This book investigates the role of inference in argumentation, considering how arguments support standpoints on the basis of different loci. The authors propose and illustrate a model for the analysis of the standpoint-argument connection, called Argumentum Model of Topics. A prominent feature of the AMT is that it distinguishes, within each and every single argumentation, between an inferential-procedural component, on which the reasoning process is based; and a material-contextual component, which anchors the argument in the interlocutors’ cultural and factual (...) common ground. The AMT explains how these components differ and how they are intertwined within each single argument. This model is introduced in Part II of the book, following a careful reconstruction of the enormously rich tradition of studies on inference in argumentation, from the antiquity to contemporary authors, without neglecting medieval and post-medieval contributions. The AMT is a contemporary model grounded in a dialogue with such tradition, whose crucial aspects are illuminated in this book. (shrink)

This book grew out of previously published papers of mine composed over a period of years; they have been reworked (sometimes beyond recognition) so as to form a reasonably coherent whole. Part One treats of informative inference. I argue (Chapter 2) that the traditional principle of induction in its clearest formulation (that laws are confirmed by their positive cases) is clearly false. Other formulations in terms of the 'uniformity of nature' or the 'resemblance of the future to the past' (...) seem to me hopelessly unclear. From a Bayesian point of view, 'learning from experience' goes by conditionalization (Bayes' rule). The traditional stum bling block for Bayesians has been to fmd objective probability inputs to conditionalize upon. Subjective Bayesians allow any probability inputs that do not violate the usual axioms of probability. Many subjectivists grant that this liberality seems prodigal but own themselves unable to think of additional constraints that might plausibly be imposed. To be sure, if we could agree on the correct probabilistic representation of 'ignorance' (or absence of pertinent data), then all probabilities obtained by applying Bayes' rule to an 'informationless' prior would be objective. But familiar contra dictions, like the Bertrand paradox, are thought to vitiate all attempts to objectify 'ignorance'. BuUding on the earlier work of Sir Harold Jeffreys, E. T. Jaynes, and the more recent work ofG. E. P. Box and G. E. Tiao, I have elected to bite this bullet. In Chapter 3, I develop and defend an objectivist Bayesian approach. (shrink)

An influential suggestion about the relationship between Bayesianism and inference to the best explanation 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. (shrink)

Abstract In his Aquinas Lecture 1992 at Marquette University, Ernan McMullin discusses whether there is a pattern of inference that particularly characterizes the sciences of nature. He pursues this theme both on a historical and a systematic level. There is a continuity of concern across the ages that separate the Greek inquiry into nature from our own vastly more complex scientific enterprise. But there is also discontinuity, the abandonment of earlier ideals as unworkable. The natural sciences involve many types (...) of inference; three of these interlock in a special way to produce “retroductive inference,” the kind of complex inference that supports causal theory. (shrink)

To learn about real world phenomena, scientists have traditionally used models with clearly interpretable elements. However, modern machine learning (ML) models, while powerful predictors, lack this direct elementwise interpretability (e.g. neural network weights). Interpretable machine learning (IML) offers a solution by analyzing models holistically to derive interpretations. Yet, current IML research is focused on auditing ML models rather than leveraging them for scientific inference. Our work bridges this gap, presenting a framework for designing IML methods—termed ’property descriptors’—that illuminate not (...) just the model, but also the phenomenon it represents. We demonstrate that property descriptors, grounded in statistical learning theory, can effectively reveal relevant properties of the joint probability distribution of the observational data. We identify existing IML methods suited for scientific inference and provide a guide for developing new descriptors with quantified epistemic uncertainty. Our framework empowers scientists to harness ML models for inference, and provides directions for future IML research to support scientific understanding. (shrink)

In my experience, various postgraduate students and sometimes lecturers think about the history of ideas in a certain way. There is a question within a field and there are (or is) a certain number of answers worth considering, not very many, 4 let us say. Each contributor is interested in making a new contribution and pursues this goal rationally. So contributor number 1 offers one of these answers, contributor number 2 offers a second answer, number 3 offers a third answer, (...) and number 4 a fourth answer. All the positions are soon taken. This is a simple rational actor model, like those used in economics. But does actual history correspond to the model? I shall focus on one case, in the field of logic, concerning the question of why not treat the inference to a conclusion as a premise. In this case, actual history is different. (NOTE: this is just the abstract; you need to download the PDF probably.). (shrink)

