Philosophers of science have paid close attention to climate simulations as means of projecting the severity and effects of climate change, but have neglected the full diversity of methods in climate science. This paper shows the philosophical richness of another method in climate science: the practice of using paleoclimate analogues to inform our climate projections. First, I argue that the use of paleoclimate analogues can offer important insights to philosophers of the historical sciences. Rather than using the present as a (...) guide to the past, as is common in the historical sciences, paleoclimate analogues involve using the past as a guide to the future. I thereby distinguish different methods in the historical sciences and argue that these distinctions bear on debates over whether the historical sciences can produce generalizations or predictions. Second, I suggest that paleoclimate analogues might actually be considered a type of climate model, and, as such, their use expands on common characterizations of models to include those that are full-scale, naturally occurring, and non-manipulable. (shrink)

Using past episodes of climate change as a source of evidence to inform our projections about contemporary climate change requires establishing the extent to which episodes in the deep past are analogous to the current crisis. However, many scientists claim that contemporary rates of climate change (e.g., rates of carbon emissions or temperature change) are unprecedented, including compared to episodes in the deep past. If so, this would limit the utility of paleoclimate analogues. In this paper, I show how a (...) data adjustment procedure called “temporal scaling,” which must be applied to both contemporary and past rate data, complicates the claim that contemporary rates are truly unprecedented. On top of giving actionable recommendations to scientists, this paper advances the philosophical literature concerning the use of models that are known to be somewhat disanalogous to their target systems. (shrink)

Phylogenetic models traditionally represent the history of life as having a strictly-branching tree structure. However, it is becoming increasingly clear that the history of life is often not strictly-branching; lateral gene transfer, endosymbiosis, and hybridization, for example, can all produce lateral branching events. There is thus motivation to allow phylogenetic models to have a reticulate structure. One proposal involves the reconciliation of genealogical discordance. Briefly, this method uses patterns of disagreement – discordance – between trees of different genes to add (...) lateral branching events to phylogenetic trees of taxa, and to estimate the most likely cause of these events. I use this practice to argue for: (1) a need for expanded accounts of multiple-models idealization, (2) a distinction between automatic and manual de-idealization, and (3) recognition that idealization may serve the meso-level aims of science in a different way than hitherto acknowledged. (shrink)

Integrated assessment models play a major role in the science and policy of climate change. Similarly to other widely used computational tools for addressing socially relevant problems, IAMs need to account for the key uncertainties characterizing processes and socio-economic responses. In the case of climate change, these are particularly complex given the very long-term nature of climate and the deep uncertainty characterizing technological and human systems. Here we draw from philosophical discussion of mathematical modeling of social problems and review the (...) role of uncertainty in climate-economic modeling. In agreement with the literature, we highlight the crucial role of epistemic uncertainty in IAMs. We posit that the normative components of models, more than the physical and socio-techno-economic ones, are the most fraught by uncertainty and yet the least understood. We suggest a research agenda to explore uncertainties of evaluation frameworks, transcending the current implicit normativity of IAMs. (shrink)

We argue that the robustness analysis of idealized models can have confirmational power. This responds to concerns recently raised in the literature, according to which the robustness analysis of models whose idealizations are not discharged is unable to confirm the causal mechanisms underlying these models, and the robustness analysis of models whose idealizations are discharged is unnecessary. In response, we make clear that, where idealizations sweep out, in a specific way, the space of possibilities— which is sometimes, though not always, (...) the case—they can be holistically discharged. In turn, this can be used to show that the robustness analysis of idealized models can have confirmational force after all. (shrink)

Computer simulations are nowadays often directly involved in the generation of experimental results. Given this dependency of experiments on computer simulations, that of simulations on models, and that of the models on free parameters, how do researchers establish trust in their experimental results? Using high-energy physics (HEP) as a case study, I will identify three different types of robustness that I call conceptual, methodological, and parametric robustness, and show how they can sanction this trust. However, as I will also show, (...) simulation models in HEP themselves fail to exhibit a type of robustness I call inverse parametric robustness. This combination of robustness and failures thereof is best understood by distinguishing different epistemic capacities of simulations and different senses of trust: Trusting simulations in their capacity to facilitate credible experimental results can mean accepting them as means for generating belief in these results, while this need not imply believing the models themselves in their capacity to represent an underlying reality. (shrink)

