We propose a new model for forming and revising beliefs about unknown probabilities. To go beyond what is known with certainty and represent the agent’s beliefs about probability, we consider a plausibility map, associating to each possible distribution a plausibility ranking. Beliefs are defined as in Belief Revision Theory, in terms of truth in the most plausible worlds. We consider two forms of conditioning or belief update, corresponding to the acquisition of two types of information: learning observable evidence obtained by (...) repeated sampling from the unknown distribution; and learning higher-order information about the distribution. The first changes only the plausibility map, but leaves the given set of possible distributions essentially unchanged; the second rules out some distributions, thus shrinking the set of possibilities, without changing their plausibility ordering.. We look at stability of beliefs under either of these types of learning, defining two related notions, as well as a measure of the verisimilitude of a given plausibility model. We prove a number of convergence results, showing how our agent’s beliefs track the true probability after repeated sampling, and how she eventually gains in a sense knowledge of that true probability. Finally, we sketch the contours of a dynamic doxastic logic for statistical learning. (shrink)

This paper concerns the extent to which uncertain propositional reasoning can track probabilistic reasoning, and addresses kinematic problems that extend the familiar Lottery paradox. An acceptance rule assigns to each Bayesian credal state p a propositional belief revision method B p , which specifies an initial belief state B p (T) that is revised to the new propositional belief state B(E) upon receipt of information E. An acceptance rule tracks Bayesian conditioning when B p (E) = B p|E (T), (...) for every E such that p(E) > 0; namely, when acceptance by propositional belief revision equals Bayesian conditioning followed by acceptance. Standard proposals for uncertain acceptance and belief revision do not track Bayesian conditioning. The "Lockean" rule that accepts propositions above a probability threshold is subject to the familiar lottery paradox (Kyburg 1961), and we show that it is also subject to new and more stubborn paradoxes when the tracking property is taken into account. Moreover, we show that the familiar AGM approach to belief revision (Harper, Synthese 30(1-2):221-262, 1975; Alchourrón et al., J Symb Log 50:510-530, 1985) cannot be realized in a sensible way by any uncertain acceptance rule that tracks Bayesian conditioning. Finally, we present a plausible, alternative approach that tracks Bayesian conditioning and avoids all of the paradoxes. It combines an odds-based acceptance rule proposed originally by Levi (1996) with a non-AGM belief revision method proposed originally by Shoham (1987). (shrink)

Several authors have investigated the question of whether canonical logic-based accounts of belief revision, and especially the theory of AGM revision operators, are compatible with the dynamics of Bayesian conditioning. Here we show that Leitgeb's stability rule for acceptance, which has been offered as a possible solution to the Lottery paradox, allows to bridge AGM revision and Bayesian update: using the stability rule, we prove that AGM revision operators emerge from Bayesian conditioning by an application of the principle of maximum (...) entropy. In situations of information loss, or whenever the agent relies on a qualitative description of her information state - such as a plausibility ranking over hypotheses, or a belief set - the dynamics of AGM belief revision are compatible with Bayesian conditioning; indeed, through the maximum entropy principle, conditioning naturally generated AGM revision operators. This mitigates an impossibility theorem of Lin and Kelly for tracking Bayesian conditioning with AGM revision, and suggests an approach to the compatibility problem that highlights the information loss incurred by acceptance rules in passing from probabilistic to qualitative representations of beliefs. (shrink)

Recent evidence increasingly suggests that comprehenders are capable of generating probabilistic predictions about forthcoming linguistic inputs during language comprehension. However, it remains debated whether language comprehenders predict low‐level word forms and whether they always make predictions. In this study, we investigated semantic and phonological prediction in high‐ and low‐constraining sentence contexts, utilizing the mouse‐tracking paradigm to trace mouse movement trajectories. Mandarin Chinese speakers listened to high‐ and low‐constraining sentences which resulted in high and low predictability for the critical (...) target words. While listening, participants viewed a visual display featuring two objects: one corresponding to the critical target word (the target object) and the other being either semantically related, phonologically related, or unrelated to the target word. Participants were instructed to click on the target object. The analysis of mouse movement trajectories revealed two key findings: (1) In both high‐ and low‐constraining contexts, there was a spatial attraction of the cursor toward semantic competitors, notably occurring before the target word was heard; (2) there are indications that phonological pretarget attraction effects were observed primarily in high‐constraining contexts. These findings suggest that the constraints of sentences have the potential to modulate the representational contents of linguistic prediction during language comprehension. Methodologically, the mouse‐tracking paradigm presents a promising tool for further exploration of linguistic prediction. (shrink)

Changing the information format from probabilities into frequencies as well as employing appropriate visualizations such as tree diagrams or 2 × 2 tables are important tools that can facilitate people’s statistical reasoning. Previous studies have shown that despite their widespread use in statistical textbooks, both of those visualization types are only of restricted help when they are provided with probabilities, but that they can foster insight when presented with frequencies instead. In the present study, we attempt to replicate this effect (...) and also examine, by the method of eye tracking, why probabilistic 2 × 2 tables and tree diagrams do not facilitate reasoning with regard to Bayesian inferences (i.e., determining what errors occur and whether they can be explained by scan paths), and why the same visualizations are of great help to an individual when they are combined with frequencies. All ten inferences of N = 24 participants were based solely on tree diagrams or 2 × 2 tables that presented either the famous “mammography context” or an “economics context” (without additional textual wording). We first asked participants for marginal, conjoint, and (non-inverted) conditional probabilities (or frequencies), followed by related Bayesian tasks. While solution rates were higher for natural frequency questions as compared to probability versions, eye-tracking analyses indeed yielded noticeable differences regarding eye movements between correct and incorrect solutions. For instance, heat maps (aggregated scan paths) of distinct results differed remarkably, thereby making correct and faulty strategies visible in the line of theoretical classifications. Moreover, the inherent structure of 2 × 2 tables seems to help participants avoid certain Bayesian mistakes (e.g., “Fisherian” error) while tree diagrams seem to help steer them away from others (e.g., “joint occurrence”). We will discuss resulting educational consequences at the end of the paper. (shrink)

