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Lies come in different sizes. There are little white lies, slight stretches, exaggerations, fibs, and whoppers. Such terms can reflect different aspects of lies, but one of these is how far a lie is from what the liar really thinks. This paper proposes that this dimension of lie-size reflects a scalar aspect of sincerity. Drawing inspiration from the study of truthlikeness, the paper elucidates this aspect of sincerity, which I call “truthful-likeness”. Truthful-likeness reflects how sincere a reply to a question is, based on how close the speaker takes the reply to be to their best available one. A related measure, expected subjective truth factor, reflects how sincere a constative act that is not a reply to a question is, based on how close to being true the speaker thinks it is. [ABSTRACT FROM AUTHOR]

Here, we have utilised the concept of fuzzy logic and Karl Popper's notion of verisimilitude to advocate navigating the complexity of psychiatric nosology, emphasising that psychiatric disorders defy Boolean logic. We underscore the importance of embracing imprecision and collecting extensive data for a more nuanced understanding of psychiatric disorders, asserting that falsifiability is crucial for scientific progress. We encourage the advancement of personalised psychiatric taxonomy, urging the continual accumulation of data to inform emerging advancements like artificial intelligence in reshaping current psychiatric nosology.

The basic problem of a theory of truth approximation is defining when a theory is "close to the truth" about some relevant domain. Existing accounts of truthlikeness or verisimilitude address this problem, but are usually limited to the problem of approaching a "deterministic" truth by means of deterministic theories. A general theory of truth approximation, however, should arguably cover also cases where either the relevant theories, or "the truth", or both, are "probabilistic" in nature. As a step forward in this direction, we first present a general characterization of both deterministic and probabilistic truth approximation; then, we introduce a new account of verisimilitude which provides a simple formal framework to deal with such issue in a unified way. The connections of our account with some other proposals in the literature are also briefly discussed. [ABSTRACT FROM AUTHOR]

What is scientific progress? This paper advances an interpretation of this question, and an account that serves to answer it (thus interpreted). Roughly, the question is here understood to concern what type of cognitive change with respect to a topic X constitutes a scientific improvement (to a greater or lesser extent) with respect to X. The answer explored in the paper is that the requisite type of cognitive change occurs when scientific results are made publicly available so as to make it possible for anyone to increase their understanding of X. This account is briefly compared to two rival accounts of scientific progress, based respectively on increasing truthlikeness and accumulating knowledge, and is argued to be preferable to both. [ABSTRACT FROM AUTHOR]

In the general problem of verisimilitude, we try to define the distance of a statement from a target, which is an informative truth about some domain of investigation. For example, the target can be a state description, a structure description, or a constituent of a first-order language (Sect. 1). In the problem of legisimilitude, the target is a deterministic or universal law, which can be expressed by a nomic constituent or a quantitative function involving the operators of physical necessity and possibility (Sect. 2). The special case of legisimilitude, where the target is a probabilistic law (Sect. 3), has been discussed by Roger Rosenkrantz (Synthese, 1980) and Ilkka Niiniluoto (Truthlikeness, 1987, Ch. 11.5). Their basic proposal is to measure the distance between two probabilistic laws by the Kullback–Leibler notion of divergence, which is a semimetric on the space of probability measures. This idea can be applied to probabilistic laws of coexistence and laws of succession, and the examples may involve discrete or continuous state spaces (Sect. 3). In this paper, these earlier studies are elaborated in four directions (Sect. 4). First, even though deterministic laws are limiting cases of probabilistic laws, the target-sensitivity of truthlikeness measures implies that the legisimilitude of probabilistic laws is not easily reducible to the deterministic case. Secondly, the Jensen-Shannon divergence is applied to mixed probabilistic laws which entail some universal laws. Thirdly, a new class of distance measures between probability distributions is proposed, so that their horizontal differences are taken into account in addition to vertical ones (Sect. 5). Fourthly, a solution is given for the epistemic problem of estimating degrees of probabilistic legisimilitude on the basis of empirical evidence (Sect. 6). [ABSTRACT FROM AUTHOR]