Convergence of model projections is often considered by climate scientists to be an important objective in so far as it may indicate the robustness of the models’ core hypotheses. Consequently, the range of climate projections from a multi-model ensemble, called “model spread”, is often expected to reduce as climate research moves forward. However, the successive Assessment Reports of the Intergovernmental Panel on Climate Change indicate no reduction in model spread, whereas it is indisputable that climate science has made improvements in (...) its modelling. In this paper, after providing a detailed explanation of the situation, we describe an epistemological setting in which a steady model spread is not doomed to be seen as negative, and is indeed compatible with a desirable evolution of climate models taken individually. We further argue that, from the perspective of collective progress, as far as the improvement of the products of a multi-model ensemble is concerned, reduction of model spread is of lower priority than model independence. (shrink)

In climate science, climate models are one of the main tools for understanding phenomena. Here, we develop a framework to assess the fitness of a climate model for providing understanding. The framework is based on three dimensions: representational accuracy, representational depth, and graspability. We show that this framework does justice to the intuition that classical process-based climate models give understanding of phenomena. While simple climate models are characterized by a larger graspability, state-of-the-art models have a higher representational accuracy and representational (...) depth. We then compare the fitness-for-providing understanding of process-based to data-driven models that are built with machine learning. We show that at first glance, data-driven models seem either unnecessary or inadequate for understanding. However, a case study from atmospheric research demonstrates that this is a false dilemma. Data-driven models can be useful tools for understanding , specifically for phenomena for which scientists can argue from the coherence of the models with background knowledge to their representational accuracy and for which the model complexity can be reduced such that they are graspable to a satisfactory extent. When citing this paper, please use the full journal title Studies in History and Philosophy of Science. (shrink)

An important epistemic issue in climate modelling concerns structural uncertainty: uncertainty about whether the mathematical structure of a model accurately represents its target. How does structural uncertainty affect our knowledge and predictions about the climate? How can we identify sources of structural uncertainty? Can we manage the effect of structural uncertainty on our knowledge claims? These are some of the questions that an epistemology of structural uncertainty faces, and these questions are also important for climate scientists and policymakers. I develop (...) three desiderata for an epistemological account of structural uncertainty. In my view, an account of structural uncertainty should identify sources of structural uncertainty, explain how these sources limit the applicability of a model, and show how the severity of structural uncertainty depends on the questions that can be asked of a model. I argue that analyzing structural uncertainty by paying attention to the details of model building can satisfy these desiderata. I focus on parametrizations, which are representations of important processes occurring at scales that are not resolved by climate models. Parametrizations are often thought to be ad-hoc, but I show that some important parametrizations are theoretically justified by explicit or implicit scale separation assumptions. These assumptions can also be supported empirically. Analyzing these theoretical and empirical justificatory roles of the scale separation assumptions can provide insights into how parametrizations contribute to structural uncertainty. I conclude by sketching how my approach can satisfy the desiderata I set out at the beginning, highlighting its importance for policy-relevant scientific statements about the climate. (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)

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...

The aim of this dissertation is to comprehensively study various robustness arguments proposed in the literature from Levins to Lloyd as well as the opposition offered to them and pose enquiry into the degree of epistemic virtue that they provide to the model prediction results with respect to climate science and modeling. Another critical issue that this dissertation strives to examine is that of the actual epistemic notion that is operational when scientists and philosophers appeal to robustness. In attempting to (...) explicate this idea, the discussion turns to arguments provided by Schupbach who completely rejects probabilistic independence in favour of explanatory reasoning, Stegenga and Menon who still see some value in probabilistic independence, and Winsberg who takes applies Schupbach’s to climate science, going beyond models to involve multi-modal evidence. After an exhaustive discussion on these arguments, this dissertation attempts to provide a thorough and updated notion of robustness in climate modeling and climate science. (shrink)

