Explanatory Considerations Research Articles

Conjunctive explanations: when are two explanations better than one?

When is it explanatorily better to adopt a conjunction of explanatory hypotheses as opposed to committing to only some of them? Although conjunctive explanations are inevitably less probable than less committed alternatives, we argue that the answer is not ‘never’. This paper provides an account of the conditions under which explanatory considerations warrant a preference for less probable, conjunctive explanations. After setting out four formal conditions that must be met by such an account, we consider the shortcomings of several approaches. We develop an account that avoids these shortcomings and then defend it by applying it to a well-known example of explanatory reasoning in contemporary science.

Distinct Profiles for Beliefs About Religion Versus Science.

A growing body of research suggests that scientific and religious beliefs are often held and justified in different ways. In three studies with 707 participants, we examine the distinctive profiles of beliefs in these domains. In Study 1, we find that participants report evidence and explanatory considerations (making sense of things) as dominant reasons for beliefs across domains. However, cuing the religious domain elevates endorsement of nonscientific justifications for belief, such as ethical considerations (e.g., believing it encourages people to be good), affiliation (what loved ones believe), and intuition (what feels true in one's heart). Study 2 replicates these differences with specific scientific and religious beliefs held with equal confidence, and documents further domain differences in beliefs' personal importance, openness to revision, and perceived objectivity. Study 3 replicates these differences, further finding that counter-consensus beliefs about contentious science topics (such as climate change and vaccination) often have properties resembling religious beliefs, while counter-religious beliefs about religion (e.g., "There is no God") have properties that more closely resemble beliefs about science. We suggest that beliefs are held and justified within coherent epistemic frameworks, with individuals using different frameworks in different contexts and domains.

A false dichotomy in denying explanatoriness any role in confirmation

AbstractRoche and Sober (2013; 2014; 2017; 2019) have offered an important new argument that explanatoriness lacks confirmatory significance. My aim in this paper is not only to contend that their argument fails to show that in confirmation ‘there is nothing special about explanatoriness’ (Roche & Sober, 2017: 589), but also to reveal what is special confirmationwise about explanatoriness. I will argue that much of the heavy work in Roche and Sober's argument is done by the dichotomy into which they carve up the philosophical options: either explanatory considerations per se are confirmatorily relevant or they acquire whatever confirmatory significance they may possess only by way of background beliefs and so no differently from non‐explanatory considerations. I will argue that this is a false dichotomy: Explanatoriness per se can have confirmatory relevance even while explanatory considerations acquire their confirmatory relevance only through background opinions. Furthermore, there is ‘something special’ about the background opinions concerning explanations that allow explanatoriness in itself to have confirmatory impact. Unlike other sorts of background, having some background opinions expressly about explanations is indispensable to being an observer at all.

The old evidence problem and the inference to the best explanation

The Problem of Old Evidence (POE) states that Bayesian confirmation theory cannot explain why a theory H can be confirmed by a piece of evidence E already known. Different dimensions of POE have been highlighted. Here, I consider the dynamic and static dimension. In the former, we want to explain how the discovery that H accounts for E confirms H. In the latter, we want to understand why E is and will be a reason to prefer H over its competitors. The aim of the paper is twofold. Firstly, I stress that two recent solutions to the dynamic dimension, recently proposed by Eva and Hartmann, can be read in terms of Inference to the Best Explanation (IBE). On this base, I gauge the weaknesses and strengths of the two models. Namely, I show that the two authors endorse a particular IBE’s formulation, and that it is still unsure if it is the one descriptively used. Moreover, I contend that, while one condition of their first model is not expression of this formulation, the only condition of their second model is. Secondly, I focus on the static dimension of POE which, now, has to be expressed in IBE terms. To solve it, I rely on the counterfactual approach, and on a version of IBE in which explanatory considerations help to evaluate the terms in Bayes’ theorem. However, it turns out that the problems of the counterfactual approach recur even when it is used to solve the static POE in IBE terms.

Exploring the Metaphysics of Hegel's Racism: The Teleology of the ‘Concept’ and the Taxonomy of Races

AbstractThis article interprets Hegel's hierarchical theory of race as an application of his general views about the metaphysics of classification and explanation. We begin by offering a reconstruction of Hegel's hierarchical theory of race based on the critical edition of relevant lecture transcripts: we argue that Hegel's position on race is appropriately classified as racist, that it postulates innate mental deficits of some races, and that it turns racism from an anthropological into a metaphysical doctrine by claiming that the division of humankind into races (at least in the Old World) is not a brute fact, but follows a ‘higher necessity’. We then summarize our interpretation of the relevant metaphysical background to this theory. On our reading, Hegel postulates an essentialist form of explanation that explains given kinds as stages in a teleological, non-temporal process through which the nature of a superordinate kind is realized. We argue that Hegel's views about a hierarchical and necessary division of humankind into races are an application of this model to the case of human diversity, motivated by explanatory considerations and subject to confirmation bias. By way of conclusion, we address two possible attempts to ‘save’ Hegelian philosophy from its racist baggage.

