In the philosophy of climate science, debate surrounding the issue of variety of evidence has mostly taken the form of attempting to connect these issues in climate science and climate modeling with philosophical accounts of what has come to be known as “robustness analysis.” I argue that an “explanatory” conception of robustness is the best candidate for understanding variety of evidence in climate science. I apply the analysis to both examples of model agreement, as well at to the convergence of (...) evidence from both model and non-model sources. (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)

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)

The epistemology of studies addressing questions about historical and prehistorical phenomena is a subject of increasing discussion among philosophers of science. A related field of inquiry that has yet to be connected to this topic is the epistemology of climate science. Branching these areas of research, I show how variety-of-evidence reasoning accounts for scientific inferences about the past by detailing a case study in paleoclimate reconstruction. This analysis aims to clarify the logic of historical inquiry in general and, by focusing (...) on a case study about climate change, it offers an epistemic account of a particular discipline that is of environmental and social importance. (shrink)

Model transfer refers to the observation that particular model structures are used across multiple distinct scientific domains. This paper puts forward an account to explain the inter-domain transfer of model structures. Central in the account is the role of validation criteria in determining whether a model is considered to be useful by practitioners. Validation criteria are points of reference to which model correctness for a particular purpose is assessed. I argue that validation criteria can be categorized as being mathematical, theoretical (...) or phenomenological in nature. Model transfer is explained by overlap in validation criteria between scientific domains. Particular emphasis is placed on overlap between phenomenological criteria. Overlap in phenomenological criteria can be explained through the notion of universal patterns. Universal patterns are abstract structures that can be made to refer to multiple distinct phenomena when coupled with phenomena-specific empirical content. I present the case study of the Yule Process, in which universal patterns play a crucial role in explaining model transfer. This paper provides an account of model transfer that stays close to modelling practice and expands existing accounts by introducing the notion of universal patterns. (shrink)

One important strategy for dealing with error in our methods is triangulation, or the use multiple methods to investigate the same object. Current accounts of triangulation assume that its primary function is to provide a confirmatory boost to hypotheses beyond what confirmation of each method alone could produce. Yet, researchers often use multiple methods to examine new constructs about which they are uncertain. For example, social psychologists use multiple indirect measures to provide convergent evidence about implicit attitudes, but how to (...) characterize implicit attitudes is an open question. To make sense of triangulation under uncertainty about constructs, I suggest two changes: first, triangulation can serve multiple epistemic functions, including some that are non-confirmatory, and second, researchers should assess the epistemic risk in claims about evidence and the acceptance/rejection of hypotheses. (shrink)

This article starts by defining instrumentalized knowledge (IK) as the practice of selectively valuing some set of reliable beliefs for the promotion of a more generally false or unreliable worldview. IK is typically exploited by conspiratorial echo chambers, which display systematic distrust and opposition towards mainstream epistemic authorities. We argue that IK is problematic in that it violates core epistemic virtues, and this gives rise to clear and present harms when abused by said echo chambers. Yet, we contend, mainstream epistemic (...) authorities (MEAs) are also complicit in practices resembling IK; we refer to these practices as instrumentalized knowledge* (IK*). IK* differs from IK in that the selective valuing of beliefs corresponds to a ”reliable” worldview, namely, one independently verified by the relevant epistemic experts. We argue that IK*, despite its apparent veracity, is also problematic, as it violates the same epistemic virtues as IK despite its aim of promoting true beliefs. This, we argue, leads it to being counterproductive in its goal of producing knowledge for the sake of the pursuit of truth, thereby raising the question of what distinguishes virtuous from nonvirtuous practices of instrumentalized knowledge. In an attempt to avoid this violation and to distinguish IK* from IK, we investigate whether and how IK* could still be epistemically virtuous. We conclude that IK* can be virtuous if its goal is to produce understanding as opposed to mere knowledge. (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)

Amalgamating evidence from heterogeneous sources and across levels of inquiry is becoming increasingly important in many pure and applied sciences. This special issue provides a forum for researchers from diverse scientific and philosophical perspectives to discuss evidence amalgamation, its methodologies, its history, its pitfalls, and its potential. We situate the contributions therein within six themes from the broad literature on this subject: the variety-of-evidence thesis, the philosophy of meta-analysis, the role of robustness/sensitivity analysis for evidence amalgamation, its bearing on questions (...) of extrapolation and external validity of experiments, its connection with theory development, and its interface with causal inference, especially regarding causal theories of cancer. (shrink)

