“Looking Up” and “Looking Down”: On the Dual Character of Mechanistic Explanations

Mechanistic explanation is at present the received view of scientific explanation. One of its central features is the idea that mechanistic explanations are both “downward looking” and “upward looking”: they explain by offering information about the internal constitution of the mechanism as well as the larger environment in which the mechanism is situated. That is, they offer both constitutive and contextual explanatory information. Adequate mechanistic explanations, on this view, accommodate the full range of explanatory factors both “above” and “below” the target phenomenon. The aim of this paper is to demonstrate that mechanistic explanation cannot furnish both constitutive and contextual information simultaneously, because these are different types of explanation with distinctly different aims. Claims that they can, I argue, depend on several intertwined confusions concerning the nature of explanation. Particularly, such claims tend to conflate mechanistic and functional explanation, which I argue ought to be understood as distinct. Conflating them threatens to oversell the explanatory power of mechanisms and obscures the means by which they explain. I offer two broad reasons in favor of distinguishing mechanistic and functional explanation: the first concerns the direction of explanation of each, and the second concerns the type of questions to which these explanations offer answers. I suggest an alternative picture on which mechanistic explanation is understood as fundamentally constitutive, and according to which an adequate understanding of a phenomenon typically requires supplementing the mechanistic explanation with a functional explanation.     

Explanatory pluralism in the medical sciences: Theory and practice

Explanatory pluralism is the view that the best form and level of explanation depends on the kind of question one seeks to answer by the explanation, and that in order to answer all questions in the best way possible, we need more than one form and level of explanation. In the first part of this article, we argue that explanatory pluralism holds for the medical sciences, at least in theory. However, in the second part of the article we show that medical research and practice is actually not fully and truly explanatory pluralist yet. Although the literature demonstrates a slowly growing interest in non-reductive explanations in medicine, the dominant approach in medicine is still methodologically reductionist. This implies that non-reductive explanations often do not get the attention they deserve. We argue that the field of medicine could benefit greatly by reconsidering its reductive tendencies and becoming fully and truly explanatory pluralist. Nonetheless, trying to achieve the right balance in the search for and application of reductive and non-reductive explanations will in any case be a difficult exercise.     

Causal Explanation and Explanatory Selection

It is observed that in ordinary everyday causal explanations often just one causal factor is mentioned. One causal factor carries the explanatory burden, even if there are several causal factors that are responsible for the event to be explained. This paper deals with the question of how to account for this explanatory selection. I argue for a pragmatic stance towards explanation, that we must attend to the question–answer situation as a whole and the context of the explanation. The context of an explanation includes the inquirer's and the explainer's beliefs and presuppositions relevant for the explanation-seeking question, and these are encoded in a reference class. Furthermore I argue that the explanation-giving answer contains an implicit counterfactual claim, the explanation-giving counterfactual. The solution to the problem of explanatory selection is to be found in the presuppositions encoded by the reference class and the eg-counterfactual. In short we select as explanatory that factor which, together with the presupposition encoded in the reference class we believe will make the eg-counterfactual true. This revised version was published online in June 2006 with corrections to the Cover Date.     

General theories of explanation: buyer beware

We argue that there is no general theory of explanation that spans the sciences, mathematics, and ethics, etc. More specifically, there is no good reason to believe that substantive and domain-invariant constraints on explanatory information exist. Using Nickel (Noûs 44(2):305–328, 2010) as an exemplar of the contrary, generalist position, we first show that Nickel’s arguments rest on several ambiguities, and then show that even when these ambiguities are charitably corrected, Nickel’s defense of general theories of explanation is inadequate along several different dimensions. Specifically, we argue that Nickel’s argument has three fatal flaws. First, he has not provided any compelling illustrations of domain-invariant constraints on explanation. Second, in order to fend off the most vehement skeptics of domain-invariant theories of explanation, Nickel must beg all of the important questions. Third, Nickel’s examples of explanations from different domains with common explanatory structure rely on incorrect formulations of the explanations under consideration, circular justifications, and/or a mischaracterization of the position Nickel intends to critique. Given that the best and most elaborate defense of the generalist position fails in so many ways, we conclude that the standard practice in philosophy (and in philosophy of science in particular), which is to develop theories of explanation that are tailored to specific domains, still is justified. For those who want to buy into a more ambitious project: beware of the costs!     

