Explanatory Virtues Research Articles

The good life as the life in touch with the good

What makes your life go well for you? In this paper, we give an account of welfare. Our core idea is simple. There are impersonally good and bad things out there: things that are good or bad period, not (or not only) good or bad for someone. The life that is good for you is the life in contact with the good. We’ll understand the relevant notion of ‘contact’ here in terms of manifestation: you’re in contact with a value when it is manifest in parts of your life or parts of your life are manifest in it. So, the more the good is manifest in your life, or your life manifest in the good, the better for you. The more the bad is manifest in your life, or your life manifest in the bad, the worse for you. We’ll argue that this account is extensionally adequate: it explains the welfare value of achievements, friendships, knowledge, pleasures and virtues. Moreover, it has a number of explanatory virtues: it’s unified, elegant and explanatorily powerful. So, we’ll suggest, it’s an excellent account of welfare, and in many ways superior to its main competitors.

Lovely and likely: Using historical methods to improve inference to the best explanation in strategy

AbstractResearch SummaryMany strategy studies implicitly rely upon inference to the best explanation (IBE) or modern abduction. We leverage recent work in the philosophy of science to consider how we arrive at “best” explanations, explanations that are lovely, in the sense that they are useful, general, and provide meaning, and likely, in the sense that they are close to the truth. Interpretation of observational results requires an understanding of context that statistical analysis alone cannot provide. At that point of encounter, historical methods—hermeneutics, contextualization and source criticism—can improve IBE by helping scholars (1) generate new candidate explanations and (2) systematically judge, privilege, and balance the explanatory virtues that constitute the loveliness and likeliness of explanations.Managerial SummaryMany strategy studies iteratively use data and theory to inference to the best explanation of observed phenomena. We leverage recent work in the philosophy of science to consider how we arrive at best explanations that are useful, general, provide meaning, and, at the same time, are close to the truth. Interpreting observational results requires an understanding of the context that statistical analysis alone cannot provide. At that point of encounter, methodological tools from the field of history can improve the process of determining the best explanation by helping scholars (1) generate new candidate explanations and (2) systematically judge and privilege explanations.

Revisiting the narrow latent scope bias in explanatory reasoning

Humans are capable explainers and lay people tend to share the same explanatory virtues held in high regard by philosophers and scientists. However, a recent line of studies found a striking deviation from normativity in lay people’s explanations, termed the “narrow latent scope bias”. When competing explanations with identical a priori probabilities fit observed evidence equally well – but differ in the number of unobserved pieces of evidence they predict (latent scope) – reasoners seem to prefer explanations that predict fewer unobserved pieces of evidence (narrow latent scope). This tendency has been described as a robust explanatory reasoning bias. The present paper empirically demonstrates across six experiments (N=2200) that this bias is less robust than has been claimed, and influenced by nuanced pragmatic inferences on the side of participants. Pragmatic factors shown to influence the bias are assumptions about how easily an unobserved piece of evidence should have been observed if it was present (“feature diagnosability”), and the formulation of the test question being asked. Across studies, genuine narrow latent scope biases resulting from fallacious reasoning were found only in a fraction of participants. It is also demonstrated that the magnitude of the bias depends on response options: it is stronger if participants are forced to commit an error, but at best weak if they are allowed to give the correct answer.

Explanatory virtues and reasons for belief

Abstract In this essay, I address an objection to inference to the best explanation due to Bas C. van Fraassen, according to which explanatory virtues cannot confirm a theory, since they make the theory more informative and thus less likely to be true given the probability axioms. I try to show that van Fraassen’s argument, once made precise, is deductively invalid, and that even an ampliative version of the argument (i) implies, absurdly, that no theory is confirmed by its fit with empirical data; (ii) fails to account for confirmatory closure under deduction; and (iii) falsely presupposes that a theory and its sub-theories can be competing explanations.

How good is an explanation?

How good is an explanation and when is one explanation better than another? In this paper, I address these questions by exploring probabilistic measures of explanatory power in order to defend a particular Bayesian account of explanatory goodness. Critical to this discussion is a distinction between weak and strong measures of explanatory power due to Good (Br J Philos Sci 19:123–143, 1968). In particular, I argue that if one is interested in the overall goodness of an explanation, an appropriate balance needs to be struck between the weak explanatory power and the complexity of a hypothesis. In light of this, I provide a new defence of a strong measure proposed by Good by providing new derivations of it, comparing it with other measures and exploring its connection with information, confirmation and explanatory virtues. Furthermore, Good really presented a family of strong measures, whereas I draw on a complexity criterion that favours a specific measure and hence provides a more precise way to quantify explanatory goodness.