Microbial model systems have a long history of fruitful use in fields that include evolution and ecology. In order to develop further insight into modelling practice, we examine how the competitive exclusion and coexistence of competing species have been modelled mathematically and materially over the course of a long research history. In particular, we investigate how microbial models of these dynamics interact with mathematical or computational models of the same phenomena. Our cases illuminate the ways in which microbial systems and (...) equations work as models, and what happens when they generate inconsistent findings about shared targets. We reveal an iterative strategy of comparative modelling in different media, and suggest reasons why microbial models have a special degree of epistemic tractability in multimodel inquiry. (shrink)

With the ever-growing quality of observational data in astronomy, the complexity of astrophysical models has been increasing in turn. This trend raises the question: Are there still reasons to prefer simpler models if the final goal is an actual model-target comparison? I argue for two aspects in which astrophysical research may favor models having reduced complexity: first, to address the problem of determining the values of adjustable parameters and, second, to pave the way for a validation of the model based (...) on the modeler’s understanding of the scope of the model and the critical processes on the target’s side. (shrink)

The geosciences include a wide spectrum of disciplines ranging from paleontology to climate science, and involve studies of a vast range of spatial and temporal scales, from the deep-time history of microbial life to the future of a system no less immense and complex than the entire Earth. Modeling is thus a central and indispensable tool across the geosciences. Here, we review both the history and current state of model-based inquiry in the geosciences. Research in these fields makes use of (...) a wide variety of models, such as conceptual, physical, and numerical models, and more specifically cellular automata, artificial neural networks, agent-based models, coupled models, and hierarchical models. We note the increasing demands to incorporate biological and social systems into geoscience modeling, challenging the traditional boundaries of these fields. Understanding and articulating the many different sources of scientific uncertainty – and finding tools and methods to address them – has been at the forefront of most research in geoscience modeling. We discuss not only structuralmodel uncertainties, parameter uncertainties, and solution uncertainties, but also the diverse sources of uncertainty arising from the complex nature of geoscience systems themselves. Without an examination of the geosciences, our philosophies of science and our understanding of the nature of model-based science are incomplete. (shrink)

Robustness arguments hold that hypotheses are more likely to be true when they are confirmed by diverse kinds of evidence. Robustness arguments require the confirming evidence to be independent. We identify two kinds of independence appealed to in robustness arguments: ontic independence —when the multiple lines of evidence depend on different materials, assumptions, or theories—and probabilistic independence. Many assume that OI is sufficient for a robustness argument to be warranted. However, we argue that, as typically construed, OI is not a (...) sufficient independence condition for warranting robustness arguments. We show that OI evidence can collectively confirm a hypothesis to a lower degree than individual lines of evidence, contrary to the standard assumption undergirding usual robustness arguments. We employ Bayesian networks to represent the ideal empirical scenario for a robustness argument and a variety of ways in which empirical scenarios can fall short of this ideal. (shrink)

I develop an account of productive surprise as an epistemic virtue of scientific investigations which does not turn on psychology alone. On my account, a scientific investigation is potentially productively surprising when results can conflict with epistemic expectations, those expectations pertain to a wide set of subjects. I argue that there are two sources of such surprise in science. One source, often identified with experiments, involves bringing our theoretical ideas in contact with new empirical observations. Another, often identified with simulations, (...) involves articulating and bringing together different parts of our knowledge. Both experiments and simulations, then, can surprise. (shrink)

Derivational robustness may increase the degree to which various pieces of evidence indirectly confirm a robust result. There are two ways in which this increase may come about. First, if one can show that a result is robust, and that the various individual models used to derive it also have other confirmed results, these other results may indirectly confirm the robust result. Confirmation derives from the fact that data not known to bear on a result are shown to be relevant (...) when it is shown to be robust. Second, robustness may increase the degree to which the robust result is indirectly confirmed if it increases the weight with which existing evidence indirectly confirms it. This may happen when it strengthens the connection between the core and the robust result by showing that auxiliaries are not responsible for the result. (shrink)