Varying the Explanatory Span: Scientific Explanation for Computer Simulations

This article aims to develop a new account of scientific explanation for computer simulations. To this end, two questions are answered: what is the explanatory relation for computer simulations? And what kind of epistemic gain should be expected? For several reasons tailored to the benefits and needs of computer simulations, these questions are better answered within the unificationist model of scientific explanation. Unlike previous efforts in the literature, I submit that the explanatory relation is between the simulation model and the results of the simulation. I also argue that our epistemic gain goes beyond the unificationist account, encompassing a practical dimension as well.     

Importance and Explanatory Relevance: The Case of Mathematical Explanations

A way to argue that something (e.g. mathematics, idealizations, moral properties, etc.) plays an explanatory role in science is by linking explanatory relevance with importance in the context of an explanation. The idea is deceptively simple: a part of an explanation is an explanatorily relevant part of that explanation if removing it affects the explanation either by destroying it or by diminishing its explanatory power, i.e. an important part (one that if removed affects the explanation) is an explanatorily relevant part. This can be very useful in many ontological debates. My aim in this paper is twofold. First of all, I will try to assess how this view on explanatory relevance can affect the recent ontological debate in the philosophy of mathematics—as I will argue, contrary to how it may appear at first glance, it does not help very much the mathematical realists. Second of all, I will show that there are big problems with it.     

Indispensability Arguments in Favour of Reductive Explanations

Instances of explanatory reduction are often advocated on metaphysical grounds; given that the only real things in the world are subatomic particles and their interaction, we have to try to explain everything in terms of the laws of physics. In this paper, we show that explanatory reduction cannot be defended on metaphysical grounds. Nevertheless, indispensability arguments for reductive explanations can be developed, taking into account actual scientific practice and the role of epistemic interests. Reductive explanations might be indispensable to address some epistemic interest answering a specific explanation-seeking question in the most accurate, adequate and efficient way. Just like explanatory pluralists often advocate the indispensability of higher levels of explanation pointing at the pragmatic value of the explanatory information obtained on these higher levels, we argue that explanatory reduction—traditionally understood as the contender of pluralism—can be defended in a similar way. The pragmatic value reductionist, lower level explanations might have in the biomedical sciences and the social sciences is illustrated by some case studies.     

Function and Functional Explanation in Social Capital Theory: A Philosophical Appraisal

Social capital is frequently offered up as a variable to explain such educational outcomes as academic attainment, drop-out rates and cognitive development. Yet, despite its popularity amongst social scientists, social capital theory remains the object of some scepticism, particularly in respect of its explanatory ambitions. I provide an account of some explanatory options available to social capital theorists, focussing on the functions ascribed to social capital and on how these are used as explanatory variables in educational theory. Two of the most influential writers in this field are Coleman and Bourdieu. I explore their commonalities and differences, both in respect of their writing and in respect of some of the many theorists they have influenced. I argue that social capital theorists have made substantial progress in responding to sceptically minded critics, but that significant questions remain, especially about the success of the more ambitious explanatory variants as these apply to educational outcomes—functional explanation in particular. Functional explanation, and its association with Bourdieu, is discussed in ‘Bourdieu and functional explanation’; thereafter I discuss the more modest ambition of identifying the functions associated with social capital. In ‘Coleman, intergenerational closure and educational outcomes’ I show how Coleman provides resources for revealing how social structure features in social explanation in an educational context, and in ‘Inequality, class and ethnicity’ I suggest that some of the questions raised in his account are most satisfactorily responded to by educational theorists who adopt Bourdieu’s emphasis on social class and inequality.     