The Shimmer in the Twilight: Walter Lippmann’s Public Opinion and the Journalist’s Way of Knowledge

ABSTRACT In the final section of Public Opinion, Walter Lippmann urges journalists to enlist help from scientists and social scientists to provide the public with “a picture of reality on which [they] can act.” Lippmann, however, acknowledges that there are matters of public concern that are not susceptible to the measuring, quantifying, and recording integral to scientific and social scientific inquiry, matters that oblige journalists “to occupy the position of an umpire in the unscored baseball game.” Lippmann did not tell journalists how to illuminate this “twilight zone” of news. This article argues that the political scientist James Scott’s discussion in his classic work Seeing Like a State of the value of “metis” for understanding one’s environment complements Lippmann’s work by highlighting practical knowledge’s value for navigating a world in which science’s “explanatory virtues” can obscure phenomena that cannot be measured but, nonetheless, journalists must describe and interpret in a republic.

How Does Explanatory Virtue Determine Probability Estimation?-Empirical Discussion on Effect of Instruction.

It is important to reveal how humans evaluate an explanation of the recent development of explainable artificial intelligence. So, what makes people feel that one explanation is more likely than another? In the present study, we examine how explanatory virtues affect the process of estimating subjective posterior probability. Through systematically manipulating two virtues, Simplicity—the number of causes used to explain effects—and Scope—the number of effects predicted by causes—in three different conditions, we clarified two points in Experiment 1: (i) that Scope's effect is greater than Simplicity's; and (ii) that these virtues affect the outcome independently. In Experiment 2, we found that instruction about the explanatory structure increased the impact of both virtues' effects but especially that of Simplicity. These results suggest that Scope predominantly affects the estimation of subjective posterior probability, but that, if perspective on the explanatory structure is provided, Simplicity can also affect probability estimation.

IBE in engineering science - the case of malfunction explanation

In this paper we investigate how inference to the best explanation (IBE) works in engineering science, focussing on the context of malfunction explanation. While IBE has gotten a lot of attention in the philosophy of science literature, few, if any, philosophical work has focussed on IBE in engineering science practice. We first show that IBE in engineering science has a similar structure as IBE in other scientific domains in the sense that in both settings IBE hinges on the weighing of explanatory virtues. We then proceed to show that, due to the intimate connection between explanation and redesign in engineering science, there is a further engineering domain-specific virtue in terms of which engineering malfunction explanations are evaluated, viz. the virtue of redesign utility. This virtue entails that the explanatory information offered by a malfunction explanation should be instrumental in predicting counterfactual dependencies in redesigned systems. We illustrate and elaborate these points in terms of a number of engineering examples, focussing in particular on the 2009 crash of Air France Flight 447. Our extension of analyses of IBE and explanation to engineering science practice offers new insights by identifying a new explanatory virtue in malfunction explanation: redesign utility.

From Probability to Consilience: How Explanatory Values Implement Bayesian Reasoning.

Recent work in cognitive science has uncovered a diversity of explanatory values, or dimensions along which we judge explanations as better or worse. We propose a Bayesian account of these values that clarifies their function and shows how they fit together to guide explanation-making. The resulting taxonomy shows that core values from psychology, statistics, and the philosophy of science emerge from a common mathematical framework and provide insight into why people adopt the explanations they do. This framework not only operationalizes the explanatory virtues associated with, for example, scientific argument-making, but also enables us to reinterpret the explanatory vices that drive phenomena such as conspiracy theories, delusions, and extremist ideologies.

The Intrinsic Probability of Grand Explanatory Theories

This paper articulates a way to ground a relatively high prior probability for grand explanatory theories apart from an appeal to simplicity. I explore the possibility of enumerating the space of plausible grand theories of the universe by using the explanatory properties of possible views to limit the number of plausible theories. I motivate this alternative grounding by showing that Swinburne’s appeal to simplicity is problematic along several dimensions. I then argue that there are three plausible grand views—theism, atheism, and axiarchism—which satisfy explanatory requirements for plausibility. Other possible views lack the explanatory virtue of these three theories. Consequently, this explanatory grounding provides a way of securing a nontrivial prior probability for theism, atheism, and axiarchism. An important upshot of my approach is that a modest amount of empirical evidence can bear significantly on the posterior probability of grand theories of the universe.

