Transformer-based Pre-Trained Language Models currently dominate the field of Natural Language Inference (NLI). We first survey existing NLI datasets, and we systematize them according to the different kinds of logical inferences that are being distinguished. This shows two gaps in the current dataset landscape, which we propose to address with one dataset that has been developed in argumentative writing research as well as a new one building on syllogistic logic. Throughout, we also explore the promises of ChatGPT. Our results (...) show that our new datasets do pose a challenge to existing methods and models, including ChatGPT, and that tackling this challenge via fine-tuning yields only partly satisfactory results. (shrink)

In recent decades “inference to the best explanation” has become a fashionable mode of reasoning. It is, however, highly problematic and flawed in ways that the article expounds. Instead, so it is argued, a process of inference from the best systematization of the relevant determinable facts is a far more plausible and promising procedure.

Philosophical discussions on causal inference in medicine are stuck in dyadic camps, each defending one kind of evidence or method rather than another as best support for causal hypotheses. Whereas Evidence Based Medicine advocates the use of Randomised Controlled Trials and systematic reviews of RCTs as gold standard, philosophers of science emphasise the importance of mechanisms and their distinctive informational contribution to causal inference and assessment. Some have suggested the adoption of a pluralistic approach to causal inference, (...) and an inductive rather than hypothetico-deductive inferential paradigm. However, these proposals deliver no clear guidelines about how such plurality of evidence sources should jointly justify hypotheses of causal associations. We here develop such guidelines by first giving a philosophical analysis of the underpinnings of Hill’s viewpoints on causality. We then put forward an evidence-amalgamation framework adopting a Bayesian net approach to model causal inference in pharmacology for the assessment of harms. Our framework accommodates a number of intuitions already expressed in the literature concerning the EBM vs. pluralist debate on causal inference, evidence hierarchies, causal holism, relevance, and reliability. (shrink)

Artificial intelligence receives attention in media as well as in academe and business. In media coverage and reporting, AI is predominantly described in contrasted terms, either as the ultimate solution to all human problems or the ultimate threat to all human existence. In academe, the focus of computer scientists is on developing systems that function, whereas philosophy scholars theorize about the implications of this functionality for human life. In the interface between technology and philosophy there is, however, one imperative aspect (...) of AI yet to be articulated: how do intelligent systems make inferences? We use the overarching concept “Artificial Intelligent Behaviour” which would include both cognition/processing and judgment/behaviour. We argue that due to the complexity and opacity of artificial inference, one needs to initiate systematic empirical studies of artificial intelligent behavior similar to what has previously been done to study human cognition, judgment and decision making. This will provide valid knowledge, outside of what current computer science methods can offer, about the judgments and decisions made by intelligent systems. Moreover, outside academe—in the public as well as the private sector—expertise in epistemology, critical thinking and reasoning are crucial to ensure human oversight of the artificial intelligent judgments and decisions that are made, because only competent human insight into AI-inference processes will ensure accountability. Such insights require systematic studies of AI-behaviour founded on the natural sciences and philosophy, as well as the employment of methodologies from the cognitive and behavioral sciences. (shrink)

In this paper, I show that the availability of what some authors have called the weak reading and the strong reading of donkey sentences with relative clauses is systematically related to monotonicity properties of the determiner. The correlation is different from what has been observed in the literature in that it concerns not only right monotonicity, but also left monotonicity (persistence/antipersistence). I claim that the reading selected by a donkey sentence with a double monotone determiner is in fact the one (...) that validates inference based on the left monotonicity of the determiner. This accounts for the lack of strong reading in donkey sentences with MON determiners, which have been neglected in the literature. I consider the relevance of other natural forms of inference as well, but also suggest how monotonicity inference might play a central role in the actual process of interpretation. The formal theory is couched in dynamic predicate logic with generalized quantifiers. (shrink)

The question of whether or not to partition data for the purposes of inferring phylogenetic hypotheses remains controversial. Opinions have been especially divided since Kluge's (1989, Systematic Zoology 38, 7–25) claim that data partitioning violates the requirement of total evidence (RTE). Unfortunately, advocacy for or against the RTE has not been based on accurate portrayals of the requirement. The RTE is a basic maxim for non-deductive inference, stipulating that evidence must be considered if it has relevance to an (...) class='Hi'>inference. Evidence is relevant if it has a positive or negative effect on a given conclusion. In the case of ℈partitioned’ phylogenetic inferences, the RTE is violated, and the basis for rational belief in any conclusion is compromised, unless it is shown that the partitions are evidentially irrelevant to one another. The goal of phylogenetic systematics is to hypothesize past causal conditions to account for observed shared similarities among two or more species. Such inferences are non-deductive, necessitating consideration of the RTE. Some phylogeneticists claim the parsimony criterion as justification for the RTE. There is no relation between the two – parsimony is a relation between a hypothesis and causal question(s). Parsimony does not dictate the content of premises prior to an inference. ℈Taxonomic congruence,’ ℈supertrees,’ and ℈conditional combination’ methods violate the RTE. Taxonomic congruence and supertree methods also fail to achieve the intended goal of phylogenetic inference, such that ℈consensus trees’ and ℈supertrees’ lack an empirical basis. ℈Conditional combination’ is problematic because hypotheses derived from partitioned data cannot be compared – a causal hypothesis inferred to account for a set of effects only has relevance to those effects, not any comparative relevance to other causal hypotheses. A similar problem arises in the comparisons of hypotheses derived from different causal theories. (shrink)