To study climate change, scientists employ computer models, which approximate target systems with various levels of skill. Given the imperfection of climate models, how do scientists use simulations to generate knowledge about the causes of observed climate change? Addressing a similar question in the context of biological modelling, Levins (1966) proposed an account grounded in robustness analysis. Recent philosophical discussions dispute the confirmatory power of robustness, raising the question of how the results of computer modelling studies contribute to the body (...) of evidence supporting hypotheses about climate change. Expanding on Staley’s (2004) distinction between evidential strength and security, and Lloyd’s (2015) argument connecting variety-of-evidence inferences and robustness analysis, I address this question with respect to recent challenges to the epistemology robustness analysis. Applying this epistemology to case studies of climate change, I argue that, despite imperfections in climate models, and epistemic constraints on variety-of-evidence reasoning and robustness analysis, this framework accounts for the strength and security of evidence supporting climatological inferences, including the finding that global warming is occurring and its primary causes are anthropogenic. (shrink)

Robustness may increase the degree to which the robust result is indirectly confirmed if it is shown to depend on confirmed rather than disconfirmed assumptions. Although increasing the weight with which existing evidence indirectly confirms it in such a case, robustness may also be irrelevant for confirmation, or may even disconfirm. Whether or not it confirms depends on the available data and on what other results have already been established.

When scientists seek further confirmation of their results, they often attempt to duplicate the results using diverse means. To the extent that they are successful in doing so, their results are said to be robust. This paper investigates the logic of such "robustness analysis" [RA]. The most important and challenging question an account of RA can answer is what sense of evidential diversity is involved in RAs. I argue that prevailing formal explications of such diversity are unsatisfactory. I propose a (...) unified, explanatory account of diversity in RAs. The resulting account is, I argue, truer to actual cases of RA in science; moreover, this account affords us a helpful, new foothold on the logic undergirding RAs. (shrink)

In robustness analysis, hypotheses are supported to the extent that a result proves robust, and a result is robust to the extent that we detect it in diverse ways. But what precise sense of diversity is at work here? In this paper, I show that the formal explications of evidential diversity most often appealed to in work on robustness – which all draw in one way or another on probabilistic independence – fail to shed light on the notion of diversity (...) relevant to robustness analysis. I close by briefly outlining a promising alternative approach inspired by Horwich’s (1982) eliminative account of evidential diversity. (shrink)

Social scientists use many different methods, and there are often substantial disagreements about which method is appropriate for a given research question. In response to this uncertainty about the relative merits of different methods, W. E. B. Du Bois advocated for and applied “methodological triangulation”. This is to use multiple methods simultaneously in the belief that, where one is uncertain about the reliability of any given method, if multiple methods yield the same answer that answer is confirmed more strongly than (...) it could have been by any single method. Against this, methodological purists believe that one should choose a single appropriate method and stick with it. Using tools from voting theory, we show Du Boisian methodological triangulation to be more likely to yield the correct answer than purism, assuming the scientist is subject to some degree of diffidence about the relative merits of the various methods. This holds even when in fact only one of the methods is appropriate for the given research question. (shrink)

I propose a distinct type of robustness, which I suggest can support a confirmatory role in scientific reasoning, contrary to the usual philosophical claims. In model robustness, repeated production of the empirically successful model prediction or retrodiction against a background of independentlysupported and varying model constructions, within a group of models containing a shared causal factor, may suggest how confident we can be in the causal factor and predictions/retrodictions, especially once supported by a variety of evidence framework. I present climate (...) models of greenhouse gas global warming of the 20th Century as an example, and emphasize climate scientists’ discussions of robust models and causal aspects. The account is intended as applicable to a broad array of sciences that use complex modeling techniques. (shrink)

There is increasing attention to the centrality of idealization in science. One common view is that models and other idealized representations are important to science, but that they fall short in one or more ways. On this view, there must be an intermediary step between idealized representation and the traditional aims of science, including truth, explanation, and prediction. Here I develop an alternative interpretation of the relationship between idealized representation and the aims of science. In my view, continuing, widespread idealization (...) calls into question the idea that science aims for truth. I argue that understanding must replace truth as the ultimate epistemic aim of science. Additionally, science has a wide variety aims, epistemic and non-epistemic, and these aims motivate different kinds of scientific products. Finally, I show how these diverse aims---all rather distant from truth---result in the expanded influence of social values on science. (shrink)