The aggregation of consistent individual judgments on logically interconnected propositions into a collective judgment on those propositions has recently drawn much attention. Seemingly reasonable aggregation procedures, such as propositionwise majority voting, cannot ensure an equally consistent collective conclusion. The literature on judgment aggregation refers to that problem as the discursive dilemma. In this paper, we motivate that many groups do not only want to reach a factually right conclusion, but also want to correctly evaluate the reasons for that conclusion. In (...) other words, we address the problem of tracking the true situation instead of merely selecting the right outcome. We set up a probabilistic model analogous to Bovens and Rabinowicz (2006) and compare several aggregation procedures by means of theoretical results, numerical simulations and practical considerations. Among them are the premise-based, the situation-based and the distance-based procedure. Our findings confirm the conjecture in Hartmann, Pigozzi and Sprenger (2008) that the premise-based procedure is a crude, but reliable and sometimes even optimal form of judgment aggregation. (shrink)

The lottery problem is often regarded as a successful counterexample to reliabilism. The process of forming your true belief that your ticket has lost solely on the basis of considering the odds is, from a purely probabilistic viewpoint, much more reliable than the process of forming a true belief that you have lost by reading the results in a normally reliable newspaper. Reliabilism thus seems forced, counterintuitively, to count the former process as knowledge if it so counts the latter (...) process. -/- I offer a theory of empirical knowledge which, while being recognizably reliabilist, restricts empirical knowledge to cases in which the fact that p and the belief that p are causally connected. I show that this form of reliabilism solves the lottery problem, avoids the problems that beset the causal theory of knowledge, and show how it handles a number of problematic cases in the recent literature. (shrink)

This paper explores the space of (propositional) probabilistic logical languages, ranging from a purely `qualitative' comparative language to a highly `quantitative' language involving arbitrary polynomials over probability terms. While talk of qualitative vs. quantitative may be suggestive, we identify a robust and meaningful boundary in the space by distinguishing systems that encode (at most) additive reasoning from those that encode additive and multiplicative reasoning. The latter includes not only languages with explicit multiplication but also languages expressing notions of dependence (...) and conditionality. We show that the distinction tracks a divide in computational complexity: additive systems remain complete for NP, while multiplicative systems are robustly complete for ∃ℝ. We also address axiomatic questions, offering several new completeness results as well as a proof of non-finite-axiomatizability for comparative probability. Repercussions of our results for conceptual and empirical questions are addressed, and open problems are discussed. (shrink)

On the standard conception of risk, the degree to which an event is risky is the function of the probability of that event. Recently, Duncan Pritchard has challenged this view, proposing instead a modal account on which risk is conceived of in terms of modal ordering (2015). On this account, the degree of risk for any given event is a function of its modal distance from the actual world, not its likelihood. Pritchard's main motivation for this is that the (...) class='Hi'>probabilistic account cannot always explain our judgements about risk. In certain cases, equally probable events are not judged to be equally risky. Here I will argue that Pritchard's account succumbs to a similar problem. Put simply, there are cases in which judgements about risk decouple from both probability and modal ordering. Thus, if we want a theory of risk that can explain our judgements about risk, neither the probabilistic nor modal account is successful. (shrink)

Many who think that naked statistical evidence alone is inadequate for a trial verdict think that use of probability is the problem, and something other than probability – knowledge, full belief, causal relations – is the solution. I argue that the issue of whether naked statistical evidence is weak can be formulated within the probabilistic idiom, as the question whether likelihoods or only posterior probabilities should be taken into account in our judgment of a case. This question also identifies (...) a major difference between the Process Reliabilist and Probabilistic Tracking views of knowledge and other concepts. Though both are externalist, and probabilistic, epistemic theories, Tracking does and Process Reliabilism does not put conditions on likelihoods. So Tracking implies that a naked statistic is not adequate evidence about an individual, and does not yield knowledge, whereas the Reliabilist thinks it gives justified belief and knowledge. Not only does the Tracking view imply that naked statistical evidence is insufficient for a verdict, but it gives us resources to explain why, in terms of risk and the special conditions of a trial. (shrink)

I take issue with Robert Brandom’s claim that on an analysis of knowledge based on objective probabilities it is not possible to provide a stable answer to the question whether a belief has the status of knowledge. I argue that the version of the problem of generality developed by Brandom doesn’t undermine a truth-tracking account of noninferential knowledge that construes truth-tacking in terms of conditional probabilities. I then consider Sherrilyn Roush’s claim that an account of knowledge based on (...) class='Hi'>probabilistic tracking faces a version of the problem of generality. I argue that the problems she raises are specific to her account, and do not affect the version of the view that I have advanced. I then consider Brandom’s argument that the cases that motivate reliabilist epistemologies are in principle exceptional. I argue that he has failed to make a cogent case for this claim. I close with the suggestion that the representationalist approach to knowledge that I endorse and Brandom rejects is in principle compatible with the kind of pragmatist approach to belief and truth that both Brandom and I endorse. (shrink)

