis pathway is presented, describing how this information may provide an input to decisions, sometimes via a number of other analysis procedures and thus a cascade of uncertainty. An initial screening is seen as a valuable component of this process, potentially avoiding unnecessary effort while guiding decision makers through issues of confidence and robustness in climate modelling information. Our focus is the usage of decadal to centennial time-scale climate change simulations as inputs to decision making, but we acknowledge that robust adaptation to the variability of present day climate encourages the development of less vulnerable systems as well as building critical experience in how to respond to climatic uncertainty.
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Verification and validation of numerical models of natural systems is impossible. This is because natural systems are never closed and because model results are always nonunique. Models can be confirmed by the demonstration of agreement between observation and prediction, but confirmation is inherently partial. Complete confirmation is logically precluded by the fallacy of affirming the consequent and by incomplete access to natural phenomena. Models can only be evaluated in relative terms, and their predictive value is always open to question. The primary value of models is heuristic.
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An incredible wealth of scientific data on global warming has been collected in the last few decades. The history of the Earth's climate has been probed by drilling into polar ice sheets and sediment layers of the oceans' vast depths, and great advances have been made in computer modelling of our climate. This book provides a concise and accessible overview of what we know about ongoing climate change and its impacts, and what we can do to confront the climate crisis. Using clear and simple graphics in full colour, it lucidly highlights information contained in the Intergovernmental Panel on Climate Change reports, and brings the subject completely up-to-date with current science and policy. The book makes essential scientific information on this critical topic accessible to a broad audience. Obtaining sound information is the first step in preventing a serious, long-lasting degradation of our planet's climate, helping to ensure our future survival.
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The author considers the implications for current assumptions about scientific knowledge and environmental policy raised by the preventive approach and the associated Precautionary Principle. He offers a critical examination of approaches to characterizing different kinds of uncertainty in policy knowledge, especially in relation to decision making upstream from environmental effects. Via the key dimension of unrecognized indeterminacy in scientific knowledge, the author argues that shifting the normative principles applied to policy use of science is not merely an external shift in relation to the same body of 'natural' knowledge, but also involves the possible reshaping of the 'natural' knowledge itself.
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The Argument In its reconstruction of scientific practice, philosophy of science has traditionally placed scientific theories in a central role, and has reduced the problem of mediating between theories and the world to formal considerations. Many applications of scientific theories, however, involve complex mathematical models whose constitutive equations are analytically unsolvable. The study of these applications often consists in developing representations of the underlying physics on a computer, and using the techniques of computer simulation in order to learn about the behavior of these systems. In many instances, these computer simulations are not simple number-crunching techniques. They involve a complex chain of inferences that serve to transform theoretical structures into specific concrete knowledge of physical systems. In this paper I argue that this process of transformation has its own epistemology. I also argue that this kind of epistemology is unfamiliar to most philosophy of science, which has traditionally concerned itself with the justification of theories, not with their application. Finally, I urge that the nature of this epistemology suggests that the end results of some simulations do not bear a simple, straightforward relation to the theories from which they stem.
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[T]he Precautionary Principle still has neither a commonly accepted definition nor a set of criteria to guide its implementation. 'There is,' Freestone... cogently observes, 'a certain paradox in the widespread and rapid adoption of the Precautionary Principle:' While it is applauded as a 'good thing,' no one is quite sure about what it really means or how it might be implemented. Advocates foresee precaution developing into 'the fundamental principle of environmental protection policy at [all] scales.'... Sceptics, however, claim its popularity derives from its vagueness; that it fails to bind anyone to anything or resolve any of the deep dilemmas that characterize modern environmental policy making.
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Computational science, especially computer simulations, is now the dominant procedure in many areas of science. This book contains the first systematic philosophical account of this new scientific method, and draws a parallel between the ways in which such computational methods have enhanced our abilities to mathematically model the world, and the more familiar ways in which scientific instruments have expanded our access to the empirical world. This expansion forms the basis for a new kind of empiricism better suited to the needs of science than the older anthropocentric forms of empiricism. Human abilities are no longer the ultimate standard of correctness within epistemology. The book includes arguments for the primacy of properties rather than objects, for how technology interacts with scientific methods, and a detailed account of how the path from a computational template or model to a scientific application is constructed and revised. This last feature allows us to hold a form of selective realism in which anti-realist arguments based on abstract reconstructions of theories can be avoided. One important consequence of the rise of computational methods is that the traditional organization of the sciences is being replaced by an organization founded on computational templates.
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To study Earth’s climate, scientists now use a variety of computer simulation models. These models disagree in some of their assumptions about the climate system, yet they are used together as complementary resources for investigating future climatic change. This paper examines and defends this use of incompatible models. I argue that climate model pluralism results both from uncertainty concerning how to best represent the climate system and from difficulties faced in evaluating the relative merits of complex models. I describe how incompatible climate models are used together in ‘multi-model ensembles’ and explain why this practice is reasonable, given scientists’ inability to identify a ‘best’ model for predicting future climate. Finally, I characterize climate model pluralism as involving both an ontic competitive pluralism and a pragmatic integrative pluralism.
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Geochemistry plays an important role when assessing the impact of CO2 storage. Due to the potential corrosive character of CO2, it might affect the chemical and physical properties of the wells, the reservoir and its surroundings and increase the environmental and financial risk of CO2 storage projects in deep geological structures. An overview of geochemical and solute transport modelling for CO2 storage purposes is given, its data requirements and gaps are highlighted, and its progress over the last 10 years is discussed. Four different application domains are identified: long-term integrity modelling, injectivity modelling, well integrity modelling and experimental modelling and their current state of the art is discussed. One of the major gaps remaining is the lack of basic thermodynamical and kinetic data at relevant temperature and pressure conditions for each of these four application domains. Real challenges are the coupled solute transport and geomechanical modelling, the modelling of impurities in the CO2 stream and pore-scale modelling applications.
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There are difficulties with probability as a representation of uncertainty. However, we argue that there is an important distinction between principle and practice. In principle, probability is uniquely appropriate for the representation and quantification of all forms of uncertainty; it is in this sense that we claim that ‘probability is perfect’. In practice, people find it difficult to express their knowledge and beliefs in probabilistic form, so that elicitation of probability distributions is a far from perfect process. We therefore argue that there is no need for alternative theories, but that any practical elicitation of expert knowledge must fully acknowledge imprecision in the resulting distribution.We outline a recently developed Bayesian technique that allows the imprecision in elicitation to be formulated explicitly, and apply it to some of the challenge problems.
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This paper describes an approach to the operationalisation of extended peer communities that deploys uncertainty, ignorance and indeterminacy, and examines the crucial role of trust. Trust underwrites both the dialogue central to extended peer communities and the functional utility of the knowledge so created, because when “facts are uncertain, values in dispute, stakes high … and the framing of the problem involves politics and values as much as science” (Ravetz J. Knowledge in an uncertain world. New Scientist 1990;127:2) the taken-for-granted trust in `normal' science is no longer assured, necessitating the cultivation of trust by other means. It is argued that extended peer communities provide a focus for the ascendant politics of the post-normal realm, in resonance with recently articulated insights into broader social theory.“… we continue to believe in the sciences, but instead of taking in their objectivity, their truth, their coldness, their extraterritoriality … we retain what has always been most interesting about them: their daring, their experimentation, their uncertainty, their warmth, their incongruous blend of hybrids, their crazy ability to reconstitute the social bond. We take away from them only the mystery of their birth and the danger their clandestineness posed to democracy” (Latour B. We have never been modern. Hemel Hempstead (UK): Harvester Wheatsheaf, 1993:142).
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