Martin Davies' criterion for the knowledge of implicit rules, viz. the causal systematicity of cognitive processes, is first exposed. Then the inference from causal systematicity of a process to syntactic properties of the input states is examined. It is argued that Davies' notion of a syntactic property is too weak to bear the conclusion that causal systematicity implies a language of thought as far as the input states are concerned. Next, it is shown that Davies' criterion (...) leads to a counterintuitive consequence: it groups together distributed connectionist systems with look-up tables. To avoid this consequence, a modified construal of causal systematicity is proposed and Davies' argument for the causal systematicity of thought is shown to be question-begging. It is briefly sketched how the modified construal links up with multiple dispositions of the same categorical base. Finally, the question of the causal efficacy of single rules is distinguished from the question of their psychological reality: implicit rules might be psychologically real without being causally efficacious. (shrink)

According to the variety of evidence thesis items of evidence from independent lines of investigation are more confirmatory, ceteris paribus, than, for example, replications of analogous studies. This thesis is known to fail (Bovens and Hartmann; Claveau). However, the results obtained by Bovens and Hartmann only concern instruments whose evidence is either fully random or perfectly reliable; instead, for Claveau, unreliability is modelled as deterministic bias. In both cases, the unreliable instrument delivers totally irrelevant information. We present a model that (...) formalizes both reliability and unreliability differently. Our instruments either are reliable, but affected by random error, or are biased but not deterministically so.Bovens and Hartmann’s results are counter-intuitive in that in their model a long series of consistent reports from the same instrument does not raise suspicion of ‘too-good-to-be-true’ evidence. This happens precisely because they contemplate neither the role of systematic bias, nor unavoidable random error of reliable instruments. In our model, the variety of evidence thesis fails as well, but the area of failure is considerably smaller than for Bovens and Hartmann and Claveau, and holds for (the majority of) realistic cases (that is, where biased instruments are very biased). The essential mechanism that triggers variety of evidence thesis failure is the rate of false to true positives for the two kinds of instruments. Our emphasis is on modelling beliefs about sources of knowledge and their role in hypothesis confirmation in interaction with dimensions of evidence, such as variety and consistency. (shrink)

I respond to the bad lot argument in the context of biological systematics. The response relies on the historical nature of biological systematics and on the availability of pattern explanations. The basic assumption of common descent enables systematic methodology to naturally generate candidate explanatory hypotheses. However, systematists face a related challenge in the issue of character analysis. Character analysis is the central problem for contemporary systematics, yet the general problem of which it is a case—what counts as evidence?—has not been (...) adequately discussed by proponents of inference to the best explanation. Facing this problem is the price of adopting abductive methods. I sketch an account of how systematists approach the problem of evidence. (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)

The paper investigates measures of explanatory power and how to define the inference schema “Inference to the Best Explanation”. It argues that these measures can also be used to quantify the systematic power of a hypothesis and the inference schema “Inference to the Best Systematization” is defined. It demonstrates that systematic power is a fruitful criterion for theory choice and IBS is truth-conducive. It also shows that even radical Bayesians must admit that systemic power is an (...) integral component of Bayesian reasoning. Finally, the paper puts the achieved results in perspective with van Fraassen’s famous criticism of IBE. (shrink)

Framing systematics as a field consistent with scientific inquiry entails that inferences of phylogenetic hypotheses have the goal of producing accounts of past causal events that explain differentially shared characters among organisms. Linking observations of characters to inferences occurs by way of why-questions implied by data matrices. Because of their form, why-questions require the use of common-cause theories. Such theories in phylogenetic inferences include natural selection and genetic drift. Selection or drift can explain ‘morphological’ characters but selection cannot be causally (...) applied to sequences since fitness differences cannot be directly associated with individual nucleotides or amino acids. The relation of selection to sequence data is by way of downward or top-down causation from those phenotypes upon which selection occurs. The application of phylogenetic inference to explain sequence data is thus restricted to instances where drift is the relevant theory; those nucleotides or amino acids that can be explained via downward causation are precluded from inclusion in the data matrix. The restrictions on the inclusion of sequence data in phylogenetic inferences equally apply to species hypotheses, precluding the more restrictive approach known as DNA barcoding. Not being able to discern drift and selection as relevant causal mechanisms can severely constrain the inclusion and explanations of sequence data. Implications of such exclusion are discussed in relation to the requirement of total evidence. (shrink)