We investigated the effects of everyday language exposure on the prediction of orthographic and phonological forms of a highly predictable word during listening comprehension. Native Japanese speakers in Tokyo (Experiment 1) and Berlin (Experiment 2) listened to sentences that contained a predictable word and viewed four objects. The critical object represented the target word (e.g., /sakana/;fish), an orthographic competitor (e.g., /tuno/;horn), a phonological competitor (e.g., /sakura/;cherry blossom), or an unrelated word (e.g., /hon/;book). The three other objects were distractors. The Tokyo (...) group fixated the target and the orthographic competitor over the unrelated objects before the target word was mentioned, suggesting that they pre-activated the orthographic form of the target word. The Berlin group showed a weaker bias toward the target than the Tokyo group, and they showed a tendency to fixate the orthographic competitor only when the orthographic similarity was very high. Thus, prediction effects were weaker in the Berlin group than in the Tokyo group. We found no evidence for the prediction of phonological information. The obtained group differences support probabilistic models of prediction, which regard the built-up language experience as a basis of prediction. (shrink)

Crupi et al. (2008) offer a confirmation-theoretic, Bayesian account of the conjunction fallacy—an error in reasoning that occurs when subjects judge that Pr( h 1 & h 2 | e ) > Pr( h 1 | e ). They introduce three formal conditions that are satisfied by classical conjunction fallacy cases, and they show that these same conditions imply that h 1 & h 2 is confirmed by e to a greater extent than is h 1 alone. Consequently, they suggest (...) that people are tracking this confirmation relation when they commit conjunction fallacies. I offer three experiments testing the merits of Crupi et al.’s account specifically and confirmation-theoretic accounts of the conjunction fallacy more generally. The results of Experiment 1 show that, although Crupi et al.’s conditions do seem to be causally linked to the conjunction fallacy, they are not necessary for it; there exist cases that do not meet their three conditions in which subjects still tend to commit the fallacy. The results of Experiments 2 and 3 show that Crupi et al.’s conditions, and those offered by other confirmation-theoretic accounts of the fallacy, are not sufficient for the fallacy either; there exist cases that meet all three of CFT’s conditions in which subjects do not tend to commit the fallacy. Additionally, these latter experiments show that such confirmation-theoretic conditions are at best only weakly causally relevant to the presence of the conjunction fallacy. Given these findings, CFT’s account specifically, and any general confirmation-theoretic account more broadly, falls short of offering a satisfying explanation of the presence of the conjunction fallacy. (shrink)

One of the major problems that artificial intelligence needs to tackle is the combination of different and potentially conflicting sources of information. Examples are multi-sensor fusion, database integration and expert systems development. In this paper we are interested in the aggregation of propositional logic-based information, a problem recently addressed in the literature on information fusion. It has applications in multi-agent systems that aim at aggregating the distributed agent-based knowledge into an (ideally) unique set of propositions. We consider a group of (...) autonomous agents who individually hold a logically consistent set of propositions. Each set of propositions represents an agent’s beliefs on issues on which the group has to make a collective decision. To make the collective decision, several aggregation procedures have been proposed in the literature. Assuming that all propositions in question are factually right or wrong, we ask how good belief fusion is as a truth tracker. Will it single out the true set of propositions? And how does information fusion compare with other aggregation procedures? We address these questions in a probabilistic framework and show that information fusion does especially well for agents with a middling competence of hitting the truth of an individual proposition. (shrink)

This paper argues that if knowledge is defined in terms of probabilistic tracking then the benefits of epistemic closure follow without the addition of a closure clause. (This updates my definition of knowledge in Tracking Truth 2005.) An important condition on this result is found in "Closure Failure and Scientific Inquiry" (2017).

The acquisition of complex motor, cognitive, and social skills, like playing a musical instrument or mastering sports or a language, is generally associated with implicit skill learning . Although it is a general view that SL is most effective in childhood, and such skills are best acquired if learning starts early, this idea has rarely been tested by systematic empirical studies on the developmental pathways of SL from childhood to old age. In this paper, we challenge the view that childhood (...) and early school years are the prime time for skill learning by tracking age-related changes in performance in three different paradigms of SL. We collected data from participants between 7 and 87 years for a Serial Reaction Time Task testing the learning of motor sequences, an Artificial Grammar Learning task testing the extraction of regularities from auditory sequences, and Probabilistic Category Learning in the Weather Prediction task , a non-sequential categorization task. Results on all three tasks show that adolescence and adulthood are the most efficient periods for skill learning, since instead of becoming less and less effective with age, SL improves from childhood into adulthood and then later declines with aging. (shrink)

Intonation plays an integral role in comprehending spoken language. Listeners can rapidly integrate intonational information to predictively map a given pitch accent onto the speaker's likely referential intentions. We use mouse tracking to investigate two questions: (a) how listeners draw predictive inferences based on information from intonation? and (b) how listeners adapt their online interpretation of intonational cues when these are reliable or unreliable? We formulate a novel Bayesian model of rational predictive cue integration and explore predictions derived under (...) a concrete linking hypothesis relating a quantitative notion of evidential strength of a cue to the moment in time, relative to the unfolding speech signal, at which mouse trajectories turn towards the eventually selected option. In order to capture rational belief updates after concrete observations of a speaker's behavior, we formulate and explore an extension of this model that includes the listener's hierarchical beliefs about the speaker's likely production behavior. Our results are compatible with the assumption that listeners rapidly and rationally integrate all available intonational information, that they expect reliable intonational information initially, and that they adapt these initial expectations gradually during exposition to unreliable input. All materials, data, and scripts can be retrieved here: https://osf.io/dnbuk/. (shrink)

Spontaneous collapse theories of quantum mechanics turn the usual Schrödinger equation into a stochastic dynamical law. In particular, in this paper, I will focus on the GRW theory. Two philosophical issues that can be raised about GRW concern (i) the ontology of the theory, in particular the nature of the wave function and its role within the theory, and (ii) the interpretation of the objective probabilities involved in the dynamics of the theory. During the last years, it has been claimed (...) that we can take advantage of dispositional properties in order to develop an ontology for GRW theory, and also in order to ground the objective probabilities which are postulated by it. However, in this paper, I will argue that the dispositional interpretations which have been discussed in the literature so far are either flawed or – at best – incomplete. If we want to endorse a dispositional interpretation of GRW theory we thus need an extended account that specifies the precise nature of those properties and which makes also clear how they can correctly ground all the probabilities postulated by the theory. Thus, after having introduced several different kinds of probabilistic dispositions, I will try to fill the gap in the literature by proposing a novel and complete dispositional account of GRW, based on what I call spontaneous weighted multi-track propensities. I claim that such an account can satisfy both of our desiderata. (shrink)