The background target of the research going into the present article is to forge an intellectual alliance between, on the one hand, active inference and the free-energy principle (FEP), and on the other, Charles S. Peirce’s theory of semiotics and pragmatism. In the present paper, the focus is on the allegiance between the nomenclatures of active and abductive inferences as the proper place to begin reaching at that wider target. The paper outlines the key conceptual elements involved in a (...) naturalistic rendering of Peirce’s late semiotic and logical notion of abductive reasoning. The target is a cognitive-biological model of abduction which preserves the functional integrity of an organism and fulfils the existential imperative for living beings’ evidence of existence. Such a model is an adaptation of Peirce’s late logical schema of abduction proposed in his largely unpublished works during the early 20th century. The proposed model is argued to be a feasible sketch also of recent breakthroughs in computational (sensu Bayesian) cognitive science. (shrink)

This paper examines the justification for the hypothesis of extended cognition. HEC claims that human cognitive processes can, and often do, extend outside our head to include objects in the environment. HEC has been justified by inference to the best explanation. Both advocates and critics of HEC claim that we can infer the truth value of HEC based on whether HEC makes a positive or negative explanatory contribution to cognitive science. I argue that IBE cannot play this epistemic role. (...) A serious rival to HEC exists with a differing truth value, and this invalidates IBEs for both the truth and the falsity of HEC. Explanatory value to cognitive science is not a guide to the truth value of HEC.Keywords: Extended mind; Extended cognition; Embedded cognition; Externalism; Inference to the best explanation; Functionalism. (shrink)

This paper deals with the question of agency and intentionality in the context of the free-energy principle. The free-energy principle is a system-theoretic framework for understanding living self-organizing systems and how they relate to their environments. I will first sketch the main philosophical positions in the literature: a rationalist Helmholtzian interpretation (Hohwy 2013; Clark 2013), a cybernetic interpretation (Seth 2015b) and the enactive affordance-based interpretation (Bruineberg and Rietveld 2014; Bruineberg et al. 2016) and will then show how agency and intentionality (...) are construed differently on these different philosophical interpretations. I will then argue that a purely Helmholtzian is limited, in that it can account only account for agency in the context of perceptual inference. The cybernetic account cannot give a full account of action, since purposiveness is accounted for only to the extent that it pertains to the control of homeostatic essential variables. I will then argue that the enactive affordance-based account attempts to provide broader account of purposive action without presupposing goals and intentions coming from outside of the theory. In the second part of the paper, I will discuss how each of these three interpretations conceives of the sense agency and intentionality in different ways. (shrink)

Current debates surrounding the virtues and shortcomings of randomization are symptomatic of a lack of appreciation of the fact that causation can be inferred by two distinct inference methods, each requiring its own, specific experimental design. There is a non-statistical type of inference associated with controlled experiments in basic biomedical research; and a statistical variety associated with randomized controlled trials in clinical research. I argue that the main difference between the two hinges on the satisfaction of the comparability (...) requirement, which is in turn dictated by the nature of the objects of study, namely homogeneous or heterogeneous populations of biological systems. Among other things, this entails that the objection according to which randomized experiments fail to provide better evidence for causation because randomization cannot guarantee comparability is mistaken. (shrink)

Although both philosophers and scientists are interested in how to obtain reliable knowledge in the face of error, there is a gap between their perspectives that has been an obstacle to progress. By means of a series of exchanges between the editors and leaders from the philosophy of science, statistics and economics, this volume offers a cumulative introduction connecting problems of traditional philosophy of science to problems of inference in statistical and empirical modelling practice. Philosophers of science and scientific (...) practitioners are challenged to reevaluate the assumptions of their own theories - philosophical or methodological. Practitioners may better appreciate the foundational issues around which their questions revolve and thereby become better 'applied philosophers'. Conversely, new avenues emerge for finally solving recalcitrant philosophical problems of induction, explanation and theory testing. (shrink)

How do we reconcile the claim of Bayesianism to be a correct normative theory of scientific reasoning with the explanationists’ claim that Inference to the Best Explanation (IBE) provides a correct description of our inferential practices? This chapter articulates and defends a version of the heuristic defence of IBE. Three challenges remain that focus on the idea that IBE can lead to knowledge. Answering those challenges requires renouncing standard Bayesianism’s commitment to personalism, while also going beyond objective Bayesianism regarding (...) the constraints on good priors. The result is a non-standard, super-objective Bayesianism that identifies probabilities with evaluations of plausibility in the light of the evidence. (shrink)

