Explanation,understanding, and unrealistic models

How can false models be explanatory? And how can they help us to understand the way the world works? Sometimes scientists have little hope of building models that approximate the world they observe. Even in such cases, I argue, the models they build can have explanatory import. The basic idea is that scientists provide causal explanations of why the regularity entailed by an abstract and idealized model fails to obtain. They do so by relaxing some of its unrealistic assumptions. This method of ‘explanation by relaxation’ captures the explanatory import of some important models in economics. I contrast this method with the accounts that Daniel Hausman and Nancy Cartwright have provided of explanation in economics. Their accounts are unsatisfactory because they require that the economic model regularities obtain, which is rarely the case. I go on to argue that counterfactual regularities play a central role in achieving ‘understanding by relaxation.’ This has a surprising implication for the relation between explanation and understanding: Achieving scientific understanding does not require the ability to explain observed regularities.     

Values in early-stage climate engineering: The ethical implications of “doing the research”

Calls for research on climate engineering have increased in the last two decades, but there remains widespread agreement that many climate engineering technologies (in particular, forms involving global solar radiation management) present significant ethical risks and require careful governance. However, proponents of research argue, ethical restrictions on climate engineering research should not be imposed in early-stage work like in silico modeling studies. Such studies, it is argued, do not pose risks to the public, and the knowledge gained from them is necessary for assessing the risks and benefits of climate engineering technologies. I argue that this position, which I call the “broad research-first” stance, cannot be maintained in light of the entrance of nonepistemic values in climate modeling. I analyze the roles that can be played by nonepistemic political and ethical values in the design, tuning, and interpretation of climate models. Then, I argue that, in the context of early-stage climate engineering research, the embeddedness of values will lead to value judgments that could harm stakeholder groups or impose researcher values on non-consenting populations. I conclude by calling for more robust reflection on the ethics and governance of early-stage climate engineering research.     

Newton’s “satis est”: A new explanatory role for laws

In this paper I argue that Newton’s stance on explanation in physics was enabled by his overall methodology and that it neither committed him to embrace action at a distance nor to set aside philosophical and metaphysical questions. Rather his methodology allowed him to embrace a non-causal, yet non-inferior, kind of explanation. I suggest that Newton holds that the theory developed in the Principia provides a genuine explanation, namely a law-based one, but that we also lack something explanatory, namely a causal account of the explanandum. Finally, I argue that examining what it takes to have law-based explanation in the face of agnosticism about the causal process makes it possible to recast the debate over action at a distance between Leibniz and Newton as empirically and methodologically motivated on both sides.     

The epistemology of climate models and some of its implications for climate science and the philosophy of science

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 (IPCC׳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.     

Mechanisms,the interventionist theory,and the ability to use causal relationships

In the area of social science, in particular, although we have developed methods for reliably discovering the existence of causal relationships, we are not very good at using these to design effective social policy. Cartwright argues that in order to improve our ability to use causal relationships, it is essential to develop a theory of causation that makes explicit the connections between the nature of causation, our best methods for discovering causal relationships, and the uses to which these are put. I argue that Woodward's interventionist theory of causation is uniquely suited to meet Cartwright's challenge. More specifically, interventionist mechanisms can provide the bridge from ‘hunting causes’ to ‘using them’, if interventionists (i) tell us more about the nature of these mechanisms, and (ii) endorse the claim that it is these mechanisms—or whatever constitutes them—that make causal claims true. I illustrate how having an understanding of interventionist mechanisms can allow us to put causal knowledge to use via a detailed example from organic chemistry.     

Linear structures,causal sets and topology

Causal set theory and the theory of linear structures (which has recently been developed by Tim Maudlin as an alternative to standard topology) share some of their main motivations. In view of that, I raise and answer the question how these two theories are related to each other and to standard topology. I show that causal set theory can be embedded into Maudlin׳s more general framework and I characterise what Maudlin׳s topological concepts boil down to when applied to discrete linear structures that correspond to causal sets. Moreover, I show that all topological aspects of causal sets that can be described in Maudlin׳s theory can also be described in the framework of standard topology. Finally, I discuss why these results are relevant for evaluating Maudlin׳s theory. The value of this theory depends crucially on whether it is true that (a) its conceptual framework is as expressive as that of standard topology when it comes to describing well-known continuous as well as discrete models of spacetime and (b) it is even more expressive or fruitful when it comes to analysing topological aspects of discrete structures that are intended as models of spacetime. On one hand, my theorems support (a). The theory is rich enough to incorporate causal set theory and its definitions of topological notions yield a plausible outcome in the case of causal sets. On the other hand, the results undermine (b). Standard topology, too, has the conceptual resources to capture those topological aspects of causal sets that are analysable within Maudlin׳s framework. This fact poses a challenge for the proponents of Maudlin׳s theory to prove it fruitful.     