In the aftermath of Gettier’s examples, knowledge came to be thought of as what you would have if in addition to a true belief and your favorite epistemic goody, such as justifiedness, you also were ungettiered, and the theory of knowledge was frequently equated, especially by its detractors, with the project of pinning down that extra bit. It would follow that knowledge contributes something distinctive that makes it indispensable in our pantheon of epistemic concepts only if avoiding gettierization has a (...) value that can be explained without presupposing the value of knowledge. Tracking-type knowledge has a value that no other logically possible conditions on true belief does. As an Evolutionarily Stable Strategy it preserves appropriate belief states through time and changing circumstances. If we characterize gettierization through the concept of relevance matching, then we see that avoiding gettierization has a value independent of that of knowledge, namely, understanding, and that it is unnecessary to add a clause to the tracking conditions to make them suppress gettierization directly, though fallibly. The bright line of value is between gettierization avoidance and understanding on the one hand and knowledge on the other, and so should be the bright line defining concepts. The concept of relevance matching is key to a definition of what it is to understand why p is true, as opposed merely to knowing that p is true. Perfect tracking implies perfect relevance matching, so knowledge and understanding are intimately connected but understanding also requires that one own states that accomplish the relevance matching rather than achieving it vicariously. The theory of understanding based on relevance matching implies that understanding requires appreciation of not only p but its connections to other matters, and explains how it is possible to know that p is true without understanding why. The view implies that understanding is literally simulation, and is suggestive about understanding other minds. (shrink)

Making decisions can be hard, but it can also be facilitated. Simple heuristics are fast and frugal but nevertheless fairly accurate decision rules that people can use to compensate for their limitations in computational capacity, time, and knowledge when they make decisions [Gigerenzer, G., Todd, P. M., & the ABC Research Group (1999). Simple Heuristics That Make Us Smart. New York: Oxford University Press.]. These heuristics are effective to the extent that they can exploit the structure of information in the (...) environment in which they operate. Specifically, they require knowledge about the predictive value of probabilistic cues. However, it is often difficult to keep track of all the available cues in the environment and how they relate to any relevant criterion. This problem becomes even more critical if compound cues are considered. We submit that knowledge about the causal structure of the environment helps decision makers focus on a manageable subset of cues, thus effectively reducing the potential computational complexity inherent in even relatively simple decision-making tasks. We review experimental evidence that tested this hypothesis and report the results of a simulation study. We conclude that causal knowledge can act as a meta-cue for identifying highly valid cues, either individual or compound, and helps in the estimation of their validities. (shrink)

Some skeptical theists use Wykstra’s CORNEA constraint to undercut Rowe-style inductive arguments from evil. Many critics of skeptical theism accept CORNEA, but argue that Rowe-style arguments meet its constraint. But Justin McBrayer argues that CORNEA is itself mistaken. It is, he claims, akin to “sensitivity” or “truth-tracking” constraints like those of Robert Nozick; but counterexamples show that inductive evidence is often insensitive. We here defend CORNEA against McBrayer’s chief counterexample. We first clarify CORNEA, distinguishing it from a deeper underlying (...) principle that we dub “CORE.” We then give both principles a probabilistic construal, and show how, on this construal, the counterexample fails. (shrink)

This paper provides a new approach to inference to the best explanation based on a new coherence measure for comparing how well hypotheses explain the evidence. It addresses a number of criticisms of the use of probabilistic measures in this context by Clark Glymour, including limitations of earlier work on IBE. Computer experiments are used to show that the new approach finds the truth with a high degree of accuracy in hypothesis selection tasks and that in some cases its (...) accuracy is greater than hypothesis selection based on maximizing posterior probability. Hence, by overcoming some of the problems with the previous approach, this work provides a more adequate defence of IBE and suggests that IBE not only tracks truth but also has practical advantages over the previous approach. Applications of the new approach to parameter estimation and model selection are also explored. (shrink)

Orthodox Bayesianism endorses revising by conditionalization. This paper investigates the zero-raising problem, or equivalently the certainty-dropping problem of orthodox Bayesianism: previously neglected possibilities remain neglected, although the new evidence might suggest otherwise. Yet, one may want to model open-minded agents, that is, agents capable of raising previously neglected possibilities. Different reasons can be given for open-mindedness, one of which is fallibilism. The paper proposes a family of open-minded propositional revisions depending on a parameter ϵ. The basic idea is this: first (...) extend the prior to the newly suggested possibilities by mixing the prior with the uniform probability on these possibilities, then conditionalize. This may put the agent back on the right track when her beliefs or evidence happen to be false. The paper justifies this family of equivocal epsilon-conditionalizations as minimal non-biased open-minded modifications of conditionalization. Several variations are discussed, such as mixing with an ad hoc or silent prior instead of the uniform prior, and a generalization to probabilistic information is given. The approach is compared to other accounts, such as Jeffrey’s Bayesianism, Gärdenfors’s probabilistic revision, maximizing entropy, and minimal revision. _1_ Introduction _2_ Downsides of Certainty Conservation _3_ Why Raise Zeros? _3.1_ Accuracy defects _3.2_ Fallibilism and dissolutions _3.3_ Weak fallibilism and partial solutions _4_ Epsilon-Conditionalization _4.1_ Definitions _4.2_ Revision types and information assumptions _4.3_ Epsilon interpretation and flavours _5_ Justifications _5.1_ Open-minded orthodox Bayesianism _5.2_ Minimal revision _5.3_ Transitivity _6_ Conclusion Appendix. (shrink)