The semantic view of computation and the argument from the cognitive science practice

According to the semantic view of computation, computations cannot be individuated without invoking semantic properties. A traditional argument for the semantic view is what we shall refer to as the argument from the cognitive science practice. In its general form, this argument rests on the idea that, since cognitive scientists describe computations (in explanations and theories) in semantic terms, computations are individuated semantically. Although commonly invoked in the computational literature, the argument from the cognitive science practice has never been discussed in detail. In this paper, we shall provide a critical reconstruction of this argument and an extensive analysis of its prospects, taking into account some ways of defending it that have never been explored so far. We shall argue that explanatory considerations support at best a weak version of the argument from the cognitive science practice, according to which semantic properties concur with formal syntactic properties in individuating computations in cognitive science, but not a strong version, according to which computation individuation in cognitive science is semantic as opposed to formal syntactic.

The Impact of Using the Total Cost of Ownership (TCO) Account for a Reusable Wooden Flat Pallet in Its Operational Phase on Respecting the Principles of Sustainable Development

Despite the commonly observed trend towards mechanization and automation of operational processes, the potential benefits of wooden pallets as an essential element of the infrastructure of logistic processes are often overlooked in considerations related to sustainable development. Aspects that are mentioned more often include the very idea of the economy itself (circular economy), characteristics of logistics (green), features of the supply chain itself (sustainable) or expectations towards transport (ecological). The authors believe that the idea of total cost of ownership (TCO) in relation to wooden pallets can be a key component of holistic thinking in terms of sustainable development. In a situation where in relation to logistics, reasonable expectations for developing sustainable supply chains are made, paying attention to such a common logistic facility, namely a cargo pallet, which is given so little attention in research, is, in the opinion of the authors, absolutely justified. Therefore, the article presents an original approach to the problem of aggregation of all costs that cargo pallets generate in their operational life cycle, using the total cost of ownership (TCO) analysis methodology. The main goal of the article, however, is to show that the total cost of ownership of a pallet (not only owning it) can become an effective tool used to significantly reduce the costs of logistic activity of enterprises (as well as whole supply chains) and support the idea of sustainable development in practice. Using the primary data from questionnaire research, the focus was on considerations that were of identification character (cognitive and explanatory considerations), which are typical for basic research that aims to explain given phenomena. Thus, the presented cognitive process covers two main areas, namely: the general theory of sustainable development and the specificity of wooden pallets as carriers used in goods trading in terms of their total costs of ownership.

The derivation of Poiseuille’s law: heuristic and explanatory considerations

The derivation of Poiseuille’s law: heuristic and explanatory considerations

The inchworm episode: Reconstituting the phenomenon of kinesin motility

New Mechanist philosophical models of "phenomenon reconstitution" understand the process to be driven by explanatory considerations. Here I discuss an episode of phenomenon reconstitution that occurred entirely within an experimental program dedicated to characterizing (rather than explaining) the phenomenon of kinesin motility. Rather than being driven by explanatory considerations, as standard mechanist views maintain, I argue that the phenomenon of kinesin motility was reconstituted to enhance researchers’ primary experimental tool—the single molecule motility assay.

Inference to the Best Explanation, Relative Plausibility, and Probability

For over fifty years, legal proof was thought to be best understood as embodying or implementing various aspects of probability theories. These probability-based explanations neglected the extent to which explanatory considerations explain juridical proof, and a paradigm shift is now occurring from probabilistic to explanatory-based understanding of juridical proof. Similar to many scientific practices, juridical proof—from beginning to end—turns on how well the evidence would explain certain conclusions, and the legal process is structured to facilitate such an inquiry. This inferential process, well known in the philosophy of science, is referred to as abduction or “inference to the best explanation.” In this chapter, we provide an account of the process in general and an explanation-based account of juridical proof in particular—the latter, due to ecological differences between the law and other settings, deviates somewhat from the philosophical debates over IBE. We then compare the explanatory approach with probability approaches and demonstrate the decided inferiority of the latter. We conclude with the theoretical and practical consequences of the debate.