Generative models: Human embryonic stem cells and multiple modeling relations

Model organisms are at once scientific models and concrete living things. It is widely assumed by philosophers of science that (1) model organisms function much like other kinds of models, and (2) that insofar as their scientific role is distinctive, it is in virtue of representing a wide range of biological species and providing a basis for generalizations about those targets. This paper uses the case of human embryonic stem cells (hESC) to challenge both assumptions. I first argue that hESC can be considered model organisms, analogous to classic examples such as Escherichia coli and Drosophila melanogaster. I then discuss four contrasts between the epistemic role of hESC in practice, and the assumptions about model organisms noted above. These contrasts motivate an alternative view of model organisms as a network of systems related constructively and developmentally to one another. I conclude by relating this result to other accounts of model organisms in recent philosophy of science.     

Process tracing in political science: What's the story?

Methodologists in political science have advocated for causal process tracing as a way of providing evidence for causal mechanisms. Recent analyses of the method have sought to provide more rigorous accounts of how it provides such evidence. These accounts have focused on the role of process tracing for causal inference and specifically on the way it can be used with case studies for testing hypotheses. While the analyses do provide an account of such testing, they pay little attention to the narrative elements of case studies. I argue that the role of narrative in case studies is not merely incidental. Narrative does cognitive work by both facilitating the consideration of alternative hypotheses and clarifying the relationship between evidence and explanation. I consider the use of process tracing in a particular case (the Fashoda Incident) in order to illustrate the role of narrative. I argue that process tracing contributes to knowledge production in ways that the current focus on inference tends to obscure.     

Hobbes on natural philosophy as “True Physics” and mixed mathematics

In this paper, I offer an alternative account of the relationship of Hobbesian geometry to natural philosophy by arguing that mixed mathematics provided Hobbes with a model for thinking about it. In mixed mathematics, one may borrow causal principles from one science and use them in another science without there being a deductive relationship between those two sciences. Natural philosophy for Hobbes is mixed because an explanation may combine observations from experience (the ‘that’) with causal principles from geometry (the ‘why’). My argument shows that Hobbesian natural philosophy relies upon suppositions that bodies plausibly behave according to these borrowed causal principles from geometry, acknowledging that bodies in the world may not actually behave this way. First, I consider Hobbes's relation to Aristotelian mixed mathematics and to Isaac Barrow's broadening of mixed mathematics in Mathematical Lectures (1683). I show that for Hobbes maker's knowledge from geometry provides the ‘why’ in mixed-mathematical explanations. Next, I examine two explanations from De corpore Part IV: (1) the explanation of sense in De corpore 25.1-2; and (2) the explanation of the swelling of parts of the body when they become warm in De corpore 27.3. In both explanations, I show Hobbes borrowing and citing geometrical principles and mixing these principles with appeals to experience.     

The problem of grounding natural modality in Kant's account of empirical laws of nature

One of the central problems of Kant's account of the empirical laws of nature is: What grounds their necessity? In this article I discuss the three most important lines of interpretation and suggest a novel version of one of them. While the first interpretation takes the transcendental principles as the only sources of the empirical laws' necessity, the second interpretation takes the systematicity of the laws to guarantee their necessity. It is shown that both views involve serious problems. The third interpretation, the “causal powers interpretation”, locates the source of the laws' necessity in the properties of natural objects. Although the second and third interpretations seem incompatible, I analyse why Kant held both views and I argue that they can be reconciled, because the metaphysical grounding project of the laws' necessity is accounted for by Kant's causal powers account, while his best system account explains our epistemic access to the empirical laws. If, however, causal powers are supposed to fulfil the grounding function for the laws' natural modality, then I suggest that a novel reading of the causal powers interpretation should be formulated along the lines of a genuine dispositionalist conception of the laws of nature.     

Modelling and representing: An artefactual approach to model-based representation

The recent discussion on scientific representation has focused on models and their relationship to the real world. It has been assumed that models give us knowledge because they represent their supposed real target systems. However, here agreement among philosophers of science has tended to end as they have presented widely different views on how representation should be understood. I will argue that the traditional representational approach is too limiting as regards the epistemic value of modelling given the focus on the relationship between a single model and its supposed target system, and the neglect of the actual representational means with which scientists construct models. I therefore suggest an alternative account of models as epistemic tools. This amounts to regarding them as concrete artefacts that are built by specific representational means and are constrained by their design in such a way that they facilitate the study of certain scientific questions, and learning from them by means of construction and manipulation.     

Intervening on time derivatives

Interventionism analyses causal influence in terms of correlations of changes under a distribution of interventions. But the correspondence between correlated changes and causal influence is not obvious. I probe its plausibility with a problem-case involving variables related as time derivative (velocity) to integral (position), such that the latter variable must change given an intervention on the former unless dependencies are introduced among the testing and controlling interventions. Under the orthodox criteria such interventions will fail to be appropriate for causal analysis. I consider various alternatives, including permitting control interventions to be chancy, restricting the available models and mitigating variation of off-path variables. None of these work. I then present a fourth suggestion which modifies the interventionist criteria in order to permit interventions which can influence other variables than just their own targets. The correspondence between correlated changes and causal influence can thereby saved when dependencies are introduced among such interventions. This modification and the required dependencies, I argue, are perfectly in line with practice and may also assist in a wider class of cases.     