Putting explanation back into “inference to the best explanation”

AbstractMany philosophers argue that explanatoriness plays no special role in confirmation – that “inference to the best explanation” (IBE) incorrectly demands giving hypotheses extra credit for their potential explanatory qualities beyond the credit they already deserve for their predictive successes. This paper argues against one common strategy for responding to this thought – that is, for trying to fit IBE within a Bayesian framework. That strategy argues that a hypothesis’ explanatory quality (its “loveliness”) contributes either to its prior probability or to its likelihood. This paper argues that this strategy fails because it must give different treatments to two hypotheses that are unlovely for the very same reason. The strategy therefore loses the insight into scientific reasoning that its reconstruction in terms of IBE is supposed to provide. The paper then provides a Bayesian account of the confirmatory role of explanatoriness that represents explanatory quality as having an impact in the same place for two hypotheses that are unlovely for the very same reason. This approach works by “putting explanation back into IBE” – that is, by invoking hypotheses that refer explicitly to scientific explanation and by invoking the agent's background opinions regarding the kind of explanation that the evidence is liable to have. On this approach, there is no list of “explanatory virtues” the possession of which always helps to make an explanation better. Rather, for different facts, there are different characteristics that our background knowledge of other explanations gives us some reason to expect the given fact's explanation to possess.

Race and medicine in light of the new mechanistic philosophy of science

Racial disparities in health outcomes have recently become a flashpoint in the debate about the value of race as a biological concept. What role, if any, race has in the etiology of disease is a philosophically and scientifically contested topic. In this article, I expand on the insights of the new mechanistic philosophy of science to defend a mechanism discovery approach to investigating epidemiological racial disparities. The mechanism discovery approach has explanatory virtues lacking in the populational approach typically employed in the study of race and biomedicine. The explanatory constraints that form an integral part of the new mechanistic approach enable mechanism discovery to avoid the epistemic and normative shortcomings of the populational approach. The methodology of mechanism discovery can fruitfully be extended to the treatment and reversal of epidemiological racial disparities.

Proving quadratic reciprocity: explanation, disagreement, transparency and depth

Gauss’s quadratic reciprocity theorem is among the most important results in the history of number theory. It’s also among the most mysterious: since its discovery in the late eighteenth century, mathematicians have regarded reciprocity as a deeply surprising fact in need of explanation. Intriguingly, though, there’s little agreement on how the theorem is best explained. Two quite different kinds of proof are most often praised as explanatory: an elementary argument that gives the theorem an intuitive geometric interpretation, due to Gauss and Eisenstein, and a sophisticated proof using algebraic number theory, due to Hilbert. Philosophers have yet to look carefully at such explanatory disagreements in mathematics. I do so here. According to the view I defend, there are two important explanatory virtues—depth and transparency—which different proofs (and other potential explanations) possess to different degrees. Although not mutually exclusive in principle, the packages of features associated with the two stand in some tension with one another, so that very deep explanations are rarely transparent, and vice versa. After developing the theory of depth and transparency and applying it to the case of quadratic reciprocity, I draw some morals about the nature of mathematical explanation.

Scientific Explanation and Trade-Offs Between Explanatory Virtues

“Explanation” refers to a wide range of activities, with a family resemblance between them. Most satisfactory explanations in a discipline for a domain fail to satisfy some general desiderata, while fulfilling others. This can happen in various ways. Why? An idealizing response would be to say that in real science explanations fall short along some dimensions, so that for any explanatory failure there is a conceivable improvement that addresses its shortcomings. The improvement may be more accurate causally or possess more unifying power, or deliver deeper understanding. We formulate a drastically less idealizing response. We argue that there are typically trade-offs in explanation, so that in strengthening one explanatory virtue one will usually weaken another. Scientific explanations, if this is correct, are constrained by such trade-offs. Particular trade-offs are appropriate for particular explanatory vehicles. There are the overarching equations of theoretical physics, which produce unification at the expense of causal detail; there are theoretical models of phenomena that occupy a middle ground between generality and the detailed workings of particular cases and get closer to explaining the workings of specific systems at the expense of unification. Sometimes experiments aim at general causal patterns at the expense of particular detail; and sometimes they are designed to give us information of particular detail at the expense of generality. There are further trade-offs associated with other vehicles of explanation. We provide examples from physics and biology.