Human agents draw a variety of inferences effortlessly, spontaneously, and with remarkable efficiency – as though these inferences were a reflexive response of their cognitive apparatus. Furthermore, these inferences are drawn with reference to a large body of background knowledge. This remarkable human ability seems paradoxical given the complexity of reasoning reported by researchers in artificial intelligence. It also poses a challenge for cognitive science and computational neuroscience: How can a system of simple and slow neuronlike elements represent a large (...) body of systemic knowledge and perform a range of inferences with such speed? We describe a computational model that takes a step toward addressing the cognitive science challenge and resolving the artificial intelligence paradox. We show how a connectionist network can encode millions of facts and rules involving n-ary predicates and variables and perform a class of inferences in a few hundred milliseconds. Efficient reasoning requires the rapid representation and propagation of dynamic bindings. Our model (which we refer to as SHRUTI) achieves this by representing (1) dynamic bindings as the synchronous firing of appropriate nodes, (2) rules as interconnection patterns that direct the propagation of rhythmic activity, and (3) long-term facts as temporal pattern-matching subnetworks. The model is consistent with recent neurophysiological evidence that synchronous activity occurs in the brain and may play a representational role in neural information processing. The model also makes specific psychologically significant predictions about the nature of reflexive reasoning. It identifies constraints on the form of rules that may participate in such reasoning and relates the capacity of the working memory underlying reflexive reasoning to biological parameters such as the lowest frequency at which nodes can sustain synchronous oscillations and the coarseness of synchronization. (shrink)

According to the Variety of Evidence Thesis items of evidence from independent lines of investigation are more confirmatory, ceteris paribus, than e.g. replications of analogous studies. This thesis is known to fail Bovens and Hartmann, Claveau. How- ever, the results obtained by the former only concern instruments whose evidence is either fully random or perfectly reliable; instead in Claveau, unreliability is modelled as deterministic bias. In both cases, the unreliable instrument delivers totally irrelevant information. We present a model which formalises (...) both reliability, and unreliability, differently. Our instruments are either reliable, but affected by random error, or they are biased but not deterministically so. Bovens and Hartmann’s results are counter-intuitive in that in their model a long series of consistent reports from the same instrument does not raise suspicion of “too-good-to- be-true” evidence. This happens precisely because they neither contemplate the role of systematic bias, nor unavoidable random error of reliable instruments. In our model the Variety of Evidence Thesis fails as well, but the area of failure is considerably smaller than for Bovens and Hartmann, Claveau and holds for realistic cases. The essential mechanism which triggers VET failure is the rate of false to true positives for the two kinds of instruments. Our emphasis is on modelling beliefs about sources of knowledge and their role in hypothesis confirmation in interaction with dimensions of evidence, such as variety and consistency. (shrink)

Does the formation of delusions involve abnormal reasoning? According to the prominent ‘two-factor’ theory of delusions (e.g. Coltheart, 2007), the answer is yes. The second factor in this theory is supposed to affect a deluded individual's ability to evaluate candidates for belief. However, most published accounts of the two-factor theory have not said much about the nature of this second factor. In an effort to remedy this shortcoming, Coltheart, Menzies and Sutton (2010) recently put forward a Bayesian account of (...) class='Hi'>inference in delusions. I outline some criticisms of this important account, and sketch an alternative account of delusional inference that, I argue, avoids these criticisms. Specifically, I argue that the second factor in delusion formation involves a systematic deviation from Bayesian updating, a deviation that may be characterized as a bias towards ‘explanatory adequacy’. I present a numerical model of this idea and show that my alternative account is broadly consistent with prominent prediction error models of delusion formation (e.g. Corlett, Murray et al., 2007). (shrink)

Advocates of cladistic parsimony methods have invoked the philosophy of Karl Popper in an attempt to argue for the superiority of those methods over phylogenetic methods based on Ronald Fisher's statistical principle of likelihood. We argue that the concept of likelihood in general, and its application to problems of phylogenetic inference in particular, are highly compatible with Popper's philosophy. Examination of Popper's writings reveals that his concept of corroboration is, in fact, based on likelihood. Moreover, because probabilistic assumptions are (...) necessary for calculating the probabilities that define Popper's corroboration, likelihood methods of phylogenetic inference—with their explicit probabilistic basis—are easily reconciled with his concept. In contrast, cladistic parsimony methods, at least as described by certain advocates of those methods, are less easily reconciled with Popper's concept of corroboration. If those methods are interpreted as lacking probabilistic assumptions, then they are incompatible with corroboration. Conversely, if parsimony methods are to be considered compatible with corroboration, then they must be interpreted as carrying implicit probabilistic assumptions. Thus, the non-probabilistic interpretation of cladistic parsimony favored by some advocates of those methods is contradicted by an attempt by the same authors to justify parsimony methods in terms of Popper's concept of corroboration. In addition to being compatible with Popperian corroboration, the likelihood approach to phylogenetic inference permits researchers to test the assumptions of their analytical methods (models) in a way that is consistent with Popper's ideas about the provisional nature of background knowledge. (shrink)

Parametric cognitive models are increasingly popular tools for analyzing data obtained from psychological experiments. One of the main goals of such models is to formalize psychological theories using parameters that represent distinct psychological processes. We argue that systematic quantitative reviews of parameter estimates can make an important contribution to robust and cumulative cognitive modeling. Parameter reviews can benefit model development and model assessment by providing valuable information about the expected parameter space, and can facilitate the more efficient design of experiments. (...) Importantly, parameter reviews provide crucial—if not indispensable—information for the specification of informative prior distributions in Bayesian cognitive modeling. From the Bayesian perspective, prior distributions are an integral part of a model, reflecting cumulative theoretical knowledge about plausible values of the model's parameters (Lee,2018). In this paper we illustrate how systematic parameter reviews can be implemented to generate informed prior distributions for the Diffusion Decision Model (DDM; Ratcliff and McKoon,2008), the most widely used model of speeded decision making. We surveyed the published literature on empirical applications of the DDM, extracted the reported parameter estimates, and synthesized this information in the form of prior distributions. Our parameter review establishes a comprehensive reference resource for plausible DDM parameter values in various experimental paradigms that can guide future applications of the model. Based on the challenges we faced during the parameter review, we formulate a set of general and DDM-specific suggestions aiming to increase reproducibility and the information gained from the review process. (shrink)