Recently, the mechanistic framework of active inference has been put forward as a principled foundation to develop an overarching theory of consciousness which would help address conceptual disparities in the field (Wiese 2018 ; Hohwy and Seth 2020 ). For that promise to bear out, we argue that current proposals resting on the active inference scheme need refinement to become a process theory of consciousness. One way of improving a theory in mechanistic terms is to use formalisms such (...) as computational models that implement, attune and validate the conceptual notions put forward. Here, we examine how computational modelling approaches have been used to refine the theoretical proposals linking active inference and consciousness, with a focus on the extent and success to which they have been developed to accommodate different facets of consciousness and experimental paradigms, as well as how simulations and empirical data have been used to test and improve these computational models. While current attempts using this approach have shown promising results, we argue they remain preliminary in nature. To refine their predictive and structural validity, testing those models against empirical data is needed i.e., new and unobserved neural data. A remaining challenge for active inference to become a theory of consciousness is to generalize the model to accommodate the broad range of consciousness explananda; and in particular to account for the phenomenological aspects of experience. Notwithstanding these gaps, this approach has proven to be a valuable avenue for theory advancement and holds great potential for future research. (shrink)

In this chapter I examine past and recent theories of unconscious inference. Most theorists have ascribed inferences to perception literally, not analogically, and I focus on the literal approach. I examine three problems faced by such theories if their commitment to unconscious inferences is taken seriously. Two problems concern the cognitive resources that must be available to the visual system (or a more central system) to support the inferences in question. The third problem focuses on how the conclusions of (...) inferences are supposed to explain the phenomenal aspects of visual experience, the looks of things. Finally, in comparing past and recent responses to these problems, I provide an assessment of the current prospects for inferential theories. (This paper is reprinted in Hatfield 2009, Perception and Cognition: Essays in the Philosophy of Psychology, Clarendon Press, 124-152.). (shrink)

This paper proposes that mathematicians routinely use inference to the best explanation (IBE) to confirm their conjectures. Mathematicians can justly reason that the ‘best explanation’ of some mathematical evidence they possess would be a proof of it that likewise proves a given conjecture. By IBE, the evidence thereby confirms that such an as-yet-undiscovered proof exists and that the conjecture holds. This reasoning can be expressed in Bayesian terms once Bayesianism’s logical omniscience has been circumvented. A Bayesian analysis identifies considerations (...) affecting a mathematical IBE’s strength and helps to unify mathematical IBEs with scientific IBEs. (shrink)

In this chapter, I examine John Henry Newman’s epistemology in A Grammar of Assent, focusing on the relationship between inquiry, inference, and the unconditional act of assent. I begin by contrasting Newman’s "real" account of how the mind actually functions with John Locke’s "theoretical" requirement that belief must always be proportioned to evidence. I argue that Newman correctly identifies assent as an "all-in" attitude that brings the dynamic process of inquiry—inherently characterized by an "alloy of doubt"—to a close. A (...) central element of my analysis is the "illative sense," the faculty Newman identifies as guiding the transition from the conditional state of inference to the unconditional state of assent. To address the common objection that assent must admit of degrees, I draw on contemporary epistemology, specifically Jane Friedman’s distinction between settledness and stability. I propose that Newman’s "indefectible" assent refers to the binary state of settledness, while the feeling of "strength" in belief refers to stability. Finally, I consider how Newman’s views interact with modern Bayesian frameworks, ultimately showing how his description of everyday assent provides a robust defense for the legitimacy of religious belief. (shrink)

We argue that (1) a purported example of an infinite inference we humans can actually perform admits a faithful, finitary description, and (2) infinite inference contravenes any view which does not grant our minds uncomputable powers. These arguments block the strategy, dating back to Carnap's Logical Syntax of Language, of using infinitary inference rules to secure the determinacy of arithmetical truth on conventionalist grounds.