Folk judgments of causation

Experimental studies suggest that people’s ordinary causal judgments are affected not only by statistical considerations but also by moral considerations. One way to explain these results would be to construct a model according to which people are trying to make a purely statistical judgment but moral considerations somehow distort their intuitions. The present paper offers an alternative perspective. Specifically, the author proposes a model according to which the very same underlying mechanism accounts for the influence of both statistical and moral considerations. On this model, it appears that ordinary causal judgments are quite different from the sorts of judgments one might find in the systematic sciences.     

Value management and model pluralism in climate science

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.     

Where explanation ends: Understanding as the place the spade turns in the social sciences

Explanations implicitly end with something that makes sense, and begin with something that does not make sense. A statistical relationship, for example, a numerical fact, does not make sense; an explanation of this relationship adds something, such as causal information, which does make sense, and provides an endpoint for the sense-making process. Does social science differ from natural science in this respect? One difference is that in the natural sciences, models are what need “understanding.” In the social sciences, matters are more complex. There are models, such as causal models, which need to be understood, but also depend on background knowledge that goes beyond the model and the correlations that make it up, which produces a regress. The background knowledge is knowledge of in-filling mechanisms, which are normally made up of elements that involve the direct understanding of the acting and believing subjects themselves. These models, and social science explanations generally, are satisfactory only when they end the regress in this kind of understanding or use direct understanding evidence to decide between alternative mechanism explanations.     

Saving the mutual manipulability account of constitutive relevance

Constitutive mechanistic explanations are said to refer to mechanisms that constitute the phenomenon-to-be-explained. The most prominent approach of how to understand this relation is Carl Craver's mutual manipulability approach (MM) to constitutive relevance. Recently, MM has come under attack (Baumgartner and Casini 2017; Baumgartner and Gebharter 2015; Harinen 2014; Kästner 2017; Leuridan 2012; Romero 2015). It is argued that MM is inconsistent because, roughly, it is spelled out in terms of interventionism (which is an approach to causation), whereas constitutive relevance is said to be a non-causal relation. In this paper, I will discuss a strategy of how to resolve this inconsistency—so-called fat-handedness approaches (Baumgartner and Casini 2017; Baumgartner and Gebharter 2015; Romero 2015). I will argue that these approaches are problematic. I will present a novel suggestion for how to consistently define constitutive relevance in terms of interventionism. My approach is based on a causal interpretation of manipulability in terms of causal relations between the mechanism's components and what I will call temporal EIO-parts of the phenomenon. Still, this interpretation accounts for the fundamental difference between constitutive relevance and causal relevance.     

Seepage,objectivity, and climate science

Based on the disproportionate amount of attention paid by climate scientists to the supposed global warming hiatus, it has recently been argued that contrarian discourse has “seeped into” climate science. While I agree that seepage has occurred, its effects remain unclear. This lack of clarity may give the impression that climate science has been compromised in a way that it hasn't—such a conclusion should be defended against. To do this I argue that the effects of seepage should be analyzed in terms of objectivity. I use seven meanings of objectivity to analyze contrarian discourse's impact on climate science. The resulting account supports the important point that climate science has not been compromised in a way that invalidates the conclusions its scientists have drawn, despite the reality of seepage having occurred.     

Understanding climate phenomena with data-driven models

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.     

Putting the Earth System in a numerical box? The evolution from climate modeling toward global change

Since the 1980s, climate modeling has undergone major transformations. The most prominent of these are the proliferation of coupled models and the integration within models of a growing number of environments and feedbacks. Climate modelers now increasingly define their object in terms of an “Earth System” instead of a “climate system”. In addition to this proliferation of coupled models, the carbon cycle and its feedback on various environments, from the atmosphere to the ocean and to vegetation cover, has become a prominent component of climate modeling. These transformations derive from the IPCC’s overall methodology, and are closely bound up with both a heightened awareness of the risks of climate change, as well as an issue of crucial political importance: the question of socio-economic/climate integration. In this article I follow, from a roughly chronological point of view, the major steps of this evolution and its links with the evolution of the political agenda. What can we say about this seemingly irreversible tendency to incorporate everything into models and to take account of everything that influences the Earth’s climate? Could we correlate it to the strong tendency toward globalization? How is the notion of climate itself affected? These are the main questions of the paper.     

I s

Richard Arthur (2006) and I (Savitt, 2009) proposed that the present in (time-oriented) Minkowski spacetime should be thought of as a small causal diamond. That is, given two timelike separated events p and q, with p earlier than q, we suggested that the present (relative to those two events) is the set I⁺(p)∩I^-(q). Mauro Dorato (2011) presents three criticisms of this proposal. I rebut all three and then examine two more plausible criticisms of the Arthur/Savitt proposal. I argue that these criticisms also fail.