Bohmian Mechanics: Realism and the “Box” Experiment

It is difficult to articulate how we should take a realist attitude towards Bohmian mechanics because there are many versions of it. This paper aims to clarify the realist commitments of Bohmian mechanics and how we can understand it from a general scientific realist perspective. I use the box experiment, a double-slit like experiment conducted by Cardone et al. (Phys Lett A 326(1–2):1–13, 2004; Int J Mod Phys B 20(09):1107–1121, 2006), as a working example to argue that a causal realist account (in the sense of selective realism) that is applicable to Bohmian mechanics, has to be supplemented with the use of Inference to the Best Explanation. The reason is because causal realism on its own does not form a sufficient basis for realism about Bohmian mechanics. In particular, I argue that the existence of the pilot wave explains why we observe an anomalous interference effect in the experiment of Cardone et al. The conclusion to draw is that a complete realist account about Bohmian mechanics rests on explanatory considerations.

Can the Evidence for Explanatory Reasoning Be Explained Away?

Recent evidence appears to show a close connection between explanation and belief revision, specifically, the revision of graded beliefs. Insofar as this is also evidence of violations of Bayesian norms of reasoning, the question arises whether we are facing a new bias here, on a par with previously discovered biases in probabilistic reasoning. We consider an apparently successful attempt by Costello and Watts to explain away a number of known such biases in terms of sampling error, which makes those biases look entirely innocuous and compatible with the descriptive adequacy of Bayesian psychology in any but the most uninteresting way. Specifically, we query whether this attempt can be extended to neutralize the aforementioned evidence allegedly showing that explanatory considerations influence our reasoning in ways inconsistent with Bayesian prescriptions.

Legal Probabilism—A Qualified Rejection: A Response to Hedden and Colyvan

The best explanation of juridical proof has evolved from probabilism to explanationism. In a recent article, Brian Hedden and Mark Colyvan criticize the evolution from probability to explanation based on a sparse review of some of the pertinent literature, joining a cottage industry of experts from cognate disciplines attempting to rescue probability theory from the corroding effects of legal scholarship. Their major point is to “fault” this transformation “for assuming an outdated and inexhaustive catalogue of ‘interpretations’ of probability. . . which . . . ignores the possibility of integrating the explanatory and other epistemological considerations . . . into a probabilistic framework.” This claim is false. The legal literature has vetted the two decades old literature that Hedden and Colyvan refer to as a new development in epistemology, which in any event has no value in resolving the problems the legal literature on juridical proof addresses. A proper understanding of both the dated literature that Hedden and Colyvan refer to, and what actually are “new developments” in epistemology support the progression from probabilism to explanationism as the best explanation of juridical proof. In addition, H&C make a fundamental error about what the conceptual problem is. Together these errors vitiate their criticisms of explanationism as the best explanation of juridical proof at present. Correcting these mistakes and misapprehensions may assist those in other fields in their understanding of what the actual issues are.

Predicting loan default in peer‐to‐peer lending using narrative data

AbstractPeer‐to‐peer (P2P) lending is facing severe information asymmetry problems and depends highly on the internal credit scoring system. This paper provides a novel credit scoring model, which forecasts the probability of default for each applicant and guides the lenders' decision‐making in P2P lending. The proposal is expected to improve the existing credit scoring models in P2P lending from two aspects, namely the classifier and the usage of narrative data. We utilize an advanced gradient boosting decision tree technique (i.e., CatBoost) to predict default loans. Moreover, a soft information extraction technique based on keyword clustering is developed to compensate for the insufficient hard credit data. Validated on three real‐world datasets, the experimental results demonstrate that variables extracted from narrative data are powerful features, and the utilization of narrative data significantly improves the predictability relative to solely using hard information. The results of sensitivity analysis reveal that CatBoost outperforms the industry benchmark under different cluster numbers of extracted soft information; meanwhile a small number of clusters (e.g., three) is preferred for consideration of model performance, computational cost, and comprehensibility. We finally facilitate a discussion on practical implication and explanatory considerations.