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)

Research on facial emotion expression has mostly focused on emotion recognition, assuming that a small number of discrete emotions is elicited and expressed via prototypical facial muscle configurations as captured in still photographs. These are expected to be recognized by observers, presumably via template matching. In contrast, appraisal theories of emotion propose a more dynamic approach, suggesting that specific elements of facial expressions are directly produced by the result of certain appraisals and predicting the facial patterns to be expected for (...) certain appraisal configurations. This approach has recently been extended to emotion perception, claiming that observers first infer individual appraisals and only then make categorical emotion judgments based on the estimated appraisal patterns, using semantic inference rules. Here we report two related studies to empirically investigate the facial action unit configurations that are used by actors to convey specific emotions in short affect bursts and to examine to what extent observers can infer a person’s emotions from the predicted facial expression configurations. The results show that 1) professional actors use many of the predicted facial action unit patterns to enact systematically specified appraisal outcomes in a realistic scenario setting, and 2) naïve observers infer the respective emotions based on highly similar facial movement configurations with a degree of accuracy comparable to earlier research findings. Based on estimates of underlying appraisal criteria for the different emotions we conclude that the patterns of facial action units identified in this research correspond largely to prior predictions and encourage further research on appraisal-driven expression and inference. (shrink)

This chapter tells how, within a century, the notions of judgment and inference were driven out of logical theory and replaced by propositions and consequence. Systematic considerations guide the treatment. The history is unashamedly Whiggish: the current position is shown as the outcome, or even culmination, of a historical development.

This paper develops a trivalent semantics for the truth conditions and the probability of the natural language indicative conditional. Our framework rests on trivalent truth conditions first proposed by Cooper (1968) and Belnap (1973) and it yields two logics of conditional reasoning: (i) a logic C of certainty-preserving inference; and (ii) a logic U for uncertain reasoning that preserves the probability of the premises. We show systematic correspondences between trivalent and probabilistic representations of inferences in either framework, and we (...) use the distinction between the two systems to cast light on the validity of inferences such as Modus Ponens, Or-To-If, and Conditional Excluded Middle. Specifically, the conditional behaves monotonically in C, but non-monotonically in U; Modus Ponens is valid in C, but valid in U only for non-nested conditionals. The result is a unified account of the semantics and epistemology of indicative conditionals that can be fruitfully applied to analyzing the validity of conditional inferences. (shrink)

David Bordwell's new book is at once a history of film criticism, an analysis of how critics interpret film, and a proposal for an alternative program for film studies. It is an anatomy of film criticism meant to reset the agenda for film scholarship. As such Making Meaning should be a landmark book, a focus for debate from which future film study will evolve. Bordwell systematically maps different strategies for interpreting films and making meaning, illustrating his points with a vast (...) array of examples from Western film criticism. Following an introductory chapter that sets out the terms and scope of the argument, Bordwell goes on to show how critical institutions constrain and contain the very practices they promote, and how the interpretation of texts has become a central preoccupation of the humanities. He gives lucid accounts of the development of film criticism in France, Britain, and the United States since World War II; analyzes this development through two important types of criticism, thematic-explicatory and symptomatic; and shows that both types, usually seen as antithetical, in fact have much in common. These diverse and even warring schools of criticism share conventional, rhetorical, and problem-solving techniques--a point that has broad-ranging implications for the way critics practice their art. The book concludes with a survey of the alternatives to criticism based on interpretation and, finally, with the proposal that a historical poetics of cinema offers the most fruitful framework for film analysis. (shrink)

According to mental model theory, illusory inferences are a class of deductions in which individuals systematically go wrong. Mental model theory explains them invoking the principle of truth, which is a tendency not to represent models that falsify the premises. In this paper we focus on the illusory problems based on conditional sentences. In three experiments, we show that: (a) rather than not representing models that falsify the conditionals, participants have a different understanding of what falsifies a conditional (Experiment I); (...) (b) specifically, participants think that a conditional with an impossible antecedent or consequent is false (Experiment 2); (c) if the domain of the conditionals in the illusory problems are expanded to show that their antecedents and consequents are possible, the participants find it easy to reach the correct conclusions (Experiment 3). According to our results, the illusory inferences based on conditional premises, differently from those based on disjunctive premises, are caused by a difference between the understanding of natural language factual conditionals and the truth table of the factual implication; the principle of truth is not necessary to explain them. (shrink)