This dissertation is devoted to empirically contrasting the Suppositional Theory of conditionals, which holds that indicative conditionals serve the purpose of engaging in hypothetical thought, and Inferentialism, which holds that indicative conditionals express reason relations. Throughout a series of experiments, probabilistic and truth-conditional variants of Inferentialism are investigated using new stimulus materials, which manipulate previously overlooked relevance conditions. These studies are some of the first published studies to directly investigate the central claims of Inferentialism empirically. In contrast, the Suppositional (...) Theory of conditionals has an impressive track record through more than a decade of intensive testing. The evidence for the Suppositional Theory encompasses three sources. Firstly, direct investigations of the probability of indicative conditionals, which substantiate “the Equation” (P(if A, then C) = P(C|A)). Secondly, the pattern of results known as “the defective truth table” effect, which corroborates the de Finetti truth table. And thirdly, indirect evidence from the uncertain and-to-if inference task. Through four studies each of these sources of evidence are scrutinized anew under the application of novel stimulus materials that factorially combine all permutations of prior and relevance levels of two conjoined sentences. The results indicate that the Equation only holds under positive relevance (P(C|A) – P(C|¬A) > 0) for indicative conditionals. In the case of irrelevance (P(C|A) – P(C|¬A) = 0), or negative relevance (P(C|A) – P(C|¬A) < 0), the strong relationship between P(if A, then C) and P(C|A) is disrupted. This finding suggests that participants tend to view natural language conditionals as defective under irrelevance and negative relevance (Chapter 2). Furthermore, most of the participants turn out only to be probabilistically coherent above chance levels for the uncertain and-to-if inference in the positive relevance condition, when applying the Equation (Chapter 3). Finally, the results on the truth table task indicate that the de Finetti truth table is at most descriptive for about a third of the participants (Chapter 4). Conversely, strong evidence for a probabilistic implementation of Inferentialism could be obtained from assessments of P(if A, then C) across relevance levels (Chapter 2) and the participants’ performance on the uncertain-and-to-if inference task (Chapter 3). Yet the results from the truth table task suggest that these findings could not be extended to truth-conditional Inferentialism (Chapter 4). On the contrary, strong dissociations could be found between the presence of an effect of the reason relation reading on the probability and acceptability evaluations of indicative conditionals (and connate sentences), and the lack of an effect of the reason relation reading on the truth evaluation of the same sentences. A bird’s eye view on these surprising results is taken in the final chapter and it is discussed which perspectives these results open up for future research. (shrink)

This study focuses on examining the thematic landscape of the history of scholarly publication in business ethics. We analyze the titles, abstracts, full texts, and citation information of all research papers published in the field’s leading journal, the Journal of Business Ethics, from its inaugural issue in February 1982 until December 2016—a dataset that comprises 6308 articles and 42 million words. Our key method is a computational algorithm known as probabilistic topic modeling, which we use to examine objectively the (...) field’s latent thematic landscape based on the vast volume of scholarly texts. This “big-data” approach allows us not only to provide time-specific snapshots of various research topics, but also to track the dynamic evolution of each topic over time. We further examine the pattern of individual papers’ topic diversity and the influence of individual papers’ topic diversity on their impact over time. We conclude this study with our recommendation for future studies in business ethics research. (shrink)

We use the normality framework of Goodman and Salow (2018, 2021, 2023b) to investigate of dynamics of rational belief. The guiding idea is that people are entitled to believe that their circumstances aren’t especially abnormal. More precisely, a rational agent’s beliefs rule out all and only those possibilities that are either (i) ruled out by their evidence or (ii) sufficiently less normal than some other possibility not ruled out by their evidence. Working within this framework, we argue that the logic (...) of rational belief revision is much weaker than is usually supposed. We do so by isolating a natural family of orthodox principles about belief revision, describing realistic cases in which these principles seem to fail, and showing how these counterexamples are predicted by independently motivated models of the cases in question. In these models, whether one evidential possibility counts as sufficiently less normal than another is determined by underlying probabilities (together with a contextually determined question). We argue that the resulting probabilistic account of belief compares favorably with other such accounts, including Lockeanism (Foley, 1993), a ‘stability’ account inspired by Leitgeb (2017), the ‘tracking theory’ of Lin and Kelly (2012), and the influential precursor of Levi (1967). We show that all of these accounts yield subtly different but similarly heterodox logics of belief revision. (shrink)

This article investigates the properties of multistate top revision, a dichotomous model of belief revision that is based on an underlying model of probability revision. A proposition is included in the belief set if and only if its probability is either 1 or infinitesimally close to 1. Infinitesimal probabilities are used to keep track of propositions that are currently considered to have negligible probability, so that they are available if future information makes them more plausible. Multistate top revision satisfies a (...) slightly modified version of the set of basic and supplementary AGM postulates, except the inclusion and success postulates. This result shows that hyperreal probabilities can provide us with efficient tools for overcoming the well known difficulties in combining dichotomous and probabilistic models of belief change. (shrink)

Many philosophers of science have argued that a set of evidence that is "coherent" confirms a hypothesis which explains such coherence. In this paper, we examine the relationships between probabilistic models of all three of these concepts: coherence, confirmation, and explanation. For coherence, we consider Shogenji's measure of association (deviation from independence). For confirmation, we consider several measures in the literature, and for explanation, we turn to Causal Bayes Nets and resort to causal structure and its constraint on probability. (...) All else equal, we show that focused correlation, which is the ratio of the coherence of evidence and the coherence of the evidence conditional on a hypothesis, tracks confirmation. We then show that the causal structure of the evidence and hypothesis can put strong constraints on how coherence in the evidence does or does not translate into confirmation of the hypothesis. (shrink)