It is truism that accuracy is valued. Some deem accuracy to be among the most fundamental values, perhaps the preeminent value, of inquiry. Because of this, accuracy has been the focus of two different, important programs in epistemology. The truthlikeness program (instigated by Popper) pursued the notion of propositional accuracy—an ordering of propositions by closeness to the objective truth of some matter. The epistemic utility program (instigated by Hempel) pursued the notion of credal state accuracy—an ordering of credal states by closeness to the ideal credal state. It may be tempting to think that each question that informs an inquiry must be  determinate of necessity. That is to say, there must be one correct, precise answer to it. That precise, correct answer is not only the maximally accurate answer to the question, but it also singles out the ideal credal state that is the epistemic target of the inquiry. An enormous amount of the effort has been focused on accuracy with respect to necessarily determinate questions. But often the focus of an inquiry is a question that is not necessarily determinate. Those who deem accuracy to be cognitively valuable, and fundamentally so, need an account of accuracy for indeterminate questions. Ideally such an account would cover both propositional accuracy and credal state accuracy. This is a tall order, and it is not clear that the order can be filled. [ABSTRACT FROM AUTHOR]

Truthlikeness is a property of a theory or a proposition that represents its closeness to the truth. We start by summarizing Niiniluoto's (Truthlikeness, Reidel, Dordrecht, 1987) proposal of truthlikeness for deterministic laws (DL), which defines truthlikeness as a function of accuracy, and García-Lapeña's (Br J Philos Sci, Forthcoming, 2021) expanded version, which defines truthlikeness for DL as a function of two factors, accuracy and nomicity. Then, we move to develop an appropriate definition of truthlikeness for probabilistic laws (PL) based on Niiniluoto's (Truthlikeness, Reidel, Dordrecht, 1987) suggestion to use the Kullback–Leibler divergence to define the distance between a probability law X and the true probability law T . We argue that the Kullback–Leibler divergence seems to be the best of the available probability distances to measure accuracy between PL. However, as in the case of DL, we argue that accuracy represents a necessary but not sufficient condition, as two PL may be equally accurate and still one may imply more true or truthlike consequences, behaviours or true facts about the system than the other. The final proposal defines truthlikeness for PL as a function of two factors, p-accuracy and p-nomicity, in intimate connexion with García-Lapeña's proposal for DL. [ABSTRACT FROM AUTHOR]

There is an ongoing debate about which rule we ought to use for scoring probability estimates. Much of this debate has been premised on scoring-rule monism, according to which there is exactly one best scoring rule. In previous work, I have argued against this position. The argument given there was based on purely a priori considerations, notably the intuition that scoring rules should be sensitive to truthlikeness relations if, and only if, such relations are present among whichever hypotheses are at issue. The present paper offers a new, quasi-empirical argument against scoring-rule monism. This argument uses computational simulations to show that different scoring rules can have different economical consequences, depending on the context of use. [ABSTRACT FROM AUTHOR]

Theories of truth approximation in terms of truthlikeness (or verisimilitude) almost always deal with (non-probabilistically) approaching deterministic truths, either actual or nomic. This paper deals first with approaching a probabilistic nomic truth, viz. a true probability distribution. It assumes a multinomial probabilistic context, hence with a lawlike true, but usually unknown, probability distribution. We will first show that this true multinomial distribution can be approached by Carnapian inductive probabilities. Next we will deal with the corresponding deterministic nomic truth, that is, the set of conceptually possible outcomes with a positive true probability. We will introduce Hintikkian inductive probabilities, based on a prior distribution over the relevant deterministic nomic theories and on conditional Carnapian inductive probabilities, and first show that they enable again probabilistic approximation of the true distribution. Finally, we will show, in terms of a kind of success theorem, based on Niiniluoto's estimated distance from the truth, in what sense Hintikkian inductive probabilities enable the probabilistic approximation of the relevant deterministic nomic truth. In sum, the (realist) truth approximation perspective on Carnapian and Hintikkian inductive probabilities leads to the unification of the inductive probability field and the field of truth approximation. [ABSTRACT FROM AUTHOR]

