Explanatory warrant for scientific realism

Nancy Cartwright relies upon an inference pattern known as inference to the best causal explanation (IBCE) to support a limited form of entity realism, according to which we are warranted in believing in entities that purportively cause observable effects. IBCE, as usually understood, is valid, even though all other forms of inference to the best explanation (IBE) are usually understood to be invalid. We argue that IBCE and IBE are in the same boat with respect to their ability to support realist conclusions. Either rule can be interpreted as valid, this is a matter of semantic convention. However, doing so deprives the rule of the empirical content the realist needs, requiring the realist to find independent warrant for a strong (theoretical or causal) premise. We then examine the proposed means of obtaining this warrant, and find them as inadequate in the case of IBCE as they are in the case of IBE.     

Coherence measures and inference to the best explanation

This paper considers an application of work on probabilistic measures of coherence to inference to the best explanation (IBE). Rather than considering information reported from different sources, as is usually the case when discussing coherence measures, the approach adopted here is to use a coherence measure to rank competing explanations in terms of their coherence with a piece of evidence. By adopting such an approach IBE can be made more precise and so a major objection to this mode of reasoning can be addressed. Advantages of the coherence-based approach are pointed out by comparing it with several other ways to characterize ‘best explanation’ and showing that it takes into account their insights while overcoming some of their problems. The consequences of adopting this approach for IBE are discussed in the context of recent discussions about the relationship between IBE and Bayesianism.     

Prime and probability: causal knowledge affects inferential and predictive effects on self-agency experiences

Experiences of having caused a certain outcome may arise from motor predictions based on action–outcome probabilities and causal inferences based on pre-activated outcome representations. However, when and how both indicators combine to affect such self-agency experiences is still unclear. Based on previous research on prediction and inference effects on self-agency, we propose that their (combined) contribution crucially depends on whether people have knowledge about the causal relation between actions and outcomes that is relevant to subsequent self-agency experiences. Therefore, we manipulated causal knowledge that was either relevant or irrelevant by varying the probability of co-occurrence (50% or 80%) of specific actions and outcomes. Afterwards, we measured self-agency experiences in an action–outcome task where outcomes were primed or not. Results showed that motor prediction only affected self-agency when relevant actions and outcomes were learned to be causally related. Interestingly, however, inference effects also occurred when no relevant causal knowledge was acquired.     

The fine-grained metaphysics of artifactual and biological functional kinds

In this paper we consider the emerging position in metaphysics that artifact functions characterize real kinds of artifacts. We analyze how it can circumvent an objection by David Wiggins (Sameness and substance renewed, 2001, 87) and then argue that this position, in comparison to expert judgments, amounts to an interesting fine-grained metaphysics: taking artifact functions as (part of the) essences of artifacts leads to distinctions between principles of activity of artifacts that experts in technology have not yet made. We show, moreover, that our argument holds not only in the artifactual realm but also in biology: taking biological functions as (part of the) essences of organs leads to distinctions between principles of activity of organs that biological experts have not yet made. We run our argument on the basis of analyses of artifact and biological functions as developed in philosophy of technology and of biology, thus importing results obtained outside of metaphysics into the debate on ontological realism. In return, our argument shows that a position in metaphysics provides experts reason for trying to detect differences between principles of activities of artifacts and organs that have not been detected so far.     

The probability of causal conditionals

Conditionals in natural language are central to reasoning and decision making. A theoretical proposal called the Ramsey test implies the conditional probability hypothesis: that the subjective probability of a natural language conditional, P(if p then q), is the conditional subjective probability, P(q/p). We report three experiments on causal indicative conditionals and related counterfactuals that support this hypothesis. We measured the probabilities people assigned to truth table cases, P(pq), P(p notq), P( notpq) and P( notp notq). From these ratings, we computed three independent predictors, P(p), P(q/p) and P(q/ notp), that we then entered into a regression equation with judged P(if p then q) as the dependent variable. In line with the conditional probability hypothesis, P(q/p) was by far the strongest predictor in our experiments. This result is inconsistent with the claim that causal conditionals are the material conditionals of elementary logic. Instead, it supports the Ramsey test hypothesis, implying that common processes underlie the use of conditionals in reasoning and judgments of conditional probability in decision making.     

Can the skepticism debate be resolved?

External world skeptics are typically opposed to admitting as evidence anything that goes beyond the purely phenomenal, and equally typically, they disown the use of rules of inference that might enable one to move from premises about the phenomenal alone to a conclusion about the external world. This seems to bar any a posteriori resolution of the skepticism debate. This paper argues that the situation is not quite so hopeless, and that an a posteriori resolution of the debate becomes possible once it is recognized that the skeptic holds overly defensive and ill-motivated positions vis-à-vis both evidence and inference, and that more reasonable ones are available. In stating these more reasonable positions, as well as in showing how they make possible an a posteriori resolution of the skepticism debate, the paper draws on the machinery of Bayesian epistemology.     