Multiple biological groups, such as ant colonies, appear to have a noteworthy inefficiency: they contain vast amounts of redundant members that are not strictly needed to maintain the group. Philosophers and biologists have proposed that such inefficiency is illusory because redundancy enhances the resilience of groups when living under harsh conditions. Still, this proposal is unsatisfactory in different respects. First, it is too vague to account for when redundancy is selectively advantageous. Furthermore, it overlooks cases in which redundancy fails to (...) increase the resilience of groups. This paper offers an account of group redundancy that addresses these difficulties. Specifically, it advances the hypothesis that the mere presence of harsh conditions is not what drives the evolution of redundancy; rather, it is the fact organisms are often unable to predict when they will face harsh conditions. Redundancy enables groups to properly respond to unpredictable circumstances without resorting to the unreliable detection systems of their members. A better understanding of the phenomenon of redundancy is likely to impact other key issues in philosophy of science, including the evolution of cooperation and transitions in individuality, and the role of redundancy in complex systems, be they biological or not. (shrink)

Lisciandra poses a challenge for robustness analysis as applied to economic models. She argues that substituting tractability assumptions risks altering the main mathematical structure of the model, thereby preventing the possibility of meaningfully evaluating the same model under different assumptions. In such cases RA is argued to be inapplicable. However, Lisciandra is mistaken to take the goal of RA as keeping the mathematical properties of tractability assumptions intact. Instead, RA really aims to keep the modeling component while varying the corresponding (...) mathematical formulation. Thus, her argument concerning whether the associated mathematical properties of certain assumptions can be kept intact is irrelevant to the success of RA. Furthermore, we explicate and develop Lloyd’s account of “model robustness” to provide solutions to Lisciandra’s challenges. Our solutions are, namely, error analysis and independent empirical support. We conclude that although complex economic models do face potential dangers, there are solutions and robustness analysis need not be given up.. (shrink)

I examine error diagnosis (model-model disagreement) in climate model intercomparisons including its difficulties, fruitful examples, and prospects for streamlining error diagnosis. I suggest that features of climate model intercomparisons pose a more significant challenge for error diagnosis than do features of individual model construction and complexity. Such features of intercomparisons include, e.g., the number of models involved, how models from different institutions interrelate, and what scientists know about each model. By considering numerous examples in the climate modeling literature, I distill (...) general strategies (e.g., employing physical reasoning and using dimension reduction techniques) used to diagnose model error. Based on these examples, I argue that an error repertoire could be beneficial for improving error diagnosis in climate modeling, although constructing one faces several difficulties. Finally, I suggest that the practice of error diagnosis demonstrates that scientists have a tacit-yet-working understanding of their models which has been under-appreciated by some philosophers. (shrink)

In this paper we consider some questions surrounding whether or not regional climate models “add value,” a controversial issue in climate science today. We highlight some objections frequently made about regional climate models both within and outside the community of modelers, including several claims that regional climate models do not “add value.” We show that there are a number of issues involved in the latter claims, the primary ones centering on the fact that different research questions are being pursued by (...) the modelers making the complaints against regional climate models. Further issues focus on historical deficiencies of particular—but not generalizable—failures of individual regional models. We provide tools to sort out these different research questions and particular failures, and to improve communication and understanding surrounding added value in climate modeling and philosophy of climate science. (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)

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.

Varied evidence confirms more strongly than less varied evidence, ceteris paribus. This epistemological Variety of Evidence Thesis enjoys widespread intuitive support. We put forward a novel explication of one notion of varied evidence and the Variety of Evidence Thesis within Bayesian models of scientific inference by appealing to measures of entropy. Our explication of the Variety of Evidence Thesis holds in many of our models which also pronounce on disconfirmatory and discordant evidence. We argue that our models pronounce rightly. Against (...) a backdrop of failures of the Variety of Evidence Thesis, the intuitive case for the Variety of Evidence Thesis emerges strengthened. Our models do however not support the general case for the thesis since our explication of it fails to hold in certain cases. The parameter space of this failure is explored and an explanation for the failure is offered. (shrink)