This paper argues that some of the discussion around meta-scientific issues can be viewed as an argument over different "meta-hypotheses" – assumptions made about how different hypotheses in a scientific literature relate to each other. I argue that, currently, such meta-hypotheses are typically left unstated except in methodological papers and that the consequence of this practice is that it is hard to determine what can be learned from a direct replication study. I argue in favor of a procedure dubbed the "limited homogeneity assumption" – assuming very little heterogeneity of effect sizes when a literature is initiated but switching to an assumption of heterogeneity once an initial finding has been successfully replicated in a direct replication study. Until that has happened, we do not allow the literature to proceed to a mature stage. This procedure will elevate the scientific status of direct replication studies in science. Following this procedure, a well-designed direct replication study is a means of falsifying an overall claim in an early phase of a literature and thus sets up a hurdle against the canonization of false facts in the behavioral sciences. [ABSTRACT FROM AUTHOR]

Knowledge representation is a central issue in a number of areas, but few attempts are usually made to bridge different approaches accross different fields. As a contribution in this direction, in this paper I focus on one such approach, the theory of conceptual spaces developed within cognitive science, and explore its potential applications in the fields of philosophy of science and formal epistemology. My case-study is provided by the theory of truthlikeness (or verisimilitude), construed as closeness to "the whole truth" about a given domain, as described in the underlying language. I show how modeling propositions and their relations within a conceptual space has interesting implications for two issues in truthlikeness theory: the so called problem of language dependence, and that of measure sensitivity. I conclude by pointing at some open issues arising from the application of conceptual spaces to the analysis of philosophical problems. [ABSTRACT FROM AUTHOR]

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This is a response to the papers in the special issue Logical Perspectives on Science and Cognition—The Philosophy of Gerhard Schurz. [ABSTRACT FROM AUTHOR]

Popper's original definition of truthlikeness relied on a central insight: that truthlikeness combines truth and information, in the sense that a proposition is closer to the truth the more true consequences and the less false consequences it entails. As intuitively compelling as this definition may be, it is untenable, as proved long ago; still, one can arguably rely on Popper's intuition to provide an adequate account of truthlikeness. To this aim, we mobilize some classical work on partial entailment in defining a new measure of truthlikeness which satisfies a number of desiderata. The resulting account has some interesting and surprising connections with other accounts on the market, thus shedding new light on current attempts of systematizing different approaches to verisimilitude. [ABSTRACT FROM AUTHOR]

The notion of truthlikeness or verisimilitude has been a topic of intensive discussion ever since the definition proposed by Karl Popper was refuted in 1974. This paper gives an analysis of old and new debates about this notion. There is a fairly large agreement about the truthlikeness ordering of conjunctive theories, but the main rival approaches differ especially about false disjunctive theories. Continuing the debate between Niiniluoto's min-sum measure and Schurz's relevant consequence measure, the paper also gives a critical assessment of Oddie's new defense of the average measure and Kuipers' refined definition of truth approximation. [ABSTRACT FROM AUTHOR]

A number of authors have recently put forward arguments pro or contra various rules for scoring probability estimates. In doing so, they have skipped over a potentially important consideration in making such assessments, to wit, that the hypotheses whose probabilities are estimated can approximate the truth to different degrees. Once this is recognized, it becomes apparent that the question of how to assess probability estimates depends heavily on context. [ABSTRACT FROM AUTHOR]