John D. Norton’s “Material Theory of Induction” has been one of the most intriguing recent additions to the philosophy of induction. Norton’s account appears to be a notably natural account of actual inductive practices, although his theory has attracted considerable criticism. I detail several novel issues for his theory but argue that supplementing the Material Theory with a theory of direct inference could address these problems. I argue that if this combination is possible, a stronger theory of inductive reasoning (...) emerges, which has a more propitious answer to the Problem of Induction. (shrink)

Throughout the Covid-19 pandemic, people have been eager to learn what factors, and especially what public health policies, cause infection rates to wax and wane. But figuring out conclusively what causes what is difficult in complex systems with nonlinear dynamics, such as pandemics. We review some of the challenges that scientists have faced in answering quantitative causal questions during the Covid-19 pandemic, and suggest that these challenges are a reason to augment the moral dimension of conversations about causal inference. (...) We take a lesson from Martha Nussbaum—who cautions us not to think we have just one question on our hands when we have at least two—and apply it to modeling for causal inference in the context of cost-benefit analysis. (shrink)

In this paper we consider logical inference as an activity that results in proofs and hence produces knowledge. We suggest to merge the semantical analysis of deliberatively seeing-to-it-that from stit theory and the semantics of the epistemic logic with justification from. The general idea is to understand proving that A as seeing to it that a proof of A is available. We introduce a semantics of various notions of proving as an activity and present a number of valid principles (...) that relate the various notions of proving to each other and to notions of justified knowledge, implicit knowledge, and possibility. We also point out and comment upon certain principles our semantics fails to validate. (shrink)

Jun Otsuka, in his recent work Thinking About Statistics (2023), undertakes a philosophical investigation of fundamental statistical methodologies, with a particular emphasis on causal inference. In his ontological analysis of causal inference, Otsuka posits that causal analysis, within a given causal model, requires the modification of the underlying probabilistic distribution. This modification, he argues, effectively constitutes a transition between possible worlds. Consequently, Otsuka identifies the objective of causal inference as the discovery of inter-world laws that govern the (...) relationships between these distinct probabilistic models (Otsuka 2023, p.168). Granting Otsuka’s ontological commitments regarding causal and probabilistic models, his interpretation of certain causal analyses as inherently inter-worldly is indeed compelling. This perspective merits particular attention given the prevailing tendency to view such analyses to mere rules for estimating causal effects. While this review does not directly challenge Otsuka’s position, it aims to further explore and contribute to this stimulating concept. (shrink)

Inference to the Best Explanation is an unrivalled exposition of a theory of particular interest to students both of epistemology and the philosophy of science.

This paper considers how we decide whether to believe what we are told. Inference to the Best Explanation, a popular general account of non-demonstrative reasoning, is applied to this task. The core idea of this application is that we believe what we are told when the truth of what we are told would figure in the best explanation of the fact that we were told it. We believe the fact uttered when it is part of the best explanation of (...) the fact of utterance. Having provided some articulation of this account of testimonial inference, the paper goes on to consider whether the account is informative and whether it is plausible. (shrink)

It has been argued that, in scientific observations, the theory of the observed source should not be involved in the observation process to avoid circular reasoning and ensure reliable inferences. However, the issue of underdetermination of the source has been largely overlooked. I argue that concerns about circularity in inferring the source stem from the hypothetico-deductive (H-D) method. The epistemic threat, if any, arises not from the theory-laden nature of observation but from the underdetermination of the source by the data, (...) since the data could be explained by proposing incompatible sources for it. Overcoming this underdetermination is key to reliably inferring the source. I propose a bidirectional version of inference to the only explanation as a methodological framework that addresses this challenge while circumventing concerns about theory-ladenness. Nevertheless, fully justifying the viability of the background theoretical framework and its accurate description of the source requires a broader conception of evidence. To this end, I argue that integrating meta empirical assessment into inference to the only explanation offers a promising strategy, extending the concept of evidence in a justifiable manner. (shrink)

In the Tractatus Wittgenstein criticizes Frege and Russell's view that laws of inference (Schlussgesetze) "justify" logical inferences. What lies behind this criticism, I argue, is an attack on Frege and Russell's conceptions of logical entailment. In passing, I examine Russell's dispute with Bradley on the question whether all relations are "internal".