Why it doesn’t matter whether the virtues are truth-conducive

A potential explanation of a fact is a hypothesis such that, if it were true, it would explain the fact in question. Let’s suppose that we become aware of a fact and some potential explanations thereof. Let’s also suppose that we would like to believe the truth. Given this aim, we can ask two questions. First, is it likely that one of these hypotheses is true? Second, given an affirmative answer to the first question, which one is it likely to be? Inference to the best explanation (IBE) offers answers to both questions. To the first, it says ‘Yes’—assuming that at least one of the hypotheses would, if true, provide a satisfactory explanation of the fact under consideration. To the second, it says that the hypothesis most likely to be true is the one that scores best on the explanatory virtues: conservatism, modesty, simplicity, generality, and predictive power. Many philosophers have argued against IBE’s answer to the first question. I am interested in an objection to its answer to the second. Many philosophers seem to think that it is unsustainable: they seem to think that even if we assume that one of the competing hypotheses is true, we should not think that IBE will help us to identify it. Or, more carefully, if these philosophers are doing what they appear to be doing—namely, offering critiques of IBE that don’t depend on assumptions about the field of competing hypotheses—then their claim is that IBE will not help us to identify the truth. I believe that this is mistaken: the argument for believing it assumes a model of IBE that we have no reason to accept.     

Antecedents of virtual community satisfaction and loyalty: an empirical test of competing theories.

The rapid growth of virtual communities (VCs) on the Internet raises important research questions regarding the antecedents of satisfaction and loyalty in VCs. This study compared the technology acceptance model (TAM), Seddon Information Systems Success Model (Seddon model), and an integrated model (combining the TAM and Seddon model) to examine which model best helps to predict user satisfaction with VCs. Using a structural equation model, data collected from 198 community members were used to compare the three models in terms of overall model fit, explanatory power, and path significance. The findings show that two TAM components (perceived usefulness and ease of use) are determinants of user satisfaction with VCs. This study also found the influence of quality-perception dimensions (e.g., system and information quality) on user satisfaction to be significant. Additionally, this study found general support for user satisfaction as a determinant of loyalty in VCs. Finally, an integrated model, combining the TAM and Seddon model, provided better explanatory power than either the TAM or the Seddon model alone. Finally, this study discusses the implications of these findings and offers directions for future research.     

Explanatory power of extended cognition

I argue that examining the explanatory power of the hypothesis of extended cognition (HEC) offers a fruitful approach to the problem of cognitive system demarcation. Although in the discussions on HEC it has become common to refer to considerations of explanatory power as a means for assessing the plausibility of the extended cognition approach, to date no satisfying account of explanatory power has been presented in the literature. I suggest that the currently most prominent theory of explanation in the special sciences, James Woodward's contrastive-counterfactual theory, and an account of explanatory virtues building on that theory can be used to develop a systematic picture of cognitive system demarcation in the psychological sciences. A major difference between my differential influence (DI) account and most other theories of cognitive extension is the cognitive systems pluralism implied by my approach. By examining the explanatory power of competing traditions in psychological memory research, I conclude that internalist and externalist classificatory strategies are characterized by different profiles of explanatory virtues and should often be considered as complementary rather than competing approaches. This suggests a deflationary interpretation of HEC.     

Inference to the best explanation, coherence and other explanatory virtues

This article generalizes the explanationist account of inference to the best explanation (IBE). It draws a clear distinction between IBE and abduction and presents abduction as the first step of IBE. The second step amounts to the evaluation of explanatory power, which consist in the degree of explanatory virtues that a hypothesis exhibits. Moreover, even though coherence is the most often cited explanatory virtue, on pain of circularity, it should not be treated as one of the explanatory virtues. Rather, coherence should be equated with explanatory power and considered to be derivable from the other explanatory virtues: unification, explanatory depth and simplicity.     

Is Mathematics a Domain for Philosophers of Explanation?

In this paper we discuss three interrelated questions. First: is explanation in mathematics a topic that philosophers of mathematics can legitimately investigate? Second: are the specific aims that philosophers of mathematical explanation set themselves legitimate? Finally: are the models of explanation developed by philosophers of science useful tools for philosophers of mathematical explanation? We argue that the answer to all these questions is positive. Our views are completely opposite to the views that Mark Zelcer has put forward recently. Throughout this paper, we show why Zelcer’s arguments fail.     

