The diverse aims of science

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. I suggest that continuing, widespread idealization calls into question the idea that science aims for truth. If instead science aims to produce understanding, this would enable idealizations to directly contribute to science's epistemic success. I also use the fact of widespread idealization to motivate the idea that science's wide variety aims, epistemic and non-epistemic, are best served by different kinds of scientific products. Finally, I show how these diverse aims—most rather distant from truth—result in the expanded influence of social values on science.     

The explanatory role of abstraction processes in models: The case of aggregations

Though it is held that some models in science have explanatory value, there is no conclusive agreement on what provides them with this value. One common view is that models have explanatory value vis-à-vis some target systems because they are developed using an abstraction process (i.e., a process which involves omitting features). Though I think this is correct, I believe it is not the whole picture. In this paper, I argue that, in addition to the well-known process of abstraction understood as an omission of features or information, there is also a family of abstraction processes that involve aggregation of features or information and that these processes play an important role in endowing the models they are used to build with explanatory value. After offering a taxonomy of abstraction processes involving aggregation, I show by considering in detail several models drawn from different sciences that the abstraction processes involving aggregation that are used to build these models are responsible (at least partially) for their having explanatory value.     

Feminist implications of model-based science

Recent philosophy of science has witnessed a shift in focus, in that significantly more consideration is given to how scientists employ models. Attending to the role of models in scientific practice leads to new questions about the representational roles of models, the purpose of idealizations, why multiple models are used for the same phenomenon, and many more besides. In this paper, I suggest that these themes resonate with central topics in feminist epistemology, in particular prominent versions of feminist empiricism, and that model-based science and feminist epistemology each has crucial resources to offer the other’s project.     

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.     

Idealizations and analogies: Explaining critical phenomena

The “universality” of critical phenomena is much discussed in philosophy of scientific explanation, idealizations and philosophy of physics. Lange and Reutlinger recently opposed Batterman concerning the role of some deliberate distortions in unifying a large class of phenomena, regardless of microscopic constitution. They argue for an essential explanatory role for “commonalities” rather than that of idealizations. Building on Batterman's insight, this article aims to show that assessing the differences between the universality of critical phenomena and two paradigmatic cases of “commonality strategy”—the ideal gas model and the harmonic oscillator model—is necessary to avoid the objections raised by Lange and Reutlinger. Taking these universal explanations as benchmarks for critical phenomena reveals the importance of the different roles played by analogies underlying the use of the models. A special combination of physical and formal analogies allows one to explain the epistemic autonomy of the universality of critical phenomena through an explicative loop.     

Chomsky in the playground: Idealization in generative linguistics

For a long time, the accepted explanatory model of language acquisition was the so-called Principles and Parameters framework (P&P). P&P seemingly provides an elegant answer to the central puzzle of generative linguistics: How do children acquire their native language given the limited time and input resources available to them? Yet P&P tells a story that is evolutionarily implausible, and for this reason it has since been abandoned. I argue that this is an unwarranted move, and that it could and should be avoided by reassessing the epistemic status of P&P. In particular, I argue that contrary to extant accounts, P&P ought to be retrospectively construed as a highly idealized (toy) model of language acquisition. The proposed reinterpretation is vindicated, I argue, insofar as it paves the way for a reconciliation of the two central explanatory challenges of modern generative linguistics.     

Structural realism versus deployment realism: A comparative evaluation

In this paper I challenge and adjudicate between the two positions that have come to prominence in the scientific realism debate: deployment realism and structural realism. I discuss a set of cases from the history of celestial mechanics, including some of the most important successes in the history of science. To the surprise of the deployment realist, these are novel predictive successes toward which theoretical constituents that are now seen to be patently false were genuinely deployed. Exploring the implications for structural realism, I show that the need to accommodate these cases forces our notion of “structure” toward a dramatic depletion of logical content, threatening to render it explanatorily vacuous: the better structuralism fares against these historical examples, in terms of retention, the worse it fares in content and explanatory strength. I conclude by considering recent restrictions that serve to make “structure” more specific. I show however that these refinements will not suffice: the better structuralism fares in specificity and explanatory strength, the worse it fares against history. In light of these case studies, both deployment realism and structural realism are significantly threatened by the very historical challenge they were introduced to answer.     

A role for spatiotemporal scales in modeling

Bogen and Woodward's distinction between data and phenomena raises the need to understand the structure of the data-to-phenomena and theory-to-phenomena inferences. I suggest that one way to study the structure of these inferences is to analyze the role of the assumptions involved in the inferences: What kind of assumptions are they? How do these assumptions contribute to the practice of identifying phenomena? In this paper, using examples from atmospheric dynamics, I develop an account of the practice of identifying the target in the data-to-phenomena and theory-to-phenomena inferences in which assumptions about spatiotemporal scales play a central role in the identification of parameters that describe the target system. I also argue that these assumptions are not only empirical but they are also idealizing and abstracting. I conclude the paper with a reflection on the role of idealizations in modeling.     

How could models possibly provide how-possibly explanations?

One puzzle concerning highly idealized models is whether they explain. Some suggest they provide so-called ‘how-possibly explanations’. However, this raises an important question about the nature of how-possibly explanations, namely what distinguishes them from ‘normal’, or how-actually, explanations? I provide an account of how-possibly explanations that clarifies their nature in the context of solving the puzzle of model-based explanation. I argue that the modal notions of actuality and possibility provide the relevant dividing lines between how-possibly and how-actually explanations. Whereas how-possibly explanations establish claims of possible explanations, how-actually explanations establish claims of actual ones. Models, in turn, simply provide evidence for these claims.     