In pragmatic theories, the notion of inference plays a central role, together with the communicative act in which it is activated. Although some scholars, such as Levinson, Sperber and Wilson, propose detailed and accurate analyses of this notion, we will maintain that these analyses can be better systematized if seen through Peirce’s notion of abduction. We will try to maintain that the variety of inferential processes in play in a linguistic act is mostly of an abductive nature. Moreover, we (...) will maintain that the typological tripartition of abductions discussed by Eco allows to account for a significant part of the mechanisms involved in the comprehension of an utterance, ranging from quasi-immediate and spontaneous levels of understanding to processes that draw on creative resources. In our proposal the vast majority of our linguistic activities implies the automatic retrieval of a habit of action. In the other cases we need more onerous processes. We might need to identify, among a range of possibilities, the appropriate rule to be applied to the contextual situation or, depending on the context and on our background knowledge, we might be forced to create ex novo a new linguistic routine. In our view, this typology of abductive inferences provides us with all the necessary tools to account for the different inferential demands entailed by different levels of the process of language comprehension. On the basis of this typology we can, develop a fine-grained model of linguistic inferences and, thus, simplify the terms of some problematic nodes debated within contextualist approaches.The benefits of this fine-grained model is that it provides a unitary framework in which all the levels of the process of language comprehension are shown to be inferential and have the same logic structure. At the same time, it is also able to account for all the different inferential efforts entailed by different linguistic processes. Thus, this model gives us both a unitary account of the structure of linguistic inferences and a fine-grained description of the differences existing between them. (shrink)

Cause and effect relationships play a central role in how we perceive and make sense of the world around us, how we act upon it, and ultimately, how we understand ourselves. Almost two decades ago, computer scientist Judea Pearl made a breakthrough in understanding causality by discovering and systematically studying the “Ladder of Causation” [Pearl and Mackenzie 2018], a framework that highlights the distinct roles of seeing, doing, and imagining. In honor of this landmark discovery, we name this the Pearl (...) Causal Hierarchy (PCH). In this chapter, we develop a novel and comprehensive treatment of the PCH through two complementary lenses, one logical-probabilistic and another inferential-graphical. Following Pearl’s own presentation of the hierarchy, we begin by showing how the PCH organically emerges from a well-specified collection of causal mechanisms (a structural causal model, or SCM). We then turn to the logical lens. Our first result, the Causal Hierarchy Theorem (CHT), demonstrates that the three layers of the hierarchy almost always separate in a measure theoretic sense. Roughly speaking, the CHT says that data at one layer virtually always underdetermines information at higher layers. Since in most practical settings the scientist does not have access to the precise form of the underlying causal mechanisms – only to data generated by them with respect to some of PCH’s layers – this motivates us to study inferences within the PCH through the graphical lens. Specifically, we explore a set of methods known as causal inference that enable inferences bridging PCH’s layers given a partial specification of the SCM. For instance, one may want to infer what would happen had an intervention been performed in the environment (second-layer statement) when only passive observations (first-layer data) are available. We introduce a family of graphical models that allows the scientist to represent such a partial specification of the SCM in a cognitively meaningful and parsimonious way. Finally, we investigate an inferential system known as docalculus, showing how it can be sufficient, and in many cases necessary, to allow inferences across PCH’s layers. We believe that connecting with the essential dimensions of human experience as delineated by the PCH is a critical step towards creating the next generation of AI systems that will be safe, robust, human-compatible, and aligned with the social good. (shrink)

There are at least twelve major virtues of good theories: evidential accuracy, causal adequacy, explanatory depth, internal consistency, internal coherence, universal coherence, beauty, simplicity, unification, durability, fruitfulness, and applicability. These virtues are best classified into four classes: evidential, coherential, aesthetic, and diachronic. Each virtue class contains at least three virtues that sequentially follow a repeating pattern of progressive disclosure and expansion. Systematizing the theoretical virtues in this manner clarifies each virtue and suggests how they might have a coordinated and cumulative (...) role in theory formation and evaluation across the disciplines—with allowance for discipline specific modification. An informal and flexible logic of theory choice is in the making here. Evidential accuracy, according to my systematization, is not a largely isolated trait of good theories, as some have made it out to be. Rather, it bears multifaceted relationships, constituting significant epistemic entanglements, with other theoretical virtues. (shrink)

How health care providers are paid affects how medicine is practiced. It is thus important to assess provider payment models not only from the economic perspective but also from the ethical perspective. China recently started to reform the provider payment model in the health care system from fee-for-service to case-based payment. This paper aims to examine this transition from an ethical perspective. We collected empirical studies on the impact of case-based payment in the Chinese health care system and applied a (...) systematic ethical matrix that integrates clinical ethics and public health ethics to analyze the empirical findings. We identified eleven prominent ethical issues related to case-based payment. Some ethical problems of case-based payment in China are comparable to ethical problems of managed care and diagnosis related groups in high-income countries. However, in this paper we discuss in greater detail four specific ethical issues in the Chinese context: professionalism, the patient-physician relationship, access to care and patient autonomy. Based on the analysis, we cautiously infer that case-based payment is currently more ethically acceptable than fee-for-service in the context of China, mainly because it seems to lower financial barriers to access care. Nonetheless, it will be difficult to justify the implementation of case-based payment if no additional measures are taken to monitor and minimize its existing negative ethical implications. (shrink)