The idea that simplicity matters in science is as old as science itself, with the much cited example of Ockham's Razor, 'entia non sunt multiplicanda praeter necessitatem': entities are not to be multiplied beyond necessity. A problem with Ockham's razor is that nearly everybody seems to accept it, but few are able to define its exact meaning and to make it operational in a non-arbitrary way. Using a multidisciplinary perspective including philosophers, mathematicians, econometricians and economists, this 2002 monograph examines simplicity (...) by asking six questions: what is meant by simplicity? How is simplicity measured? Is there an optimum trade-off between simplicity and goodness-of-fit? What is the relation between simplicity and empirical modelling? What is the relation between simplicity and prediction? What is the connection between simplicity and convenience? The book concludes with reflections on simplicity by Nobel Laureates in Economics. (shrink)

In Lehrer’s case of the superstitious lawyer, a lawyer possesses conclusive evidence for his client’s innocence, and he appreciates that the evidence is conclusive, but the evidence is causally inert with respect to his belief in his client’s innocence. This case has divided epistemologists ever since Lehrer originally proposed it in his argument against causal analyses of knowledge. Some have taken the claim that the lawyer bases his belief on the evidence as a data point for our theories to accommodate, (...) while others have denied that the lawyer has knowledge, or that he bases his belief on the evidence. In this paper, we move the dialectic forward by way of arguing that the superstitious lawyer genuinely infers his client’s innocence from the evidence. To show that the lawyer’s inference is genuine, we argue in defense of a version of a doxastic construal of the ‘taking’ condition on inference. We also provide a pared-down superstitious lawyer-style case, which displays the key features of the original case without including its complicated and distracting features. But interestingly, although we argue that the lawyer’s belief is based on his good evidence, and is also plausibly doxastically justified, we do not argue that the lawyer knows that his client is innocent. (shrink)

What is an inference and when is an inference deductive rather than inductive, abductive, etc. The goal of this paper is precisely to determine what is that we, humans, do when we engage in deduction, i.e., whether there is something that satisfies both our pre-theoretical intuitions and theoretical presuppositions about deduction, as a cognitive process. The paper is structured in two parts: the first one deals with the issue of what is an inference. There, I will defend (...) the hypothesis that an inference is a causal process where an initial cognitive state causes another in such a way that the causal translation depends essentially on the content of the involved states. In other words, in an inference, a mental state A causes another B, partially because they have the content they have. Based on the conclusion from the first part, I will develop an account of deductive inference in the second part of the article. In order to achieve this, I will start by setting the stage and developing four constraints that an adequate account of deductive inference ought to satisfy. Then, I will present six different hypotheses that one might propose to characterize inferences of this kind. I have divided the proposals in two groups, the first one – which I will call the “logical proposals” – assume that whether an inference is a deduction or not depends exclusively on the content of the mental states involved in the inference, while the second – which I will call the “cognitive proposals” – reject the aforementioned assumption and instead recognize that extra cognitive information is required to determine whether an inference is a deduction or not. I will evaluate each proposal in turn, according to whether they satisfy the constraints introduced at the beginning of this second part. As a result of such evaluation, I will conclude that only one of the cognitive proposals actually satisfies the four constraints and therefore should be considered to give an adequate characterization of deductive inference. (shrink)

How do we go about weighing evidence, testing hypotheses, and making inferences? According to the model of _Inference to the Best Explanation_, we work out what to infer from the evidence by thinking about what would actually explain that evidence, and we take the ability of a hypothesis to explain the evidence as a sign that the hypothesis is correct. In _Inference to the Best Explanation_, Peter Lipton gives this important and influential idea the development and assessment it deserves. The (...) second edition has been substantially enlarged and reworked, with a new chapter on the relationship between explanation and Bayesianism, and an extension and defence of the account of contrastive explanation. It also includes an expanded defence of the claims that our inferences really are guided by diverse explanatory considerations, and that this pattern of inference can take us towards the truth. This edition of _Inference to the Best Explanation_ has also been updated throughout and includes a new bibliography. (shrink)

An interesting consequence of the structuralist theory of truth approximation, as developed in my From Instrumentalism to Constructive Realism, henceforth ICR, concerns so-called ‘inference to the best explanation’. It will be argued that this popular rule among scientific realists can better be replaced by, various kinds of, ‘inference to the best theory’.

In their 2013 paper, William Roche and Elliott Sober used Bayesian confirmation theory and the probabilistic concept of screening-off to pose problems for the epistemological theory of Inference to the Best Explanation. Several philosophers replied to those criticisms and Roche and Sober refined and extended their critique in subsequent papers. This article assesses where the debate now stands.