Explaining enkratic asymmetries: knowledge-first style

There are two different kinds of enkratic principles for belief: evidential enkratic principles and normative enkratic principles. It’s frequently taken for granted that there’s not an important difference between them. But evidential enkratic principles are undermined by considerations that gain no traction at all against their normative counterparts. The idea that such an asymmetry exists between evidential and normative enkratic principles is surprising all on its own. It is also something that calls out for explanation. Similarly, the considerations that undermine evidential enkratic principles also undermine certain narrow-scope evidential principles. This too generates explanatory questions. I show how a knowledge-first view of rationality can easily address these (and further) explanatory questions. Thus we have one more reason to put knowledge first in epistemology.     

Explanatory Consolidation: From ‘Best’ to ‘Good Enough’

In science and everyday life, we often infer that something is true because it would explain some set of facts better than any other hypothesis we can think of. But what if we have reason to believe that there is a better way to explain these facts that we just haven’t thought of? Wouldn’t that undermine our warrant for believing the best available explanation? Many philosophers have assumed that we can solve such underconsideration problems by stipulating that a hypothesis should not only be ‘the best’ explanation available; rather, it should also be ‘good enough’. Unfortunately, however, the only current suggestion for what it might mean to say that an explanation is ‘good enough’ is, well, not good enough. This paper aims to provide a better account of what is required for an explanatory hypothesis to be considered ‘good enough’. In brief, the account holds that a ‘good enough’ hypothesis is one that has gone through a process that I call explanatory consolidation, in which accumulating evidence and failed attempts to formulate better alternatives gradually make it more plausible that the explanation we currently have is better than any other that could be formulated.     

Abstraction and its Limits: Finding Space For Novel Explanation

Several modern accounts of explanation acknowledge the importance of abstraction and idealization for our explanatory practice. However, once we allow a role for abstraction, questions remain. I ask whether the relation between explanations at different theoretical levels should be thought of wholly in terms of abstraction, and argue that changes of the quantities in terms of which we describe a system can lead to novel explanations that are not merely abstractions of some more detailed picture. I use the example of phase transitions as described by statistical mechanics and thermodynamics to illustrate this, and to demonstrate some details of the relationship between abstraction, idealization, and novel explanation.     

A mechanistic perspective on canonical neural computation

Although it has been argued that mechanistic explanation is compatible with abstraction (i.e., that there are abstract mechanistic models), there are still doubts about whether mechanism can account for the explanatory power of significant abstract models in computational neuroscience. Chirimuuta has recently claimed that models describing canonical neural computations (CNCs) must be evaluated using a non-mechanistic framework. I defend two claims regarding these models. First, I argue that their prevailing neurocognitive interpretation is mechanistic. Additionally, a criterion recently proposed by Levy and Bechtel to legitimize mechanistic abstract models, and also a criterion proposed by Chirimuuta herself aimed to distinguish between causal and non-causal explanation, can be employed to show why these models are explanatory only under this interpretation (as opposed to a purely mathematical or non-causal interpretation). Second, I argue that mechanism is able to account for the special epistemic achievement implied by CNC models. Canonical neural components contribute to an integrated understanding of different cognitive functions. They make it possible for us to explain these functions by describing different mechanisms constituted by common basic components arranged in different ways.     

The metaphor police: A case study of the role of metaphor in explanation

While many scholars have pointed to the role of metaphor in explanation, relatively little experimental research has examined whether and how metaphors are used and understood in everyday explanatory discourse. Across 3 experiments, we investigated the nature and function of metaphor in explanation by drawing on a real-world example where the terms guardian and warrior were used to metaphorically explain the role of police officers. We found, first, that the associations participants brought to mind for these concepts differed depending on whether they had previously answered questions about law enforcement (e.g., associations for warrior emphasized aggression and violence rather than strength and bravery when participants had previously answered questions about policing). Second, people were almost evenly split in their judgment of which metaphor was more appropriate to explain the role of law enforcement; this preference was highly predictive of beliefs related to policing and the criminal justice system. Third, and most important, using these metaphors to explain the job of policing causally influenced attitudes toward law enforcement in a metaphor-congruent manner (i.e., exposure to the guardian metaphor led to more positive attitudes), a finding that could not be accounted for by basic lexical priming. These studies complement existing work that has identified metaphor as a mechanism for representing abstract concepts, but also highlight the communicative and explanatory, rather than representational, functions of metaphor by showing that metaphors can encapsulate and convey an array of structured attitudes and beliefs.