How children go about learning the general regularities that govern language, as well as keeping track of the exceptions to them, remains one of the challenging open questions in the cognitive science of language. Computational modeling is an important methodology in research aimed at addressing this issue. We must determine appropriate learning mechanisms that can grasp generalizations from examples of specific usages, and that exhibit patterns of behavior over the course of learning similar to those in children. Early learning of (...) verb argument structure is an area of language acquisition that provides an interesting testbed for such approaches due to the complexity of verb usages. A range of linguistic factors interact in determining the felicitous use of a verb in various constructions—associations between syntactic forms and properties of meaning that form the basis for a number of linguistic and psycholinguistic theories of language. This article presents a computational model for the representation, acquisition, and use of verbs and constructions. The Bayesian framework is founded on a novel view of constructions as a probabilistic association between syntactic and semantic features. The computational experiments reported here demonstrate the feasibility of learning general constructions, and their exceptions, from individual usages of verbs. The behavior of the model over the timecourse of acquisition mimics, in relevant aspects, the stages of learning exhibited by children. Therefore, this proposal sheds light on the possible mechanisms at work in forming linguistic generalizations and maintaining knowledge of exceptions. (shrink)

Sensitivity is a modal epistemic principle. Modal knowledge accounts are externalist in nature and claim that the knowledge yielding connection between a true belief and the truthmaker must be spelled out in modal terms. The sensitivity condition was introduced by Robert Nozick. He suggests that if S knows that p, then S’s belief that p tracks truth. Nozick argues that this truth-tracking relation can be captured by subjunctive conditionals. As a first approximation, he provides the following modal analysis of (...) knowledge: S knows that p iff (1) p is true; (2) S believes that p; (3) if p were false, S wouldn’t believe that p and (4) if p were true, S would believe that p. The dominant terminology in the literature, also adopted here, is to call condition (3) the sensitivity condition and condition (4) the adherence condition. The sensitivity condition is intuitively appealing since it states that a subject does not know that p if she would believe that p even if p were false. Nozick used the sensitivity condition to accomplish two major tasks. First, he provided a solution to the Gettier problem by arguing that in Gettier cases subjects do not know since the sensitivity condition is violated. Second, he presented a controversial solution to the skeptical problem according to which we have external world knowledge but do not know that the skeptical hypothesis is false. This solution is available because sensitivity is not closed under known entailment. Quickly, criticism of the sensitivity condition emerged. First, most epistemologists regarded the price of abandoning knowledge closure as a price too high to pay. Second, it was noted that sensitivity leads to the counterintuitive consequence of precluding us from inductive knowledge since induction typically yields insensitive beliefs. The most dominant reaction to these problems was to replace sensitivity by the modal principle of safety, nowadays the most popular modal principle. However, sensitivity is not only important as a starting point of modal epistemology. Because of its intuitive attractiveness, many authors aimed at refining the original sensitivity account in order to avoid well-known problems. This has led to a second wave of sensitivity accounts. As of today, various sensitivity-based theories are on the market, including accounts that avoid closure failure, probabilistic interpretations of sensitivity and adherence, and contextualist approaches. There is thus a vivid and ongoing debate about the sensitivity principle in epistemology. (shrink)

Insight and strategy 2 Abstract In multiple-cue learning (also known as probabilistic category learning) people acquire information about cue-outcome relations and combine these into predictions or judgments. Previous studies claim that people can achieve high levels of performance without explicit knowledge of the task structure or insight into their own judgment policies. It has also been argued that people use a variety of suboptimal strategies to solve such tasks. In three experiments we re-examined these conclusions by introducing novel measures (...) of task knowledge and self-insight, and using ‘rolling regression’ methods to analyze individual learning. Participants successfully learned a four-cue probabilistic environment and showed accurate knowledge of both the task structure and their own judgment processes. Learning analyses suggested that the apparent use of suboptimal strategies emerges from the incremental tracking of statistical contingencies in the environment. (shrink)

We investigate how to assign probabilities to sentences that contain a type-free truth predicate. These probability values track how often a sentence is satisfied in transfinite revision sequences, following Gupta and Belnap’s revision theory of truth. This answers an open problem by Leitgeb which asks how one might describe transfinite stages of the revision sequence using such probability functions. We offer a general construction, and explore additional constraints that lead to desirable properties of the resulting probability function. One such property (...) is Leitgeb’s Probabilistic Convention T, which says that the probability of φ equals the probability that φ is true. (shrink)

What kind of thing do you believe when you believe that you are in a certain place, that it is a certain time, and that you are a certain individual? What happens if you get lost, or lose track of the time? Can you ever be unsure of your own identity? These are the kind of questions considered in my thesis. Beliefs about where, when and who you are are what are called in the literature de se, or self-locating beliefs. (...) This thesis examines how we can represent de se beliefs, and how we can reason about de se uncertainty. In the first part of the thesis, I present and motivate a specific account of the content of de se belief, based on the one given by David Lewis. On this account, the content of de se beliefs are centred propositions. I defend this view against a rival account, put forward by Robert Stalnaker, according to whom the content of de se beliefs are ordinary propositions. In the second part of the thesis, I explore how we can reason probabilistically about de se uncertainty. I start by defining probabilities over centred propositions, and investigate what probabilities mean in this context. As it turns out, all the main interpretations of probability can be extended to centred propositions. The only trouble seems to arise for the Bayesian principle of updating via conditionalization. After giving a diagnosis of the problem, I offer a solution by formulating a natural extension of conditionalization, which I argue preserves the essential features of Bayesian reasoning. In the final chapter, I apply my view and show that it leads to a natural resolution of a puzzle that is generally taken to be a test case for any account of centred updating. (shrink)