This paper argues that in mathematical practice, conjectures are sometimes confirmed by “Inference to the Best Explanation” as applied to some mathematical evidence. IBE operates in mathematics in the same way as IBE in science. When applied to empirical evidence, IBE sometimes helps to justify the expansion of scientists’ ontological commitments. Analogously, when applied to mathematical evidence, IBE sometimes helps to justify mathematicians' in expanding the range of their ontological commitments. IBE supplements other forms of non-deductive reasoning in mathematics, (...) avoiding obstacles sometimes faced by enumerative induction or hypothetico-deductive reasoning. Both platonist and non-platonist interpretations of mathematics ought to accommodate explanation in mathematics and ought to recognize IBE in mathematics, though these interpretations disagree on the ontological commitments that mathematicians ought to have. This paper offers an inductive account of why mathematical IBE tends to lead to mathematical truths. (shrink)

This paper investigates the role of Inference to the Best Explanation (IBE) in Explainable Artificial Intelligence (XAI), arguing that IBE can both act as a framework for revealing the considerations and preferences behind XAI explanations and for guiding and evaluating them in a more rigorous manner. The paper focusses specifically on post-hoc explainability and model agnostic techniques, illustrating the argument through examples of salience maps and feature importance techniques. Here, IBE in XAI happens in two steps. The first aims (...) at maximising the range and accuracy of possible inferences made; the second aims to provide a certain balance of explanatory virtues given a certain why-question and stakeholder that requires an explanation. Using the language of explanatory virtues enables a more nuanced discussion about what we may like or dislike about certain explanations in the current XAI landscape and provides actionable ways of improving explanations. (shrink)

Taking the standpoint of monotonicity reasoning, this paper provides a systematic way of looking at the inference pattern mou in the Mohist text. We have taken a logical, as well as a linguistic perspective, emphasizing features of classical Chinese, the role of context, and making use of any possible clues that we can find from the old text. By applying monotonicity rules we provide a uniform account of why shi er ran examples are valid inferences, and shi er buran (...) examples are counter-examples. (shrink)

Utilizing an idea that has its first appearance in Gerhard Gentzen’s unpublished manuscripts, we generate an exhaustive repertoire of all the possible inference rules that are related to the left implication inference rule of the sequent calculus from a ground sequent, that is, a logical axiom. We discuss the similarities and differences of these derived rules as well as their interaction with the implication right rule under cut and the structural axiom. We further consider the question of analyticity (...) of cuts in calculi using one of the new rules instead of the standard left implication rule. (shrink)

Active inference offers a unified theory of perception, learning, and decision-making at computational and neural levels of description. In this article, we address the worry that active inference may be in tension with the belief–desire–intention model within folk psychology because it does not include terms for desires at the mathematical level of description. To resolve this concern, we first provide a brief review of the historical progression from predictive coding to active inference, enabling us to distinguish between (...) active inference formulations of motor control and active inference formulations of decision processes. We then show that, despite a superficial tension when viewed at the mathematical level of description, the active inference formalism contains terms that are readily identifiable as encoding both the objects of desire and the strength of desire at the psychological level of description. We demonstrate this with simple simulations of an active inference agent motivated to leave a dark room for different reasons. Despite their consistency, we further show how active inference may increase the granularity of folk-psychological descriptions by highlighting distinctions between drives to seek information versus reward—and how it may also offer more precise, quantitative folk-psychological predictions. Finally, we consider how the implicitly conative components of active inference may have partial analogues in other systems describable by the broader free energy principle to which it conforms. (shrink)

Scepticism about inference to the best explanation is far less often discussed than scepticism about another ampliative form of inference, enumerative induction. Both of these inference forms are widely used, and scepticism about either can pose an important challenge. This chapter aims to redress the imbalance by giving scepticism about inference to the best explanation the attention it, too, deserves. The chapter’s conclusion is that inference to the best explanation, even to the observable, may be (...) in a worse epistemic position than enumerative induction. The reason for this is that there are sceptical arguments that target inference to the best explanation which do not have inductive analogues, but the converse is not true. (shrink)