I bring out the limitations of four important views of what the target of useful climate model assessment is. Three of these views are drawn from philosophy. They include the views of Elisabeth Lloyd and Wendy Parker, and an application of Bayesian confirmation theory. The fourth view I criticise is based on the actual practice of climate model assessment. In bringing out the limitations of these four views, I argue that an approach to climate model assessment that neither demands too (...) much of such assessment nor threatens to be unreliable will, in typical cases, have to aim at something other than the confirmation of claims about how the climate system actually is. This means, I suggest, that the Intergovernmental Panel on Climate Change ’s focus on establishing confidence in climate model explanations and predictions is misguided. So too, it means that standard epistemologies of science with pretensions to generality, e.g., Bayesian epistemologies, fail to illuminate the assessment of climate models. I go on to outline a view that neither demands too much nor threatens to be unreliable, a view according to which useful climate model assessment typically aims to show that certain climatic scenarios are real possibilities and, when the scenarios are determined to be real possibilities, partially to determine how remote they are. (shrink)

The United Kingdom Climate Impacts Program’s UKCP09 project makes high-resolution forecasts of climate during the 21st century using state of the art global climate models. The aim of this paper is to introduce and analyze the methodology used and then urge some caution. Given the acknowledged systematic errors in all current climate models, treating model outputs as decision relevant probabilistic forecasts can be seriously misleading. This casts doubt on our ability, today, to make trustworthy, high-resolution predictions out to the end (...) of this century. (shrink)

Computer simulations of the formation and evolution of large-scale structure in the universe are integral to the enterprise of modern cosmology. Establishing the reliability of these simulations has been extremely challenging, primarily because of epistemic opacity. In this setting, robustness analysis defined by requiring converging outputs from a diverse ensemble of simulations is insufficient to determine simulation validity. We propose an alternative path of structured code validation that applies eliminative reasoning to isolate and reduce possible sources of error, a potential (...) path that is already being explored by some cosmologists. (shrink)

This article contributes to the revision of the procedure of robustness analysis of mathematical models in epistemic democracy using the systematic review method. It identifies the drawbacks of robustness analysis in epistemic democracy in terms of sample universality and inference from samples with the same results. To exemplify the effectiveness of systematic review, this article conducted a pilot review of diversity trumps ability theorem models, which are mathematical models of deliberation often cited by epistemic democrats. A review of nine models (...) extracted from 352 papers exemplifies the effectiveness of robustness analysis supplemented by systematic review in epistemic democracy. (shrink)

Representationalism—the view that scientific modeling is best understood in representational terms—is the received view in contemporary philosophy of science. Contributions to this literature have focused on a number of puzzles concerning the nature of representation and the epistemic role of misrepresentation, without considering whether these puzzles are the product of an inadequate analytical framework. The goal of this paper is to suggest that this possibility should be taken seriously. The argument has two parts, employing the “can’t have” and “don’t need” (...) tactics drawn from philosophy of mind. On the one hand, I propose that representationalism doesn’t work: different ways to flesh out representationalism create a tension between its ontological and epistemological components and thereby undermine the view. On the other hand, I propose that representationalism is not needed in the first place—a position I articulate based on a pragmatic stance on the success of scientific research and on the feasibility of alternative philosophical frameworks. I conclude that representationalism is untenable and unnecessary, a philosophical dead end. A new way of thinking is called for if we are to make progress in our understanding of scientific modeling. (shrink)

Scientific models consist of fictitious elements and assumptions. Various attempts have been made to answer the question of how a model, which is sometimes viewed as a fiction, can explain or predict the target phenomenon adequately. I examine two accounts of models-as-fictions which are aiming at disentangling the myth of representing the reality by fictional models. I argue that both views have their own weaknesses in spite of many virtues. I propose to re-evaluate the problems of representation from a novel (...) perspective in which some of the model representations can be regarded as fictional representations. I argue that this type of model representation is credible despite being a fictional representation of the reality. (shrink)

Reports of quantitative experimental results often distinguish between the statistical uncertainty and the systematic uncertainty that characterize measurement outcomes. This article discusses the practice of estimating systematic uncertainty in high-energy physics. The estimation of systematic uncertainty in HEP should be understood as a minimal form of quantitative robustness analysis. The secure evidence framework is used to explain the epistemic significance of robustness analysis. However, the empirical value of a measurement result depends crucially not only on the resulting systematic uncertainty estimate, (...) but on the learning aims for which that result will be used. Important conceptual and practical questions regarding systematic uncertainty assessment call for further investigation. _1_ Introduction _2_ Systematic Uncertainty: The Very Idea _3_ Systematic Uncertainty in High-Energy Physics _4_ Methodological Debates in High-Energy Physics _5_ The Secure Evidence Framework _6_ Systematic Uncertainty Assessment as Robustness Analysis _7_ Security and Sensitivity _8_ Conclusion Appendix. (shrink)