This discussion centers on two desiderata: the role of measurement in information-gathering and physical interaction in scientific practice. By taking inspiration from van Fraassen’s view, we present a methodological account of perspectival measurement that addresses empirical practice where there is complex intervention, disagreeing results, and limited theory. The specific aim of our account is to provide a methodological prescription for developing measurement processes in the context of limited theory. The account should be useful to philosophers of science, who are interested (...) in the intersection between representation and intervention; scientists, who are interested in methodological suggestions for theory-development and reliability; and interdisciplinary researchers, who are interested in the intersections between the pandemic, built environments, and social processes. We apply the process-view of measurement to COVID-19, specifically, measuring replication in the SARS-CoV-2 virus. The aim is to show that our account tracks key elements—manipulation/intervention, independence, invariance, and theory-development—by organizing unfolding measurement processes. Additionally, we use our account to make prescriptive suggestions for measurement practice in the COVID-19 context by discussing the need to broaden measurement perspective on interaction, manipulation, and production. (shrink)

Philosophers of science have offered various accounts of climate model evaluation which have largely centered on model-fit assessment. However, despite the wide-spread prevalence of process-based evaluation in climate science practice, this sort of model evaluation has been undertheorized by philosophers of science. In this paper, I aim to expand this narrow philosophical view of climate model evaluation by providing a philosophical account of process evaluation that is rooted in a close examination of scientific practice. I propose dynamical adequacy as a (...) metric by which scientists test and evaluate models that represent and produce key regional climate processes and features. I argue that process-based evaluation confirms the adequacy of a regional climate model for simulating and predicting future changes of a specific regional climate feature. I offer a case study of how, in practice, scientists establish the reliability of model projections by assessing the dynamical adequacy of such models. I also show how process-based evaluation mitigates some well-known shortcomings of model-fit assessment and supports the adequacy and reliability of climate model projections against philosophical objections, like confirmational holism. Such adequacy is especially important for decision makers who need reliable regional model projections to guide their climate change mitigation and adaptation policies. (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)

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...

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)

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)

Writing collectively as the Oscar Seminar in 2008, John Pollock and several colleagues advance an objectivist argument for a 1/3 solution to the Sleeping Beauty problem. In 2011, Joel Pust raises a serious objection to their argument to which Paul D. Thorn, a member of the Oscar Seminar, offers a subtle reply. I argue that the Oscar Seminar s argument for 1/3 is unsound. I do not, however, defend Pust’s objection. Rather I develop a new objection, one that is not (...) threatened by the considerations to which Thorn appeals in his reply to Pust. (shrink)

A number of philosophers of science have argued that there are important differences between robustness in modeling and experimental contexts, and—in particular—many of them have claimed that the former is non-confirmatory. In this paper, I argue for the opposite conclusion: robust hypotheses are confirmed under conditions that do not depend on the differences between and models and experiments—that is, the degree to which the robust hypothesis is confirmed depends on precisely the same factors in both situations. The positive argument turns (...) on the fact that confirmation theory doesn’t recognize a difference between different sources of evidence. Most of the paper is devoted to rebutting various objections designed to show that it should. I end by explaining why philosophers of science have (often) gone wrong on this point. (shrink)

Inference to the Best Explanation (IBE) is widely criticized for being an unreliable form of ampliative inference – partly because the explanatory hypotheses we have considered at a given time may all be false, and partly because there is an asymmetry between the comparative judgment on which an IBE is based and the absolute verdict that IBE is meant to license. In this paper, I present a further reason to doubt the epistemic merits of IBE and argue that it motivates (...) moving to an inferential pattern in which IBE emerges as a degenerate limiting case. Since this inferential pattern is structurally similar to an argumentative strategy known as Inferential Robustness Analysis (IRA), it effectively combines the most attractive features of IBE and IRA into a unified approach to non-deductive inference. (shrink)

Some authors claim that minimal models have limited epistemic value (Fumagalli, 2016; Grüne-Yanoff, 2009a). Others defend the epistemic benefits of modelling by invoking the role of robustness analysis for hypothesis confirmation (see, e.g., Levins, 1966; Kuorikoski et al., 2010) but such arguments find much resistance (see, e.g., Odenbaugh & Alexandrova, 2011). In this paper, we offer a Bayesian rationalization and defence of the view that robustness analysis can play a confirmatory role, and thereby shed light on the potential of minimal (...) models for hypothesis confirmation. We illustrate our argument by reference to a case study from macroeconomics. At the same time, we also show that there are cases in which robustness analysis is detrimental to confirmation. We characterize these cases and link them to recent investigations on evidential variety (Landes, 2020b, 2021; Osimani and Landes, forthcoming). We conclude that robustness analysis over minimal models can confirm, but its confirmatory value depends on concrete circumstances. (shrink)

The paper discusses modelling uncertainties in climate models and how they can be addressed based on physical principles as well as based on how the models perform in light of empirical data. We ar...