Consciousness and inference to the best explanation: Compiling empirical evidence supporting the access-phenomenal distinction and the overflow hypothesis

A tacit assumption in the field of consciousness studies is that the more empirical evidence a theory can explain, the better it fares when weighed against competitors. If one wants to take seriously the potential for empirical evidence to move forward debates in consciousness studies, there is a need to gather, organize, validate, and compare evidence. We present an inference to the best explanation (IBE) process on the basis of empirical support that is applicable in debates between competing theories of consciousness. Our proposed IBE process consists in four steps: Assimilate, Compile, Validate, and Compare. Until now, the vast majority of the work in the field has consisted in gathering empirical evidence for theories i.e., the assimilation step. To illustrate the feasibility of our proposed IBE process, and what it may look like when applied in practice, we deliver a complete collection (the compilation step) of empirical support for the distinction between A-Consciousness and P-Consciousness and the overflow hypothesis. Finally, we offer an example of the validation step, by scrutinizing the interpretation of aphantasics’ performance on retro-cue paradigms offered in the literature in support of the overflow hypothesis. The compilation we deliver here is the first effort in the IBE process, the end result of which — hopefully — will be the ability of the research community to carry out side-by-side comparisons of theories and the empirical phenomena they claim to explain, i.e., the comparison step.     

Contrast,inference and scientific realism

The thesis of underdetermination presents a major obstacle to the epistemological claims of scientific realism. That thesis is regularly assumed in the philosophy of science, but is puzzlingly at odds with the actual history of science, in which empirically adequate theories are thin on the ground. We propose to advance a case for scientific realism which concentrates on the process of scientific reasoning rather than its theoretical products. Developing an account of causal–explanatory inference will make it easier to resist the thesis of underdetermination. For, if we are not restricted to inference to the best explanation only at the level of major theories, we will be able to acknowledge that there is a structure in data sets which imposes serious constraints on possible theoretical alternatives. We describe how Differential Inference, a form of inference based on contrastive explanation, can be used in order to generate causal hypotheses. We then go on to consider how experimental manipulation of differences can be used to achieve Difference Closure, thereby confirming claims of causal efficacy and also eliminating possible confounds. The model of Differential Inference outlined here shows at least one way in which it is possible to ‘reason from the phenomena’.     

Serious metaphysics and the vindication of reductions

What would be sufficient to show of some apparently higher-level property that it is ‘nothing over and above’ some complex configuration of more basic properties? This paper defends a new method for justifying reductions by demonstrating its comparative advantages over two methods recently defended in the literature. Unlike its rivals, what I’ll call “the semantic method” makes a reduction’s truth epistemically transparent without relying on conceptual analyses.     

BonJour’s Abductivist Reply to Skepticism

The abductivist reply to skepticism is the view that commonsense explanations of the patterns and regularities that appear in our sensory experiences should be rationally preferred to skeptical explanations of those same patterns and regularities on the basis of explanatory considerations. In this article I critically examine Laurence BonJour’s rationalist version of the abductivist position. After explaining why BonJour’s account is more defensible than other versions of the view, I argue that the notion of probability he relies upon is deeply problematic, that he incorporates an implausible double-standard concerning a priori and a posteriori justification, and that his view is vulnerable to skepticism about the a priori. I suggest that some of these problems are due to idiosyncratic commitments BonJour makes and that abductivists would be better off without them. I conclude with some suggestions about how to improve BonJour’s abductivist response to skepticism.

On classifying abduction

We can witness the recent surge of interest in classifying different patterns or types of abduction. Many philosophers have suggested their own classifications emphasizing different aspects of abduction. Such a development is remarkable, in view of the fact that until quite recently the focus of the research on Peircean abduction was to identify its logical form. Another agenda in the recent attempts to classify abduction is whether to allow non-explanatory abductions. In order to resolve these two closely related issues, I propose to examine how Peirce would have responded to them. In particular, I suggest to do this in connection with Peirce's another life-long project, the classification of sciences. In this examination, it will be shown that Peirce struggled with the problem of conflating induction and abduction. I shall discuss how this problem influenced both Peirce's views on the interrelationship between abduction, deduction, and induction on the one hand, and his many classifications of sciences on the other. Also, the implication of the fundamental change in Peirce's views of abduction, deduction, and induction to the problem of the classification of sciences will be uncovered. Finally, I shall discuss whether inference to the best explanation is abduction. Insofar as this problem has bearing on the two controversial issues in classifying abduction, my negative answer will demonstrate that classifying abduction is yet to get off the ground.     

Underdetermination and Rational Choice of Theories

The underdetermination of theory by data argument (UD) is traditionally construed as an argument that tells us that we ought to favour an anti-realist position over a realist position. I argue that when UD is constructed as an argument saying that theory choice is to proceed between theories that are empirically equivalent and adequate to the phenomena up until now, the argument will not favour constructive empiricism over realism. A constructive empiricist cannot account for why scientists are reasonable in expecting one theory to be empirically adequate rather than another, given the criteria he suggests for theory choice.