In previous work, we studied four well known systems of qualitative probabilistic inference, and presented data from computer simulations in an attempt to illustrate the performance of the systems. These simulations evaluated the four systems in terms of their tendency to license inference to accurate and informative conclusions, given incomplete information about a randomly selected probability distribution. In our earlier work, the procedure used in generating the unknown probability distribution (representing the true stochastic state of the world) tended (...) to yield probability distributions with moderately high entropy levels. In the present article, we present data charting the performance of the four systems when reasoning in environments of various entropy levels. The results illustrate variations in the performance of the respective reasoning systems that derive from the entropy of the environment, and allow for a more inclusive assessment of the reliability and robustness of the four systems. (shrink)

A. W. F. Edwards is one of the most influential mathematical geneticists in the history of the discipline. One of the last students of R. A. Fisher, Edwards pioneered the statistical analysis of phylogeny in collaboration with L. L. Cavalli-Sforza, and helped establish Fisher's concept of likelihood as a standard of statistical and scientific inference. In this book, edited by philosopher of science Rasmus Grønfeldt Winther, Edwards's key papers are assembled alongside commentaries by leading scientists, discussing Edwards's influence on (...) their own research and on thinking in their field overall. In an extensive interview with Winther, Edwards offers his thoughts on his contributions, their legacy, and the context in which they emerged. This book is a resource both for anyone interested in the history and philosophy of genetics, statistics, and science, and for scientists seeking to develop new algorithmic and statistical methods for understanding the genetic relationships between and among species both extant and extinct. (shrink)

Despite the recent “epistemic turn” in the philosophy of measurement, philosophers have ignored a nearly 80-year controversy about the relationship between statistical inference and measurement theory. Some scholars maintain that measurement theory places no constraints on statistics, whereas others argue that the measurement scale (e.g., ordinal or interval) of one’s data determines which statistical methods are “permissible.” I defend an intermediate position: Even if existing measurement theory were irrelevant to statistical inference, it would be critical for scientific (...) class='Hi'>inference, which requires connecting statistical hypotheses to broader research hypotheses. (shrink)

Some assertions give rise to the acquaintance inference: the inference that the speaker is acquainted with some individual. Discussion of the acquaintance inference has previously focused on assertions about aesthetic matters and personal tastes (e.g. 'The cake is tasty'), but it also arises with reports about how things seem (e.g. 'Tom seems like he's cooking'). 'Seem'-reports give rise to puzzling acquaintance behavior, with no analogue in the previously-discussed domains. In particular, these reports call for a distinction between (...) the specific acquaintance inference (that the speaker is acquainted with a specific individual) and the general acquaintance inference (that the speaker is acquainted with something or other of relevance). We frame a novel empirical generalization -- the specific with stage-level generalization -- that systematizes the observed behavior, in terms of the semantics of the embedded 'like'-clause. We present supporting experimental work, and explain why the generalization makes sense given the evidential role of 'seem'-reports. Finally, we discuss the relevance of this result for extant proposals about the semantics of 'seem'-reports. More modestly, it fills a gap in previous theories by identifying which reports get which of two possible interpretations; more radically, it suggests a revision of the kind of explanation that should be given for the acquaintance behavior in question. (shrink)

Mary Hesse’s work on the role of analogical reasoning in science has set the disciplinary standard. Her classic Models and Analogies in Science (1966) spearheaded the emergence of the philosophy of scientific modelling and enjoys lasting prominence in the field. Hesse’s account famously divided analogy into three kinds: positive, negative, and neutral. In this chapter we aim to bring attention to the one element that has been relatively least explored, the negative analogy. We draw on an example from medical practice, (...) namely the use of the Neer Classification system in the medical prognosis and treatment of shoulder fractures. We argue that there are externally and internally functioning negative analogies, and that, importantly, the latter have a ‘positive’ inferential role. We describe three types of externally functioning negative analogies that have already been to some extent discussed in the literature—which we refer to as ‘crucial’, ‘irrelevant’, and ‘requisite’—as well as a new fourth type that we call ‘scope-fixing.’ We then apply Hesse’s distinction between horizontal and vertical relations to the clinical case study and suggest that there are two different ways in which internally functioning negative analogies can serve as premises in analogical inference. (shrink)

