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)

In his groundbreaking book, Against Coherence (2005), Erik Olsson presents an ingenious impossibility theorem that appears to show that there is no informative relationship between probabilistic measures of coherence and higher likelihood of truth. Although Olsson's result provides an important insight into probabilistic models of epistemological coherence, the scope of his negative result is more limited than generally appreciated. The key issue is the role conditional independence conditions play within the witness testimony model Olsson uses to establish his result. Olsson (...) maintains that his witness model yields charitable ceteris paribus conditions for any theory of probabilistic coherence. Not so. In fact, Olsson's model, like Bayesian witness models in general, selects a peculiar class of models that are in no way representative of the range of options available to coherence theorists. Recent positive results suggest that there is a way to develop a formal theory of coherence after all. Further, although Bayesian witness models are not conducive to the truth, they are conducive to reliability. (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)

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)

Coherentism maintains that coherent beliefs are more likely to be true than incoherent beliefs, and that coherent evidence provides more confirmation of a hypothesis when the evidence is made coherent by the explanation provided by that hypothesis. Although probabilistic models of credence ought to be well-suited to justifying such claims, negative results from Bayesian epistemology have suggested otherwise. In this essay we argue that the connection between coherence and confirmation should be understood as a relation mediated by the causal relationships (...) among the evidence and a hypothesis, and we offer a framework for doing so by fitting together probabilistic models of coherence, confirmation, and causation. We show that the causal structure among the evidence and hypothesis is sometimes enough to determine whether the coherence of the evidence boosts confirmation of the hypothesis, makes no difference to it, or even reduces it. We also show that, ceteris paribus, it is not the coherence of the evidence that boosts confirmation, but rather the ratio of the coherence of the evidence to the coherence of the evidence conditional on a hypothesis. (shrink)

This chapter identifies conditions under which robust predictive modeling results have special epistemic significance—related to truth, confidence, and security—and considers whether those conditions are met in the context of climate modeling today. The findings are disappointing. When today’s climate models agree that an interesting hypothesis about future climate change is true, it cannot be inferred, via the arguments considered here anyway, that the hypothesis is likely to be true, nor that confidence in the hypothesis should be significantly increased, nor that (...) a claim to have evidence for the hypothesis is now more secure. In some other modeling contexts, the prospects for such arguments are brighter. (shrink)

Can computer simulation results be evidence for hypotheses about real-world systems and phenomena? If so, what sort of evidence? Can we gain genuinely new knowledge of the world via simulation? I argue that evidence from computer simulation is aptly characterized as higher-order evidence: it is evidence that other evidence regarding a hypothesis about the world has been collected. Insofar as particular epistemic agents do not have this other evidence, it is possible that they will gain genuinely new knowledge of the (...) world via simulation. I illustrate with examples inspired by uses of simulation in meteorology and astrophysics. (shrink)

A number of recent discussions comparing computer simulation and traditional experimentation have focused on the significance of “materiality.” I challenge several claims emerging from this work and suggest that computer simulation studies are material experiments in a straightforward sense. After discussing some of the implications of this material status for the epistemology of computer simulation, I consider the extent to which materiality (in a particular sense) is important when it comes to making justified inferences about target systems on the basis (...) of experimental results. (shrink)

In this essay, I argue that uneliminated idealizations pose a serious problem for scientific realism. I consider one method for “de-idealizing” models—robustness analysis. However, I argue that unless idealizations are eliminated from an idealized theory and robustness analysis need not do that, scientists are not justified in believing that the theory is true. I consider one example of modeling from the biological sciences that exemplifies the problem.

Theoretical biology and economics are remarkably similar in their reliance on mathematical models, which attempt to represent real world systems using many idealized assumptions. They are also similar in placing a great emphasis on derivational robustness of modeling results. Recently philosophers of biology and economics have argued that robustness analysis can be a method for confirmation of claims about causal mechanisms, despite the significant reliance of these models on patently false assumptions. We argue that the power of robustness analysis has (...) been greatly exaggerated. It is best regarded as a method of discovery rather than confirmation. (shrink)

Andrew Wayne discusses some recent attempts to account, within a Bayesian framework, for the "common methodological adage" that "diverse evidence better confirms a hypothesis than does the same amount of similar evidence". One of the approaches considered by Wayne is that suggested by Howson and Urbach and dubbed the "correlation approach" by Wayne. This approach is, indeed, incomplete, in that it neglects the role of the hypothesis under consideration in determining what diversity in a body of evidence is relevant diversity. (...) In this paper, it is shown how this gap can be filled, resulting in a more satisfactory account of the evidential role of diversity of evidence. In addition, it is argued that Wayne's criticism of the correlation approach does not indicate a serious flaw in the approach. (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)