A new perspective on the relationship between metacognition and social cognition: metacognitive concepts as socio-cognitive tools

I defend an alternative to the two traditional accounts of the relationship between metacognition and social cognition: metacognition as primary versus social cognition as primary. These accounts have complementary explanatory vices and virtues. They also share a natural assumption: that interpretation in terms of mental states is “spectatorial”, aiming exclusively for an objective description of the mental facts about self and others. I argue that if one rejects this assumption in favor of the view that interpretation in terms of mental states also plays important regulative roles with respect to minds and behavior, a new and superior conception of the relationship between metacognition and social cognition comes into view. On this conception, person-level metacognitive concepts are socio-cognitive tools that shape us into better cognitive agents and more predictable cognitive objects, thereby enhancing our abilities at social coordination. Mastery of these metacognitive concepts relies on subpersonal, non-conceptual, procedural metacognition. This reconceptualization of the relationship between metacognition and social cognition combines the complementary explanatory virtues of the two traditional conceptions, while avoiding their complementary explanatory vices.

Explaining historical moral convergence: the empirical case against realist intuitionism

Over the course of human history there appears to have been a global shift in moral values towards a broadly ‘liberal’ orientation. Huemer (Philosophical Studies 173: 1983–2010, 2016) argues that this shift better accords with a realist than an antirealist metaethics: it is best explained by the discovery of mind-independent truths through intuition. In this article I argue, contra Huemer, that the historical data are better explained assuming the truth of moral antirealism. Realism does not fit the data as well as Huemer suggests, whereas antirealists have underappreciated resources to explain the relevant historical dynamics. These resources include an appeal to socialization, to technological and economical convergences, to lessons learned from history, to changes induced by consistency reasoning and to the social function of moral norms in overcoming some of the cooperation problems that globalizing societies face. I point out that the realist’s explanans has multiple shortcomings, that the antirealist’s explanans has several explanatory virtues, and conclude that the latter provides a superior account of the historical shift towards liberal values.

A study of explanatory virtues

The inference to the best explanation (IBE) appeals to how hypotheses would explain certain facts. If a hypothesis explains well and better than any other (available) hypotheses, IBE concludes that it is true. Our work examines what makes it the case that some hypotheses would explain facts better than others. We also examine other non-explanatory features which are (thought to be) correlated with the truth of a proposed explanation. We name all these features explanatory virtues, and they are the measure employed by IBE of goodness of explanation. Our research has the purpose of investigating four chief (proposed) explanatory virtues: simplicity, unification, non ad hocness, and coherence. We intend to study the description and justification of these proposed virtues. Our final goal is to improve and appraise the employment of IBE, a valuable tool in philosophy.

Against the iterative conception of set

According to the iterative conception, each set is formed out of sets that are, in some sense, prior to it. Because priority plays an essential role in explanations of why contradiction-inducing sets, such as the universal set, do not exist, the success of these explanations depends on our ability to make sense of the relevant priority relation. I argue that attempts to do this have fallen short: understanding priority in a straightforwardly constructivist sense threatens the coherence of the empty set and raises serious epistemological concerns; but the leading realist interpretations–ontological and modal interpretations of priority—are deeply problematic as well. I conclude that the purported explanatory virtues of the iterative conception are, at present, unfounded.

Does IBE Require a ‘Model’ of Explanation?

Abstract In this article, I consider an important challenge to the popular theory of scientific inference commonly known as ‘inference to the best explanation’ (IBE), one that has received scant attention.1 The problem is that there exists a wide array of rival models of explanation, thus leaving IBE objectionably indeterminate. First, I briefly introduce IBE. Then, I motivate the problem and offer three potential solutions, the most plausible of which is to adopt a kind of pluralism about the rival models of explanation. However, I argue that (i) how ranking explanations on this pluralistic account of IBE remains obscure and (ii) pluralism leads to contradictory results. In light of these objections, I attempt to dissolve the problem by showing why IBE does not require a ‘model’ of explanation and by giving an account of what explanation consists in within the context of IBE. 1IBE and the Plentitude Problem2Three Potential Solutions 2.1Solution 1: Primitivism2.2Solution 2: Accomodationism2.3Solution 3: Pluralism3Two Problems for Pluralism 3.1Difficulties with ranking explanations3.2The inevitability of conflicting verdicts4Dissolving the Plentitude Problem 4.1The explanatory virtues screen-off the model of explanation4.2The virtue-centric conception of explanation5Concluding Remarks

Explanatory Virtues are Indicative of Truth

Explanatory Virtues are Indicative of Truth