Naturalism, Science and the Supernatural

There is overwhelming agreement amongst naturalists that a naturalistic ontology should not allow for the possibility of supernatural entities. I argue, against this prevailing consensus, that naturalists have no proper basis to oppose the existence of supernatural entities. Naturalism is characterized, following Leiter and Rea, as a position which involves a primary commitment to scientific methodology and it is argued that any naturalistic ontological commitments must be compatible with this primary commitment. It is further argued that properly applied scientific method has warranted the acceptance of the existence of supernatural entities in the past and that it is plausible to think that it will do so again in the future. So naturalists should allow for the possibility of supernatural entities.

Back to the future: Temporal perspective in the explanation of events

Prospective hindsight involves generating an explanation for a future event as if it had already happened; i.e., one goes forward in time, and then looks back. In order to examine how shifts in perspective might influence people's perceptions of events, we investigated two possible factors: temporal perspective (whether an event is set in the future or past) and uncertainty (whether the event's occurrence is certain or uncertain). In the first experiment, temporal perspective showed little influence while outcome uncertainty strongly affected the nature of explanations for events. Explanations for sure events tended to be longer, to contain a higher proportion of episodic reasons, and to be expressed in past tense. Evidence from the second experiment supports the view that uncertainty mediates not the amount of time spent explaining, but rather subjects' choice of explanation type. The implications of these findings for the use of temporal perspective in decision aiding are discussed.     

Error probabilities for inference of causal directions

A main message from the causal modelling literature in the last several decades is that under some plausible assumptions, there can be statistically consistent procedures for inferring (features of) the causal structure of a set of random variables from observational data. But whether we can control the error probabilities with a finite sample size depends on the kind of consistency the procedures can achieve. It has been shown that in general, under the standard causal Markov and Faithfulness assumptions, the procedures can only be pointwise but not uniformly consistent without substantial background knowledge. This implies the impossibility of choosing a finite sample size to control the worst case error probabilities. In this paper, I consider the simpler task of inferring causal directions when the skeleton of the causal structure is known, and establish a similarly negative result concerning the possibility of controlling error probabilities. Although the result is negative in form, it has an interesting positive implication for causal discovery methods.     

Independence and dependence in human causal reasoning

Causal graphical models (CGMs) are a popular formalism used to model human causal reasoning and learning. The key property of CGMs is the causal Markov condition, which stipulates patterns of independence and dependence among causally related variables. Five experiments found that while adult’s causal inferences exhibited aspects of veridical causal reasoning, they also exhibited a small but tenacious tendency to violate the Markov condition. They also failed to exhibit robust discounting in which the presence of one cause as an explanation of an effect makes the presence of another less likely. Instead, subjects often reasoned “associatively,” that is, assumed that the presence of one variable implied the presence of other, causally related variables, even those that were (according to the Markov condition) conditionally independent. This tendency was unaffected by manipulations (e.g., response deadlines) known to influence fast and intuitive reasoning processes, suggesting that an associative response to a causal reasoning question is sometimes the product of careful and deliberate thinking. That about 60% of the erroneous associative inferences were made by about a quarter of the subjects suggests the presence of substantial individual differences in this tendency. There was also evidence that inferences were influenced by subjects’ assumptions about factors that disable causal relations and their use of a conjunctive reasoning strategy. Theories that strive to provide high fidelity accounts of human causal reasoning will need to relax the independence constraints imposed by CGMs.     

论达尔文的最佳解释推理

科学研究需要正确的思维方法的指导,而最佳解释推理就是一种重要的思维方法。达尔文运用最佳解释推理论证通过自然选择而进化的理论最为成功,在《物种起源》中,达尔文以解释力、解释的一致性为基础,引用大量事实,充分比较了自然选择理论与上帝的独立创造说或神创论,符合最佳解释推理模型的评价标准。     

Ambiguity and explanation

This paper presents evidence that ‘because’ is importantly ambiguous between two closely related senses covering what are usually called causal explanations, on the one hand, and grounding or metaphysical explanations, on the other hand. To this end, it introduces the lexical categories of monosemy, polysemy and homonymy; describes a test for polysemy; and discusses the results of the test when applied to ‘because’. It also shows how to understand so-called hybrid explanations in light of the semantic facts established by the analysis.     

Patterns of abduction

This article describes abductions as special patterns of inference to the best explanation whose structure determines a particularly promising abductive conjecture (conclusion) and thus serves as an abductive search strategy (Sect. 1). A classification of different patterns of abduction is provided which intends to be as complete as possible (Sect. 2). An important distinction is that between selective abductions, which choose an optimal candidate from given multitude of possible explanations (Sects. 3–4), and creative abductions, which introduce new theoretical models or concepts (Sects. 5–7). While selective abduction has dominated the literature, creative abductions are rarely discussed, although they are essential in science. The article introduces several kinds of creative abductions, such as theoretical model abduction, common cause abduction and statistical factor analysis, and illustrates them by various real case examples. It is suggested to demarcate scientifically fruitful abductions from purely speculative abductions by the criterion of causal unification (Sect. 7.1).