In 1958, to refute the argument known as the theoretician's dilemma, Hempel suggested that theoretical terms might be logically indispensable for inductive systematization of observational statements. This thesis, in some form or another, has later been supported by Scheffler, Lehrer, and Tuomela, and opposed by Bohnert, Hooker, Stegmüller, and Cornman. In this paper, a critical survey of this discussion is given. Several different putative definitions of the crucial notion inductive systematization achieved by a theory are discussed by reference to the (...) properties of inductive inference. The consequences of the following differences between deductive and inductive inference are emphasized: the lack of simple transitivity properties (even in a modified sense) of inductive inference, and the failure of the inductive analogue of the converse of The Deduction Theorem. The main conclusions are: (i) Hempel's original thesis may very well be right but his argument for it is unsatisfactory, (ii) theoretical terms can be logically indispensable for a non-Hempelian kind of inductive systematization, relative to both Craigian and Ramseyan elimination, (iii) Lehrer's attempt to prove the indispensability of theoretical terms for inductive-probabilistic systematization is, as a modification of Hempelian kind of inductive-deterministic systematization, unsatisfactory, and (iv) there does not seem to be much hope of escaping the conclusion (ii), if it is true, by extending the Craigian replacement programme along the lines suggested by Cornman. (shrink)

This chapter contains sections titled: Introduction From Art to Science: An Introduction to Schools of Thought How to Infer Phylogeny, Or, Why Some Cladists Aren't “Cladists” Summary and Synthesis Acknowledgment References.

We describe the foundations and the systematization of natural logic-like monotonic inference using unscoped episodic logical forms (ULFs) that as reported by Kim et al. (Proceedings of the 1st and 2nd Workshops on Natural Logic Meets Machine Learning (NALOMA), Groningen, 2021a, b) introduced and first evaluated. In addition to providing a more detailed explanation of the theory and system, we present results from extending the inference manager to address a few of the limitations that as reported by Kim (...) et al. (Proceedings of the 1st and 2nd Workshops on Natural Logic Meets Machine Learning (NALOMA), Groningen, 2021b) naive system has. Namely, we add mechanisms to incorporate lexical information from the hypothesis (or goal) sentence, enable the inference manager to consider multiple possible scopings for a single sentence, and match against the goal using English rather than the ULF. (shrink)

The paper deals with the problem of axiomatizing a system T1 of discrete tense logic, where one thinks of time as the set Z of all the integers together with the operations +1 ("immediate successor") and-1 ("immediate predecessor"). T1 is like the Segerberg-Sundholm system WI in working with so-called infinitary inference ruldes; on the other hand, it differs from W I with respect to (i) proof-theoretical setting, (ii) presence of past tense operators and a "now" operator, and, most importantly, (...) with respect to (iii) the presence in T1 of so-called systematic frame constants, which are meant to hold at exactly one point in a temporal structure and to enable us to express the irreflexivity of such structures. Those frame constants will be seen to play a paramount role in our axiomatization of T1. (shrink)

Methods for performing complex probabilistic reasoning tasks, often based on masses of different forms of evidence obtained from a variety of different sources, are being sought by, and developed for, persons in many important contexts including law, medical diagnosis, and intelligence analysis. The complexity of these tasks can often be captured and represented by graphical structures now called inference networks. These networks are directed acyclic graphs, consisting of nodes, representing relevant hypotheses, items of evidence, and unobserved variables, and arcs (...) joining some of the nodes, representing dependency relations among them. This chapter describes and comments on two different approaches to inference network construction. In the first approach, a DAG network structure is explicitly constructed as a vehicle for probabilistic analyses. Since the associated computations can be regarded as generalising the use of Bayes' rule, such networks are commonly called Bayesian networks. The second approach stems from the work of the American jurist John H. Wigmore who was the very first person to attempt a systematic study of inference networks. (shrink)

Relevance of premises to conclusion can be explicated through Toulmin’s notion of warrant, understood as an inference rule, albeit not necessarily formal. A normative notion of relevance requires the warrant to be reliable. To determine reliability, we propose a fourfold classification of warrants into a priori, empirical, institutional, and evaluative, with further subdivisions possible. This classification has its ancestry in classical rhetoric and recent epistemology. Distinctive to each type of warrant is the mode by which such connections are intuitively (...) discovered and the grounds on which we ultimately justify them. The classification of warrants is thus epistemic. We illustrate the difference by contrasting empirical physical with institutional intuition, and argue for the advantages of this approach over Toulmin’s conception of field dependence. (shrink)

There are two uses of randomization in efforts to control systematic bias in experimental design: (a) Alchemical uses seek to convert unavoidable systematic errors into random errors. (b) Hygienic uses seek to reduce the prospect of the experimenter's involvement with the implementation of the experiment contributing to bias. A few remarks are made at the end of the paper about the hygienic use of randomization as a preventative against sticky fingers. The bulk of the discussion addresses the alchemical applications. The (...) thesis is that attitudes towards the cogency of Fisher's alchemical use of randomization ought to depend on views concerning statistical deduction or direct inference. (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)

Thomas Hofweber has written a very rich book. In line with the conviction that ontology should be informed by linguistic considerations, he develops a systematic approach to central ontological questions as they arise in different regions of discourse. More generally, the book seeks to cast light upon the nature of ontology and its proper place in enquiry. His preferred methodology is not without consequence: it promises, for example, to solve what otherwise look like intractable philosophical puzzles raised by arithmetical practice (...) and numerical discourse, and, likewise, his treatment of propositional discourse has ramifications for larger questions to do with the prospects of metaphysical enquiry generally. (shrink)

Newton's methodology emphasized propositions "inferred from phenomena." These rest on systematic dependencies that make phenomena measure theoretical parameters. We consider the inferences supporting Newton's inductive argument that gravitation is proportional to inertial mass. We argue that the support provided by these systematic dependencies is much stronger than that provided by bootstrap confirmation; this kind of support thus avoids some of the major objections against bootstrapping. Finally we examine how contemporary testing of equivalence principles exemplifies this Newtonian methodological theme.