The disconnect problem arises wherever there is ongoing and severe discordance between the scientific assessment of a politically relevant issue, and the politics and legislation of said issue. Here, we focus on the disconnect problem as it arises in the case of climate change, diagnosing a failure to respect the necessary tradeoff between authority and autonomy within a public institution like science. After assessing the problematic deployment of scientific authority in this arena, we offer suggestions for how to mitigate climate (...) change’s particular disconnect problem, as well as more general proposals for reforming science advising. (shrink)

In the philosophy of science and epistemology literature, robustness analysis has become an umbrella term that refers to a variety of strategies. One of the main purposes of this paper is to argue that different strategies rely on different criteria for justifications. More specifically, I will claim that: i) robustness analysis differs from de-idealization even though the two concepts have often been conflated in the literature; ii) the comparison of different model frameworks requires different justifications than the comparison of models (...) that differ only for the assumption under test; iii) the replacement of specific assumptions with different ones can encounter specific difficulties in scientific practice. These claims will be supported by a case study in population ecology and a case study in geographical economics. (shrink)

The present paper draws on climate science and the philosophy of science in order to evaluate climate-model-based approaches to assessing climate projections. We analyze the difficulties that arise in such assessment and outline criteria of adequacy for approaches to it. In addition, we offer a critical overview of the approaches used in the IPCC working group one fourth report, including the confidence building, Bayesian and likelihood approaches. Finally, we consider approaches that do not feature in the IPCC reports, including three (...) approaches drawn from the philosophy of science. We find that all available approaches face substantial challenges, with IPCC approaches having as a primary source of difficulty their goal of providing probabilistic assessments. (shrink)

The paper addresses the evaluation of climate models and gives an overview of epistemic uncertainties in climate modeling; the uncertainties concern the data situation as well as the causal behavior of the climate system. In order to achieve reasonable results nonetheless, multimodel ensemble studies are employed in which diverse models simulate the future climate under different emission scenarios. The models jointly deliver a robust range of climate prognoses due to a broad plurality of theories, techniques, and methods in climate research; (...) the range reliably indicates the future development of the global climate. Nevertheless, the uncertainties are widely used by skeptics to challenge the IPCC’s prognoses. Such skeptical allegations can well be distinguished from points of fruitful epistemological criticism: in spite of the enduring range of prognoses, the epistemic uncertainties should not play a role in finding agreements on climate change mitigation. (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)

A question at the intersection of scientific modeling and public choice is how to deal with uncertainty about model predictions. This "high-level" uncertainty is necessarily value-laden, and thus must be treated as irreducibly subjective. Nevertheless, formal methods of uncertainty analysis should still be employed for the purpose of clarifying policy debates. I argue that such debates are best informed by models which integrate objective features with subjective ones. This integrated subjectivism is illustrated with a case study from the literature on (...) monetary policy. The paper concludes with some morals for the use of models in determining climate policy. (shrink)

Robustness concepts are often invoked to manage two obstacles confronting models of ecological systems: complexity and uncertainty. The intuitive idea is that any result derived from many idealized but credible models is thereby made more reliable or is better confirmed. An appropriate basis for this inference has proven elusive. Here, several representations of robustness analysis are vetted, paying particular attention to complex models of ecosystems and the global climate. The claim that robustness is itself confirmatory because robustness analysis employs a (...) Bayesian variety-of-evidence argument is criticized, but recent overwhelming pessimism about robustness may have a silver lining. (shrink)

Here I critically assess an argument put forward by Kuorikoski et al. (Br J Philos Sci, 61(3):541–567, 2010) for the epistemic import of model-based robustness analysis. I show that this argument is not sound since the sort of probabilistic independence on which it relies is unfeasible. By revising the notion of probabilistic independence imposed on the models’ results, I introduce a prima-facie more plausible argument. However, despite this prima-facie plausibility, I show that even this new argument is unsound in most (...) if not all cases of model-based robustness analysis. This I do to demonstrate that the epistemic import of model-based robust analysis cannot be satisfactorily defended on the basis of probabilistic independence. (shrink)

According to an adequacy-for-purpose view, models should be assessed with respect to their adequacy or fitness for particular purposes. Such a view has been advocated by scientists and philosophers...