Assuming that the target of theory oriented empirical science in general and of nomic truth approximation in particular is to characterize the boundary or demarcation between nomic possibilities and nomic impossibilities, I have presented, in my article entitled "Models, postulates, and generalized nomic truth approximation" (Kuipers in Synthese 193(10):3057–3077, 2016. 10.1007/s11229-015-0916-9), the 'basic' version of generalized nomic truth approximation, starting from 'two-sided' theories. Its main claim is that nomic truth approximation can perfectly be achieved by combining two prima facie opposing views on theories: (1) the traditional (Popperian) view: theories are (models of) postulates that exclude certain possibilities from being realizable, enabling explanation and prediction and (2) the model view: theories are sets of models that claim to (approximately) represent certain realizable possibilities. Nomic truth approximation, i.e. increasing truth-content and decreasing falsity-content, becomes in this way revising theories by revising their models and/or their postulates in the face of increasing evidence. The basic version of generalized nomic truth approximation is in many respects as simple as possible. Among other things, it does not take into account that one conceptual possibility may be more similar (or closer) to another than a third one (is to that other). However, for example, one theory may include a possibility that is more similar to a wrongly not included possibility than another theory can offer. Similarly, for wrongly not excluded possibilities. In this article it will be shown that such 'refined' considerations can be taken into account by adapted clauses based on a ternary similarity relation between possibilities (structures). This allows again abductive conclusions about refined truth approximation if a theory is persistently more successful in the refined sense than another. It will also be indicated and illustrated that this refined approach enables a specification to the effect that refined truth approximation can be obtained by the method of idealization and subsequent concretization. Finally, the basic and the refined approach will be evaluated with regard to some general principles and objections that have been discussed in the literature. [ABSTRACT FROM AUTHOR]

This paper builds an integrated framework of measures of information based on the Model for Information (MfI) developed by the author. Since truth is expressed using information, an analysis of truth depends on the nature of information and its limitations. These limitations include those implied by the geometry of information and those implied by the relativity of information. This paper proposes an approach to truth and truthlikeness that takes these limitations into account by incorporating measures of the quality of information. Another measure of information is the amount of information. This has played a role in two important theoretical difficulties—the Bar-Hillel Carnap paradox and the “scandal of deduction”. This paper further provides an analysis of the amount of information, based on MfI, and shows how the MfI approach can resolve these difficulties.

In a 2017 AJP paper, Cevolani and Schurz (C&S) propose a novel solution to the Preface Paradox that appeals to the notion of expected truthlikeness. This discussion note extends and analyses their approach by applying it to the related Lottery Paradox. [ABSTRACT FROM AUTHOR]

Scientific realists use the 'no miracle argument' to show that the empirical and pragmatic success of science is an indicator of the ability of scientific theories to give true or truthlike representations of unobservable reality. While antirealists define scientific progress in terms of empirical success or practical problem-solving, realists characterize progress by using some truth-related criteria. This paper defends the definition of scientific progress as increasing truthlikeness or verisimilitude. Antirealists have tried to rebut realism with the 'pessimistic metainduction', but critical realists turn this argument into an optimistic view about progressive science. [ABSTRACT FROM AUTHOR]

The so-called Preface Paradox seems to show that one can rationally believe two logically incompatible propositions. We address this puzzle, relying on the notions of truthlikeness and approximate truth as studied within the post-Popperian research programme on verisimilitude. In particular, we show that adequately combining probability, approximate truth, and truthlikeness leads to an explanation of how rational belief is possible in the face of the Preface Paradox. We argue that our account is superior to other solutions of the paradox, including a recent one advanced by Hannes Leitgeb (Analysis74.1). [ABSTRACT FROM AUTHOR]

The notion of truthlikeness (verisimilitude, approximate truth), coined by Karl Popper, has very much fallen into oblivion, but the paper defends it. It can be regarded in two different ways. Either as a notion that is meaningful only if some formal measure of degree of truthlikeness can be constructed; or as a merely non-formal comparative notion that nonetheless has important functions to fulfill. It is the latter notion that is defended; it is claimed that such a notion is needed for both a reasonable backward-looking and a reasonable forward-looking view of science. On the one hand, it is needed in order to make sense of the history of science as containing a development; on the other, it is needed in order to understand present-day sciences as containing knowledge-seeking activities. The defense of truthlikeness requires also a defense of two other notions: quasi- comparisons and regulative ideas, which is supplied in this paper as well. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to look at some existing methods of semantic information quantification and suggest some alternatives. It begins with an outline of Bar-Hillel and Carnap’s theory of semantic information before going on to look at Floridi’s theory of strongly semantic information. The latter then serves to initiate an in-depth investigation into the idea of utilising the notion of truthlikeness to quantify semantic information. Firstly, a couple of approaches to measure truthlikeness are drawn from the literature and explored, with a focus on their applicability to semantic information quantification. Secondly, a similar but new approach to measure truthlikeness/information is presented and some supplementary points are made.