This is one of four volumes from the same press collecting Hare’s major papers. Of the six papers in this volume, two have appeared in Mind, one in the Philosophical Review, two have appeared in special collections, and only one has not been previously published. There is brief additional material appended to some of the articles and, perhaps most important, a four page bibliography of Hare’s writings. From this bibliography one can discover which of Hare’s articles appear in each of (...) the volumes of this group and also find their original location. One might thus save the cost of a volume or two for there is little doubt, considering the size and price of these books, upon what principle the publisher of this series has decided. The articles are largely a development and defense of Hare’s views of practical reason as set forth in The Language of Morals, with particular attention to the logical forms of imperatives and ought statements, and to the forms of inference valid for each. Beyond this basic theme, the collection is not particularly unified. Two fundamental impressions, however, emerge. The first is an awareness of the enormous influence of J. L. Austin on Hare’s thinking. This is evident not only in his discussion and rejection of Austin’s distinction between locutionary and illocutionary acts, but more importantly in his constant respect for ordinary usage and his resistance to ideal formalism. The second impression is that there is much to be done to develop a logic for imperative and prescriptive inferences. These papers present some basic principles for such a development but, since they are primarily defenses against criticisms of Hare’s book, there is an ad hoc character about them. What is now required is a more complete and systematic treatment of these topics. Ordinary language analysts might here make some inroads on the domain of formal logic to recall some of that logic to philosophy. The articles collected, therefore, are valuable, particularly as they encourage further development of the overlap of logic and ethics.—K. M. (shrink)

Some theorists, ranging from W. James to contemporary psychologists, have argued that forgetting is the key to proper functioning of memory. The authors elaborate on the notion of beneficial forgetting by proposing that loss of information aids inference heuristics that exploit mnemonic information. To this end, the authors bring together 2 research programs that take an ecological approach to studying cognition. Specifically, they implement fast and frugal heuristics within the ACT-R cognitive architecture. Simulations of the recognition heuristic, which relies (...) on systematic failures of recognition to infer which of 2 objects scores higher on a criterion value, demonstrate that forgetting can boost accuracy by increasing the chances that only 1 object is recognized. Simulations of the fluency heuristic, which arrives at the same inference on the basis of the speed with which objects are recognized, indicate that forgetting aids the discrimination between the objects' recognition speeds. (shrink)

According to active inference, action is enabled by a top-down modulation of sensory signals. Computational models of this mechanism complement ideomotor theories of action representation. Such theories postulate common neural representations for action and perception, without specifying how action is enabled by such representations. In active inference, motor commands are replaced by proprioceptive predictions. In order to initiate action through such predictions, sensory prediction errors have to be attenuated. This paper argues that such top-down modulation involves systematic misrepresentations. (...) More specifically, the paper first argues for the following conditional claim. If active inference provides an accurate computational description of how action is enabled in the brain, then action is enabled by systematic misrepresentations. Furthermore, it is argued that an inference to the best explanation provides reason for believing the antecedent is true: Firstly, active inference provides a crucial extension to ideomotor theories. Secondly, active inference explains otherwise puzzling phenomena related to sensory attenuation, e.g. in force-matching or self-tickling paradigms. Taken together, these reasons support the claim that action is indeed enabled by systematic misrepresentations. The claim casts doubt on the assumption that representations are systematically beneficial to the extent that they are true: if the argument in this paper is sound, systematically beneficial misrepresentations may lie at the heart of our neural architecture. (shrink)

Modern scientific cosmology pushes the boundaries of knowledge and the knowable. This is prompting questions on the nature of scientific knowledge. A central issue is what defines a 'good' model. When addressing global properties of the Universe or its initial state this becomes a particularly pressing issue. How to assess the probability of the Universe as a whole is empirically ambiguous, since we can examine only part of a single realisation of the system under investigation: at some point, data will (...) run out. We review the basics of applying Bayesian statistical explanation to the Universe as a whole. We argue that a conventional Bayesian approach to model inference generally fails in such circumstances, and cannot resolve, e.g., the so-called 'measure problem' in inflationary cosmology. Implicit and non-empirical valuations inevitably enter model assessment in these cases. This undermines the possibility to perform Bayesian model comparison. One must therefore either stay silent, or pursue a more general form of systematic and rational model assessment. We outline a generalised axiological Bayesian model inference framework, based on mathematical lattices. This extends inference based on empirical data (evidence) to additionally consider the properties of model structure (elegance) and model possibility space (beneficence). We propose this as a natural and theoretically well-motivated framework for introducing an explicit, rational approach to theoretical model prejudice and inference beyond data. (shrink)