The ecological rationality of explanatory reasoning

There is growing evidence that explanatory considerations influence how people change their degrees of belief in light of new information. Recent studies indicate that this influence is systematic and may result from people’s following a probabilistic update rule. While formally very similar to Bayes’ rule, the rule or rules people appear to follow are different from, and inconsistent with, that better-known update rule. This raises the question of the normative status of those updating procedures. Is the role explanation plays in people’s updating their degrees of belief a bias? Or are people right to update on the basis of explanatory considerations, in that this offers benefits that could not be had otherwise? Various philosophers have argued that any reasoning at deviance with Bayesian principles is to be rejected, and so explanatory reasoning, insofar as it deviates from Bayes’ rule, can only be fallacious. We challenge this claim by showing how the kind of explanation-based update rules to which people seem to adhere make it easier to strike the best balance between being fast learners and being accurate learners. Borrowing from the literature on ecological rationality, we argue that what counts as the best balance is intrinsically context-sensitive, and that a main advantage of explanatory update rules is that, unlike Bayes’ rule, they have an adjustable parameter which can be fine-tuned per context. The main methodology to be used is agent-based optimization, which also allows us to take an evolutionary perspective on explanatory reasoning.

Why Explanatory Considerations Matter

This paper aims at elucidating the connection between explanatory considerations and epistemic justification stipulated by explanationism which take epistemic justification to be definable in terms of best explanations. By relying on the notion of truthlikeness, this paper argues that it is rational for a subject to expect the best explanation she has for her evidence to be more truthlike than any of the other potential explanations available to her by virtue of containing a class of propositions that, given her evidence, she is justified in believing. Based on this elucidation of the connection between explanatory considerations and epistemic justification, an explanationist account of the evidential support relation is then offered.

Relevance, probative value, and explanatory considerations

In my short contribution to this issue, I focus on Allen and Pardo proposal to redefine key evidentiary concepts in explanatory terms, with special attention to ‘relevancy’ and ‘probative value’. I also join forces with Allen and Pardo in replying to critics to explanatory approaches to evidence law.

Best, second-best, and good-enough explanations: How they matter to reasoning.

There is a wealth of evidence that people's reasoning is influenced by explanatory considerations. Little is known, however, about the exact form this influence takes, for instance about whether the influence is unsystematic or because of people's following some rule. Three experiments investigate the descriptive adequacy of a precise proposal to be found in the philosophical literature, to wit, that we should infer to the best explanation, provided certain additional conditions are met. The first experiment studies the relation between the quality of an explanation and people's willingness to infer that explanation when only one candidate explanation is given. The second experiment presents participants always with two explanations and investigates the effect of the presence of an alternative on the participants' willingness to infer the target explanation. Although Experiments 1 and 2 manipulate explanation quality and willingness to infer to the best explanation between participants, Experiment 3 manipulates those measures within participants, thereby allowing to study the influence of explanatory considerations on inference at the individual level. The third experiment also studies the connection between explanation quality, willingness to infer, and metacognitive confidence in the decision to infer. The main conclusions that can be drawn from these experiments are that (a) the quality of an explanation is a good predictor of people's willingness to accept that explanation, and a better predictor than the prior probability of the explanation, and (b) if more than one possible explanation is given, people are the less willing to infer the best explanation the better they deem the second-best explanation. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

Can there be a burden of the best explanation?

In this article I address a foundational question in evidence law: how should judges and jurors reason with evidence? According to a widely accepted approach, legal fact-finding should involve a determination of whether each cause of action is proven to a specific probability. In most civil cases, the party carrying the burden of persuasion is said to need to persuade triers that the facts she needs to prevail are “more likely than not” true. The problem is that this approach is both a descriptively and normatively inadequate account of reasoning with evidence in law. It does not offer a plausible picture of how people in general, and legal fact-finders in particular, reason with evidence. And it turns out that if we try to do what the approach tells us, we end up with absurd results. Faced with these difficulties, a group of evidence scholars has proposed an alternative. According to them, legal fact-finding should involve a determination of which hypothesis best explains the admitted evidence, rather than whether each cause of action is proven to a specific probability. My main contributions in this article are twofold. First, I elaborate on the many descriptive, normative and explanatory considerations in support of an explanation-based approach to standards. Second, I offer novel replies to pressing objections against that same approach.

The heuristic conception of inference to the best explanation

An influential suggestion about the relationship between Bayesianism and inference to the best explanation (IBE) holds that IBE functions as a heuristic to approximate Bayesian reasoning. While this view promises to unify Bayesianism and IBE in a very attractive manner, important elements of the view have not yet been spelled out in detail. I present and argue for a heuristic conception of IBE on which IBE serves primarily to locate the most probable available explanatory hypothesis to serve as a working hypothesis in an agent’s further investigations. Along the way, I criticize what I consider to be an overly ambitious conception of the heuristic role of IBE, according to which IBE serves as a guide to absolute probability values. My own conception, by contrast, requires only that IBE can function as a guide to the comparative probability values of available hypotheses. This is shown to be a much more realistic role for IBE given the nature and limitations of the explanatory considerations with which IBE operates.