Theories of truthlikeness (or verisimilitude) are currently being classified according to two independent distinctions: that between 'content' and 'likeness' accounts, and that between 'conjunctive' and 'disjunctive' ones. In this article, I present and discuss a new definition of truthlikeness, which employs Carnap's notion of the content elements entailed by a theory or proposition, and is then labelled 'Carnapian'. After studying in detail the properties and shortcomings of this definition, I argue that it occupies a unique position in the landscape of different approaches to truthlikeness. More precisely, I show that it provides the only explication of truthlikeness which is both 'conjunctive' and 'content-based' in a suitably defined sense. [ABSTRACT FROM AUTHOR]

The merging/fusion of belief/data collections in propositional logic form is a topic that has received due attention within the domains of database and AI research. A distinction can be made between two types of scenarios to which the process of merging can be applied. In the first type, the collections represent preferences, such as the voting choices of a group of people, that need to be aggregated so as to give a consistent result that in some way best represents the collective judgement of the group. In the second type, the collections represent factual data that is to be aggregated with an aim of obtaining a result that maximises factual correctness. After introducing a general framework for belief merging via some prominent literature on the topic, this paper then introduces and considers a method for belief merging with the second type of scenario in mind. Its suitability is corroborated by demonstrating how it can be seen as a special case of a merging procedure that combines aggregation of probabilities and maximisation of expected truthlikeness. [ABSTRACT FROM AUTHOR]

This paper builds an integrated framework of measures of information based on the Model for Information (MfI) developed by the author. Since truth is expressed using information, an analysis of truth depends on the nature of information and its limitations. These limitations include those implied by the geometry of information and those implied by the relativity of information. This paper proposes an approach to truth and truthlikeness that takes these limitations into account by incorporating measures of the quality of information. Another measure of information is the amount of information. This has played a role in two important theoretical difficulties—the Bar-Hillel Carnap paradox and the "scandal of deduction". This paper further provides an analysis of the amount of information, based on MfI, and shows how the MfI approach can resolve these difficulties. [ABSTRACT FROM AUTHOR]

Woosuk Park's paper 'Misrepresentation in Context' is a useful plea for a theory of representation with promising interaction between cognitive science, philosophy of science, and aesthetics. In this paper, I argue that such a unified account is provided by Charles S. Peirce's semiotics. This theory puts Park's criticism of Nelson Goodman and Jerry Fodor in context. Some of Park's pertinent remarks on the problem of misrepresentation can be illuminated by the account of truthlikeness and idealization developed by philosophers of science. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the problem of truth approximation via belief merging, i.e., we ask whether, and under what conditions, a group of inquirers merging together their beliefs makes progress toward the truth about the underlying domain. We answer this question by proving some formal results on how belief merging operators perform with respect to the task of truth approximation, construed as increasing verisimilitude or truthlikeness. Our results shed new light on the issue of how rational (dis)agreement affects the inquirers' quest for truth. In particular, they vindicate the intuition that scientific inquiry, and rational discussion in general, benefits from some heterogeneity in opinion and interaction among different viewpoints. The links between our approach and related analyses of truth tracking, judgment aggregation, and opinion dynamics, are also highlighted. [ABSTRACT FROM AUTHOR]

Highlights: [•] Science does not make progress by accumulating justified truths [•] Fallibilists argue that successions of false theories may progress toward the truth [•] Scientific progress means that successor theories have higher degree of truthlikeness [•] Degrees of truthlikeness can be estimated by their evidence-relative expected values. [Copyright &y& Elsevier]