Schurz (2019, ch. 4) argues that probabilistic accounts of induction fail. In particular, he criticises probabilistic accounts of induction that appeal to direct inference principles, including subjective Bayesian approaches (e.g., Howson 2000) and objective Bayesian approaches (see, e.g., Williamson 2017). In this paper, I argue that Schurz’ preferred direct inference principle, namely Reichenbach’s Principle of the Narrowest Reference Class, faces formidable problems in a standard probabilistic setting. Furthermore, the main alternative direct inference principle, Lewis’ Principal Principle, is (...) also hard to reconcile with standard probabilism. So, I argue, standard probabilistic approaches cannot appeal to direct inference to explicate the logic of induction. However, I go on to defend a non-standard objective Bayesian account of induction: I argue that this approach can both accommodate direct inference and provide a viable account of the logic of induction. I then defend this account against Schurz’ criticisms. (shrink)

I argue that inference can tolerate forms of self-ignorance and that these cases of inference undermine canonical models of inference on which inferrers have to appreciate (or purport to appreciate) the support provided by the premises for the conclusion. I propose an alternative model of inference that belongs to a family of rational responses in which the subject cannot pinpoint exactly what she is responding to or why, where this kind of self-ignorance does nothing to undermine (...) the intelligence of the response. (shrink)

Mary Hesse’s work on the role of analogical reasoning in science has set the disciplinary standard. Her classic Models and Analogies in Science (1966) spearheaded the emergence of the philosophy of scientific modelling and enjoys lasting prominence in the field. Hesse’s account famously divided analogy into three kinds: positive, negative, and neutral. In this chapter we aim to bring attention to the one element that has been relatively least explored, the negative analogy. We draw on an example from medical practice, (...) namely the use of the Neer Classification system in the medical prognosis and treatment of shoulder fractures. We argue that there are externally and internally functioning negative analogies, and that, importantly, the latter have a ‘positive’ inferential role. We describe three types of externally functioning negative analogies that have already been to some extent discussed in the literature—which we refer to as ‘crucial’, ‘irrelevant’, and ‘requisite’—as well as a new fourth type that we call ‘scope-fixing.’ We then apply Hesse’s distinction between horizontal and vertical relations to the clinical case study and suggest that there are two different ways in which internally functioning negative analogies can serve as premises in analogical inference. (shrink)

This paper develops an inference system for natural language within the ‘Natural Logic’ paradigm as advocated by van Benthem, Sánchez and others. The system that we propose is based on the Lambek calculus and works directly on the Curry-Howard counterparts for syntactic representations of natural language, with no intermediate translation to logical formulae. The Lambek -based system we propose extends the system by Fyodorov et~al., which is based on the Ajdukiewicz/Bar-Hillel calculus Bar Hillel,. This enables the system to deal (...) with new kinds of inferences, involving relative clauses, non-constituent coordination, and meaning postulates that involve complex expressions. Basing the system on the Lambek calculus leads to problems with non-normalized proof terms, which are treated by using normalization axioms. (shrink)

A group of philosophers led by the late John Pollock has applied a method of reasoning about probability, known as direct inference and governed by a constraint known as Reichenbach's principle, to argue in support of ‘thirdism’ concerning the Sleeping Beauty Problem. A subsequent debate has ensued about whether their argument constitutes a legitimate application of direct inference. Here I defend the argument against two extant objections charging illegitimacy. One objection can be overcome via a natural and plausible (...) definition, given here, of the binary relation ‘logically stronger than’ between two properties that can obtain even when the respective properties differ from one another in ‘arity’; given this definition, the Pollock group's argument conforms to Reichenbach's principle. Another objection prompts a certain refinement of Reichenbach's principle that is independently well-motivated. My defense of the Pollock group's argument has epistemological import beyond the Sleeping Beauty problem, because it both widens and sharpens the applicability of direct inference as a method for inferring single-case epistemic probabilities on the basis of general information of a probabilistic or statistical nature. (shrink)

Inference versus consequence , an invited lecture at the LOGICA 1997 conference at Castle Liblice, was part of a series of articles for which I did research during a Stockholm sabbatical in the autumn of 1995. The article seems to have been fairly effective in getting its point across and addresses a topic highly germane to the Uppsala workshop. Owing to its appearance in the LOGICA Yearbook 1997 , Filosofia Publishers, Prague, 1998, it has been rather inaccessible. Accordingly it (...) is republished here with only bibliographical changes and an afterword. (shrink)