In this paper, we investigate the social herding phenomenon known as informational cascades, in which sequential inter-agent communication might lead to epistemic failures at group level, despite availability of information that should be sufficient to track the truth. We model an example of a cascade, and check the correctness of the individual reasoning of each agent involved, using two alternative logical settings: an existing probabilistic dynamic epistemic logic, and our own novel logic for counting evidence. Based on this analysis, (...) we conclude that cascades are not only likely to occur but are sometimes unavoidable by "rational" means: in some situations, the group’s inability to track the truth is the direct consequence of each agent’s rational attempt at individual truth-tracking. Moreover, our analysis shows that this is even so when rationality includes unbounded higher-order reasoning powers, as well as when it includes simpler, non-Bayesian forms of heuristic reasoning. (shrink)

Bayesian cognitive science is a research programme that relies on modelling resources from Bayesian statistics for studying and understanding mind, brain, and behaviour. Conceiving of mental capacities as computing solutions to inductive problems, Bayesian cognitive scientists develop probabilistic models of mental capacities and evaluate their adequacy based on behavioural and neural data generated by humans (or other cognitive agents) performing a pertinent task. The overarching goal is to identify the mathematical principles, algorithmic procedures, and causal mechanisms that enable cognitive (...) agents to take uncertainty into account and weigh it appropriately in producing adaptive behaviour. -/- The appeal of Bayesian cognitive science derives from its transparency, normativity, and unifying power. Resources from Bayesian statistics allow cognitive scientists to characterise transparently, in terms of random variables and probabilistic dependencies between them, the inductive problems that many mental capacities are presumed to solve. Given this characterisation, researchers can define an upper bound on one’s performance on that problem and use it as a benchmark for interpreting experimental results, formulating hypotheses about why and when deviations from optimal performance should be expected, and seeking explanations of mental capacities within one encompassing theoretical framework. -/- Although critics question the testability, explanatory power, and plausibility of Bayesian models of mental capacities, Bayesian cognitive science has proved itself to be an incredibly fruitful research programme. Fuelled by empirical and theoretical results from psychology, neuroscience, philosophy, computer science, artificial intelligence, and evolution, Bayesian cognitive science has: advanced our understanding of why cognitive systems should keep track of uncertainty and use it to facilitate adaptive behaviour; clarified how the brain might encode information about the uncertainty of its stimuli and perform computations with probability distributions; and crystallised the insight that cognitive agents’ management of uncertainty might be grounded in predictive processes aimed at avoiding surprising exchanges with the environment. (shrink)

Against modal rationalism, Manolo Martínez argues that elementary bodily mechanisms allow cognizers to know possibility. He presents an exemplary behavioral mechanism adapted to maximizing expected outcome in a random game. The bodily mechanism purportedly tracks probabilities and related possibilities. However, it is doubtful that cognizers like us can know metaphysical modalities purely by virtue of bodily mechanisms without using rational capacities. Firstly, Martínez’s mechanism is limited. But knowledge of probabilities arguably has to cover a variety of probabilistic outcomes. One (...) may need an ability to calculate probabilities. Bodily mechanisms can realize such an ability, but this will presumably amount to instantiating rational capacities. Secondly, the purported connection between the items tracked by the bodily mechanism and genuine metaphysical possibilities is tenuous. There are points at which it may fail. Further, we would need to know by rational metaphysical considerations that the connection holds in order to bodily know possibilities. (shrink)

All of us make judgments of probability, and we rely on them for our decision-making. This paper argues that such judgments are trustworthy only to the extent that one has good reasons to think that they are produced by maximally inclusive, well calibrated cognitive processes. A cognitive process is maximally inclusive when it takes into account all the evidence which one regards as relevant, and it is well calibrated when anything it would assign, say, an 80% probability to would be (...) true 80% of the time. We further have good reasons to think these judgments are trustworthy when, inter alia, they are produced by processes that have good track records of calibration. Call this inclusive calibrationism—or just “calibrationism” for short. In arguing for calibrationism, I also appeal to various empirical results, including research into probabilistic reasoning funded by the US intelligence community. Together, these ideas and results have implications for some important philosophical problems: the problem of the priors, the problem of unique events and the use of intuition in probabilistic reasoning. These theses and results also imply that our judgments are often less trustworthy than we might hope for potentially many domains, including law, medicine and others—barring good track records, that is. (shrink)