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)

Odenbaugh and Alexandrova provide a challenging critique of the epistemic benefits of robustness analysis, singling out for particular criticism the account we articulated in Kuorikoski et al.. Odenbaugh and Alexandrova offer two arguments against the confirmatory value of robustness analysis: robust theorems cannot specify causal mechanisms and models are rarely independent in the way required by robustness analysis. We address Odenbaugh and Alexandrova’s criticisms in order to clarify some of our original arguments and to shed further light on the properties (...) of robustness analysis and its epistemic rationale. (shrink)

We claim that the process of theoretical model refinement in economics is best characterised as robustness analysis: the systematic examination of the robustness of modelling results with respect to particular modelling assumptions. We argue that this practise has epistemic value by extending William Wimsatt's account of robustness analysis as triangulation via independent means of determination. For economists robustness analysis is a crucial methodological strategy because their models are often based on idealisations and abstractions, and it is usually difficult to tell (...) which idealisations are truly harmful. (shrink)

Michael Weisberg has argued that robustness analysis is useful in evaluating both scientific models and their implications and that robustness analysis comes in three types that share their form and aim. We argue for three cautionary claims regarding Weisberg's reconstruction: robustness analysis may be of limited or no value in evaluating models and their implications; the unificatory reconstruction conceals that the three types of robustness differ in form and role; there is no confluence of types of robustness. We illustrate our (...) central first claim with a case study: the application of Lotka-Volterra models to technology diffusion. (shrink)

Confirmation in evolutionary biology depends on what biologists take to be the genuine rivals. Investigating what constrains the scope of biological possibility provides part of the story: explaining how possible helps determine what counts as a genuine rival and thus informs confirmation. To clarify the criteria for genuine rivalry I distinguish between global and local constraints on biological possibility, and offer an account of how-possibly explanation. To sharpen the connection between confirmation and explaining how possible I discuss the view that (...) formal inquiry can provide a kind of confirmation-theoretic support for evolutionary models, and offer an example of how-possibly explanation interacting with testing practice. (shrink)

Confirmation in evolutionary biology depends on what biologists take to be the genuine rivals. Investigating what constrains the scope of biological possibility provides part of the story: explaining how possible helps determine what counts as a genuine rival and thus informs confirmation. To clarify the criteria for genuine rivalry I distinguish between global and local constraints on biological possibility, and offer an account of how-possibly explanation. To sharpen the connection between confirmation and explaining how possible I discuss the view that (...) formal inquiry can provide a kind of confirmation-theoretic support for evolutionary models, and offer an example of how-possibly explanation interacting with testing practice. (shrink)

Pierre Duhem’s influential argument for holism relies on a view of the role that background theory plays in testing: according to this still common account of “auxiliary hypotheses,” elements of background theory serve as truth-apt premises in arguments for or against a hypothesis. I argue that this view is mistaken. Rather than serving as truth-apt premises in arguments, auxiliary hypotheses are employed as “epistemic tools”: instruments that perform specific tasks in connecting our theoretical questions with the world but that are (...) not premises in arguments. On the resulting picture, the acceptability of an auxiliary hypothesis depends not on its truth but on contextual factors such as the task or purpose it is put to and the other tools employed alongside it. (shrink)

It is often claimed that scientists can obtain new knowledge about nature by running computer simulations. How is this possible? I answer this question by arguing that computer simulations are arguments. This view parallels Norton’s argument view about thought experiments. I show that computer simulations can be reconstructed as arguments that fully capture the epistemic power of the simulations. Assuming the extended mind hypothesis, I furthermore argue that running the computer simulation is to execute the reconstructing argument. I discuss some (...) objections and reject the view that computer simulations produce knowledge because they are experiments. I conclude by comparing thought experiments and computer simulations, assuming that both are arguments. (shrink)

Computer simulations and experiments share many important features. One way of explaining the similarities is to say that computer simulations just are experiments. This claim is quite popular in the literature. The aim of this paper is to argue against the claim and to develop an alternative explanation of why computer simulations resemble experiments. To this purpose, experiment is characterized in terms of an intervention on a system and of the observation of the reaction. Thus, if computer simulations are experiments, (...) either the computer hardware or the target system must be intervened on and observed. I argue against the first option using the non-observation argument, among others. The second option is excluded by e.g. the over-control argument, which stresses epistemological differences between experiments and simulations. To account for the similarities between experiments and computer simulations, I propose to say that computer simulations can model possible experiments and do in fact often do so. (shrink)