[Edited from Conclusion section:] We have looked at various challenging issues to do with getting connectionism to cope with high-level cognitive activities such a reasoning and natural language understanding. The issues are to do with various facets of generalization that are not commonly noted. We have been concerned in particular with the special forms these issues take in the arena of propositional attitude processing. The main problems we have looked at are: (1) The need to construct explicit representations of generalizations, (...) not just generalize correctly to individual cases; (2) The need to be able to match two or more complex short-term information structures, to enable rapid generalization from recent examples rather than from long-term memories; (3) The need to represent and reason with anomalous combinations of concepts; (4) The need to perform embedded reasoning. This presents special problems for systems using non-concatenative representations (as in mainstream connectionist approaches). We also touched on vague quantification in attitude report complements. Neither this topic nor that of analogies between short-term structures (point 2) has been adequately addressed in the symbolic framework, let alone in connectionism. -/- The opportunities and problems covered are put forward as things worth being optimistic about or pessimistic about, respectively. They are not put forward as decisive arguments for or against connectionism. The hope is that this chapter contributes to a greater understanding of the connectionist/symbolist gap by presenting some unusual issues and by throwing new light on some well known ones. (shrink)

These two small works are a good supplement to Rescher’s recent trilogy. Whereas the systems-theoretic approach is employed in Methodological Pragmatism in dealing with the problem of the legitimation of claims to factual knowledge or cognitive rationality, Dialectics deals with the argumentation aspect of thesis-introduction rather than the logical aspect of thesis-derivation. Although some key notions such as the idea of burden of proof and presumption have been stated in the former work, what is offered here is a systematic discussion (...) of a disputational model of inquiry. The principal aims are "to exhibit the sociocommunal roots of the foundations of rationality, to provide an instrument for the critique of scepticism implicit in the cognitive solipsism of the Cartesian approach, and to illuminate the communal and controversy-oriented aspects of rational argumentation and inquiry—scientific inquiry in particular." Again, Rescher limits his discussion to "the probative mechanism in the factual domain." This book, if it were not for its price, should be a good supplement to a course in "informal" logic which focuses on argumentation rather than on mere evaluation of arguments. Plausible Reasoning provides the mechanism for evaluation of plausibility claims as distinct from probabilistic ones. The basic differences between these two types of claims are clearly discussed. "Plausibility is essentially a classificatory concept which ranks theses in terms of the standing and solidity of their cognitive basis. Plausibility grades theses by an external or extrinsic standard of the hierarchical nature of their supporting bases. It classifies propositions by the status of the evidential sources or validating principles that vouch for them. Probability weighs alternatives and evaluates theses by this relative contentual weight of the supporting considerations."—A.S.C. (shrink)

ArgumentThis paper argues that ancient Babylonian signs (omens) reflect a mode of inferential reasoning as a function of their syntactic and logical structure as conditionals. Taking into account the institutional context that produced a systematic written body of omens, the paper is principally interested in the cognitive disposition of such texts. Investigating what constitutes system in these works, formal aspects of the material are examined in terms of the nature of conditionals and the logic of conditional statements. It is argued (...) that conditional statements about ominous phenomena speak as much to a notion of conceivability and possibility as to empirical physical actuality. It is claimed that the epistemological character of the Babylonian compendia of signs cannot be understood without due attention to epistemic possibility and further, that the cuneiform omen texts comprise a different kind of scientific knowledge from that limited to a knowledge of physical phenomena and their laws. (shrink)

This article contributes to the revision of the procedure of robustness analysis of mathematical models in epistemic democracy using the systematic review method. It identifies the drawbacks of robustness analysis in epistemic democracy in terms of sample universality and inference from samples with the same results. To exemplify the effectiveness of systematic review, this article conducted a pilot review of diversity trumps ability theorem models, which are mathematical models of deliberation often cited by epistemic democrats. A review of nine (...) models extracted from 352 papers exemplifies the effectiveness of robustness analysis supplemented by systematic review in epistemic democracy. (shrink)

Scientific inference aims to understand the external world and the influence of our actions on it, and arises as an interaction of hypothesis, observation and inferential procedures. Of these, only the observations originate in the external world, although even they are the perceptions of the observer. Nonuniqueness poses a great problem to objective scientific inference; this book discusses the causes of nonuniqueness and suggests that they are not entirely removable. It argues that truth is not manifest and that (...) progress in science is not sequential but is achieved by tentative theorisation and systematic error elimination. (shrink)

Drawing from a Sellarsian realist-naturalist epistemology, we trace different levels of cognitive hierarchy procedures through which a representational system learns to update its own states and improve its ‘map-making’ capabilities from pre-conscious operations which modulate base-localization functions, to patterns of epistemic revision and integration at the conceptual and theoretical levels, producing a nomological double of its world. We show how ontological theorization becomes diachronically coordinated with and constrained by empirical science, and how the formal-quantitative kernel of scientific theories corresponds to (...) qualitative-conceptual determinations at the structural level. Following Johanna Seibt’s characterization of ontology as a theory of categorial inference, we trace the preservation of inferential semantic structure across ontological theories in relation to model languages and provide provisional indications to coordinate Seibt’s account with a convergent realist assessment of systematic modeling, defining the epistemological conditions for articulating the preservation of formal structure in theories toward a limit-point of enquiry. (shrink)