I examine, from Mayo's severe testing perspective, the case found in the Intergovernmental Panel on Climate Change fourth report for the claim that increases in anthropogenic greenhouse gas concentrations caused most of the post-1950 global warming. My examination begins to provide an alternative to standard, probabilistic assessments of OUR FAULT. It also brings out some of the limitations of variety of evidence considerations in assessing this and other hypotheses about the causes of climate change, and illuminates the epistemology of optimal (...) fingerprinting studies. Finally, it shows that some features of Mayo's perspective should be kept in whatever approach is preferred for assessing hypotheses about the causes of climate change. (shrink)

A dynamical system is called chaotic if small changes to its initial conditions can create large changes in its behavior. By analogy, we call a dynamical system structurally chaotic if small changes to the equations describing the evolution of the system produce large changes in its behavior. Although there are many definitions of “chaos,” there are few mathematically precise candidate definitions of “structural chaos.” I propose a definition, and I explain two new theorems that show that a set of models (...) is structurally chaotic if it contains a chaotic function. I conclude by discussing the relationship between structural chaos and structural stability. (shrink)

Suppose a theory T entails hypotheses H and $$H'$$, neither of which entails the other. A number of authors have argued that a piece of evidence E “indirectly confirms” H when E confirms either T or $$H'$$. But there has been a protracted and unsettled debate about whether indirect confirmation is a sound inference procedure. Skeptics argue that the procedure employs conditions of confirmation that jointly lead to absurdity. Proponents argue that this criticism is unfounded or that its import is (...) exaggerated. I will argue that no side has the story quite right, and some have the story quite wrong. Indirect confirmation, as characterized above, is unsound, and a good chunk of this paper will be concerned with showing why most extant defenses of the procedure err. On the other hand, when certain probabilistic (in)dependence relations hold between T, H, and $$H'$$, indirect confirmation can work, for reasons that trace back to Reichenbach’s principle of the common cause. I illustrate these matters with some contemporary and historical examples, with a particular focus on Kepler’s use of data about mars’s elliptical orbit to justify a claim about earth’s. (shrink)

In the 1960s, theoretical biologist Richard Levins criticised modellers in his own discipline of population biology for pursuing the “brute force” strategy of building hyper-realistic models. Instead of exclusively chasing complexity, Levins advocated for the use of multiple different kinds of complementary models, including much simpler ones. In this paper, I argue that the epistemic challenges Levins attributed to the brute force strategy still apply to state-of-the-art climate models today: they have big appetites for unattainable data, they are limited by (...) computational tractability, and they are incomprehensible to the human modeller. Along the lines Levins described, this uncertainty generates a trade-off between realistic, precise models with predictive power and simple, highly idealised models that facilitate understanding. In addition to building ensembles of highly complex dynamical models, climate modellers can address model uncertainty by comparing models of different types, such as dynamical and data-driven models, and by systematically comparing models at different levels of what climate modellers call the model hierarchy. Despite its age, Levins’ paper remains incredibly insightful and should be considered an important entry into the philosophy of computational modelling. (shrink)

This is the second of three parts of an introduction to the philosophy of climate science. In this second part about modelling climate change, the topics of climate modelling, confirmation of climate models, the limits of climate projections, uncertainty and finally model ensembles will be discussed.

. Through examining a case study of a major fluids modelling code, this paper charts two key properties of software as a material for building models. Scientific software development is characterized by piecemeal growth, and as a code expands, it begins to manifest frustrating properties that provide an important axis of motivation in the laboratory. The first such feature is a tendency towards brittleness. The second is an accumulation of supporting technologies that sometimes cause scientists to express a frustration with (...) the bureaucracy of highly regulated working practices. Both these features are important conditions for the pursuit of research through simulation. (shrink)