What is narrative possibility?

In this paper I offer a new account of narrative possibility that I call the “ecological approach.” I situate it relative to alternative “metaphysical” and “epistemological” approaches, and argue that it has advantages in comparison. It saves some of the important insights from each, but serves the purposes of narrative explanation better than either, specifically because it delimits the explanatory modal space of narrative explanation correctly, whereas the others do not.     

Detail and generality in mechanistic explanation

This article is about the role of abstraction in mechanistic explanations. Abstraction is widely recognised as a necessary concession to the practicalities of scientific work, but some mechanist philosophers argue that it is also a positive explanatory feature in its own right. I claim that in as much as these arguments are based on the idea that mechanistic explanation exhibits a trade-off between fine-grained detail and generality, they are unsuccessful. Detail and generality both appear to be important sources of explanatory power, but investigators do not need to make a choice between these desiderata, at least when an explanation incorporates further detail through the decomposition of the mechanism's parts.     

What was classical genetics?

I present an account of classical genetics to challenge theory-biased approaches in the philosophy of science. Philosophers typically assume that scientific knowledge is ultimately structured by explanatory reasoning and that research programs in well-established sciences are organized around efforts to fill out a central theory and extend its explanatory range. In the case of classical genetics, philosophers assume that the knowledge was structured by T. H. Morgan’s theory of transmission and that research throughout the later 1920s, 30s, and 40s was organized around efforts to further validate, develop, and extend this theory. I show that classical genetics was structured by an integration of explanatory reasoning (associated with the transmission theory) and investigative strategies (such as the ‘genetic approach’). The investigative strategies, which have been overlooked in historical and philosophical accounts, were as important as the so-called laws of Mendelian genetics. By the later 1920s, geneticists of the Morgan school were no longer organizing research around the goal of explaining inheritance patterns; rather, they were using genetics to investigate a range of biological phenomena that extended well beyond the explanatory domain of transmission theories. Theory-biased approaches in history and philosophy of science fail to reveal the overall structure of scientific knowledge and obscure the way it functions.     

Self-organised criticality—what it is and what it isn’t

The last decade and a half has seen an ardent development of self-organised criticality (SOC), a new approach to complex systems, which has become important in many domains of natural as well as social science, such as geology, biology, astronomy, and economics, to mention just a few. This has led many to adopt a generalist stance towards SOC, which is now repeatedly claimed to be a universal theory of complex behaviour. The aim of this paper is twofold. First, I provide a brief and non-technical introduction to SOC. Second, I critically discuss the various bold claims that have been made in connection with it. Throughout, I will adopt a rather sober attitude and argue that some people have been too readily carried away by fancy contentions. My overall conclusion will be that none of these bold claims can be maintained. Nevertheless, stripped of exaggerated expectations and daring assertions, many SOC models are interesting vehicles for promising scientific research.     

The realizers and vehicles of mental representation

The neural vehicles of mental representation play an explanatory role in cognitive psychology that their realizers do not. Cognitive psychology individuates neural structures as representational vehicles in terms of the specific causal properties to which cognitive mechanisms are sensitive. Explanations that appeal to properties of vehicles can capture generalisations which are not available at the level of their neural realizers. In this paper, I argue that the individuation of realizers as vehicles restricts the sorts of explanations in which they can participate. I illustrate this with reference to Rupert’s (2011) claim that representational vehicles can play an explanatory role in psychology in virtue of their quantity or proportion. I propose that such quantity-based explanatory claims can apply only to realizers and not to vehicles, in virtue of the particular causal role that vehicles play in psychological explanations.     

Re-orienting discussions of scientific explanation: A functional perspective

Philosophy of science offers a rich lineage of analysis concerning the nature of scientific explanation, but the vast majority of this work, aiming to provide an analysis of the relation that binds a given explanans to its corresponding explanandum, presumes the proper analytic focus rests at the level of individual explanations. There are, however, other questions we could ask about explanation in science, such as: What role(s) does explanatory practice play in science? Shifting focus away from explanations, as achievements, toward explaining, as a coordinated activity of communities, the functional perspective aims to reveal how the practice of explanatory discourse functions within scientific communities given their more comprehensive aims and practices. In this paper, I outline the functional perspective, argue that taking the functional perspective can reveal important methodological roles for explanation in science, and consequently, that beginning here provides resources for developing more adequate responses to traditional concerns. In particular, through an examination of the ideal gas law, I emphasize the normative status of explanations within scientific communities and discuss how such status underwrites a compelling rationale for explanatory power as a theoretical virtue.     

Minkowski spacetime and Lorentz invariance: The cart and the horse or two sides of a single coin?

Michel Janssen and Harvey Brown have driven a prominent recent debate concerning the direction of an alleged arrow of explanation between Minkowski spacetime and Lorentz invariance of dynamical laws in special relativity. In this article, I critically assess this controversy with the aim of clarifying the explanatory foundations of the theory. First, I show that two assumptions shared by the parties—that the dispute is independent of issues concerning spacetime ontology, and that there is an urgent need for a constructive interpretation of special relativity—are problematic and negatively affect the debate. Second, I argue that the whole discussion relies on a misleading conception of the link between Minkowski spacetime structure and Lorentz invariance, a misconception that in turn sheds more shadows than light on our understanding of the explanatory nature and power of Einstein׳s theory. I state that the arrow connecting Lorentz invariance and Minkowski spacetime is not explanatory and unidirectional, but analytic and bidirectional, and that this analytic arrow grounds the chronogeometric explanations of physical phenomena that special relativity offers.