The 2017 codetection of electromagnetic radiation and gravitational waves was the first of its kind and marked the beginning of multimessenger astronomy. But this event has been treated within recent literature as something of an end as well. The 2017 detection is often regarded as an instance of falsification for all theories of modified gravity which postulate gravitational waves propagate along separate geodesics from electromagnetic radiation, perhaps most notably Jacob Bekenstein’s Tensor-Vector-Scalar gravity. I critically examine this explicit endorsement of falsification (...) by astronomers and astrophysicists. While the current state of multimetric modified gravity theories is dim, recent developments in multimessenger observation do not offer a deductive falsification. Rather such evidence should be regarded as a corroboration of the null hypothesis, following Deborah Mayo's error-statistical account. (shrink)

A central component to scientific practice is the construction and use of scientific models. Scientists believe that the success of a model justifies making claims that go beyond the model itself. However, philosophical analysis of models suggests that drawing inferences about the world from successful models is more complex. In this dissertation I develop a framework that can help disentangle the related strands of evaluation of model success, model extendibility, and the ability to draw ampliative inferences about the world from (...) models. I present and critically assess two leading accounts of model assessment, arguing that neither is sufficient to provide a complete understanding of model evaluation. I introduce a more powerful framework incorporating elements of the two views, which can help answer these three questions: What is the target of evaluation in model assessment? How does that evaluation proceed? What licenses us in making inferences about the real world, based on the evaluation of our models as successful? The framework identifies two distinct targets of model evaluation: representational similarity between the model and target system, and the adequacy of the model as a tool to answer questions. Both assessments must be relativized to a purpose, of which there are three general kinds: descriptive, predictive, and explanatory. These purposes differ in the way they inform the similarity relation, which is relevant for the similarity assessment, and the output they produce, which is relevant for the adequacy assessment. Any model can be assessed relative to any purpose, however a model encodes certain decisions made during the model’s construction, which impact its ability to be applied to a new purpose or new domain. My framework shows that extending a model, and drawing inferences from it, depends on its representational similarity. I apply this framework to several examples taken from astrophysics showing in detail how it can help illuminate the structure of the models, as well as make the justification for inferences made from them clear. The final chapter is a detailed analysis of a contemporary debate surrounding the use of models in astrophysics, between proponents of MOND and the standard ΛCDM model. (shrink)

The goal of this dissertation is to contribute to the epistemology of science by addressing a set of related questions arising from current discussions in the philosophy and science of climate change: (1) Given the imperfection of computer models, how do they provide information about large and complex target systems? (2) What is the relationship between consilient reasoning and robust evidential support in the production of scientific knowledge? (3) Does taking the mean of a set of model outputs provide epistemic (...) advantages over using the output of a single ‘best model’? Synthesizing research in philosophy and science, the thesis analyzes connections among consilient inductions, robustness analysis, and the aggregation of various sources of evidence, including computer simulations, by investigating case studies of climate change that exemplify the strength of consilient reasoning and the security of robust evidential support. It also explains the rationale and epistemic conditions for improving estimates by averaging multiple estimates, comparing a simple case of averaging estimates to practices in multi-model ensemble studies. I argue: (A) the concepts of consilience and robustness account for the strength and security of inferences that rely on imperfect computer modelling methods, (B) consilient reasoning is conducive to attaining robust evidential support, and (C) an analogy can explain why averaging the outputs of multiple models can improve estimates of a target system, given that conditions of model independence, skill and unequal weighting are taken into account. (shrink)

Experimental design is one aspect of a scientific method. A well-designed, properly conducted experiment aims to control variables in order to isolate and manipulate causal effects and thereby maximize internal validity, support causal inferences, and guarantee reliable results. Traditionally employed in the natural sciences, experimental design has become an important part of research in the social and behavioral sciences. Experimental methods are also endorsed as the most reliable guides to policy effectiveness. Through a discussion of some of the central concepts (...) associated with experimental design, including controlled variation and randomization, this chapter will provide a summary of key ethical issues that tend to arise in experimental contexts. In addition, by exploring assumptions about the nature of causation and by analyzing features of causal relationships, systems, and inferences in social contexts, this chapter will summarize the ways in which experimental design can undermine the integrity of not only social and behavioral research but policies implemented on the basis of such research. (shrink)

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)

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