The book deals with the changing nature and with the history of the concept of scientific determinism from the classical mechanics until the time immediately preceding quantum mechanics: such a historical-philosophical reconstruction is aimed at (1) signalizing and overcoming the deficiencies of the received opinion on the topic and (2) understanding better a concept which has influenced science from the beginning. -/- Before dealing with historical matters I develop in the first Chapter a kind of new, three-dimensional “measurement system” for (...) analyzing any concept of scientific determinism: many different concepts have been developed in the course of history, and we need a classification system which, on the one hand, is inclusive and broad enough to deal with different concepts; on the other hand, which makes possible to differentiate with some precision between them. My “measurement system” has three dimension or parameter: the “strength” (of the determining link between different states of the physical system), the “depth” (depending on how strong the ontological commitment of the particular concept of scientific determinism is), and the breadth (referring to the domain or the object, to which a deterministic evolution is ascribed). -/- In the second chapter I discuss briefly some main shortcomings of the received opinion about scientific determinism. Then I try to identify the “core” of scientific determinism at its origins while at the same time embedding it in the broader historical-cultural context especially of the Renaissance. On the one hand I show how the core of scientific determinism was mathematical, and this distinguished it from other related concepts, as mechanism, the principle of sufficient reason, and the aristotelic conception of science. On the other hand, precisely the support which these related concepts provided to scientific determinism, together with its mathematical nature, endowed it with an incredible resistance against empirical classification. -/- In the following three chapters I analyze the historic-philosophical development of scientific determinism along the three parameter of my measurement system. -/- In the third Chapter I reconstruct the weakening of scientific determinism with respect to its depth: the starting point is the question about the extent in which the deterministic mathematical descriptions do refer. In the development of mechanics into analytical mechanics the more and more formal character of its mathematical descriptions became evident and it became less obvious that these formal, deterministic structures also have an immediate material truth. Parallel developments within epistemology, starting with Kantian philosophy (which I deal with quite in detail), let the deterministic structures (principle, laws, equations, causal relations) appear less absolute and real and more as a product of reason, as conventions, or as models. A brief consideration of the interpretation of mechanics and its principles by Jacobi, Hertz, Mach and Poincaré shows the implications of these developments. -/- The fourth chapter reconstructs the weakening of scientific determinism with respect to the strength of the determining link: here my work deepens the track which has been opened by Ian Hacking, who interpreted the so-called “probabilistic revolution” in the 19th Century as the main reason for the erosion of determinism. The increasing pervasiveness of statistical methods in the social and natural sciences in the course of the 19th Century gave rise to a conception of scientific laws which could dispense with strict determinism. A new, empirical interpretation of probability (Frequentism) and the statistical explanation of thermodynamic phenomena in physics even suggested chance phenomena to be a necessary condition for the emergence of natural laws. -/- The last part of my work (Ch. 5, 6 and 7) considers the period (2nd half of the 19th century until ca. 1920) in which the concept of scientific determinism became explicit and was discussed as a world-picture or a world-view – that is, in its maximal breadth. I argue that there were two main reasons for the emergence of an explicit and ideological opposition “determinism vs. indeterminism” at that time: the first was the successful application of the deterministic paradigm to sociology, history, physiology and psychology in the course of the 19th Century, which provided scientific determinism with ethical implications (in particular with respect to the problem of free will, which scientific determinism seemed to deny). The second, related reason is that in the 19th Century natural scientists became public men, science was increasingly popularized and scientific issues were increasingly related to life-issues, to worldview-questions, and even to politics. In Chapter 6 I reconstruct the debates on the issue “determinism vs. indeterminism” in such a public, ideological and sometimes even political context. Among the discussants were Fechner, Du Bois-Reymond, Helmholtz, Bernard, Ostwald, Haeckel, Boussinesq, Maxwell, Boutroux, Poincaré, Renouvier, James und Peirce. Also in Vienna the debate on the issue of determinism was fervid and took ideological and political connotations: Michael Stöltzner and Deborah Coen have pointed to a particular tradition of “Vienna Indeterminism” (Stöltzner), or Viennese liberal probabilism (Coen), which was characterized by a strongly empirical conception of science and by the full acceptance and appreciation of statistical thinking in science. In the last Chapter of my work I focus on the early philosophy of Edgar Zilsel, a philosopher who stood near to the Vienna Circle and who has been much neglected in the literature by now, and I suggest considering it as part of the “Vienna Indeterminism”. First, I show how he gave an indeterministic foundation both to statistical and causal knowledge as well as to irreversibility in physics. Second, I inquire into his philosophy of probability and show how it developed in parallel to the ones of Hans Reichenbach and Richard von Mises. Finally, Zilsel happens to be a further, relevant case-study for pointing to the ideological and political side of the issue of determinism. -/- At the end of this reconstruction it should have become clear how no physical theory, no empirical evidence and no experimental confirmation can support or contradict scientific determinism in its non-trivial formulations. The validity of any such formulation depends strongly not only (from the systematical point of view) from the definition of other concepts (like time, physical state, causality etc.), but also from the particular conception of science which more or less implicitly constitutes its background, and which is subjected to historical change. -/- (ISBN: 9783495491034). (shrink)

Statistical learning is often taken to lie at the heart of many cognitive tasks, including the acquisition of language. One particular task in which probabilistic models have achieved considerable success is the segmentation of speech into words. However, these models have mostly been tested against English data, and as a result little is known about how a statistical learning mechanism copes with input regularities that arise from the structural properties of different languages. This study focuses on statistical word segmentation (...) in Arabic, a Semitic language in which words are built around consonantal roots. We hypothesize that segmentation in such languages is facilitated by tracking consonant distributions independently from intervening vowels. Previous studies have shown that human learners can track consonant probabilities across intervening vowels in artificial languages, but it is unknown to what extent this ability would be beneficial in the segmentation of natural language. We assessed the performance of a Bayesian segmentation model on English and Arabic, comparing consonant-only representations with full representations. In addition, we examined to what extent structurally different proto-lexicons reflect adult language. The results suggest that for a child learning a Semitic language, separating consonants from vowels is beneficial for segmentation. These findings indicate that probabilistic models require appropriate linguistic representations in order to effectively meet the challenges of language acquisition. (shrink)

Soon after the first COVID-19 positive case was detected in Wuhan, China, the virus spread around the globe, and in no time, it was declared as a global pandemic by the WHO. Testing, which is the first step in identifying and diagnosing COVID-19, became the first need of the masses. Therefore, testing kits for COVID-19 were manufactured for efficiently detecting COVID-19. However, due to limited resources in the densely populated countries, testing capacity even after a year is still a limiting (...) factor for COVID-19 diagnosis on a larger scale and contributes to a lag in disease tracking and containment. Due to this reason, we started this study to provide a better cost-effective solution for enhancing the testing capacity so that the maximum number of people could get tested for COVID-19. For this purpose, we utilized the approach of artificial neural networks (ANN) to acquire the relevant data on COVID-19 and its testing. The data were analyzed by using Machine Learning, and probabilistic algorithms were applied to obtain a statistically proven solution for COVID-19 testing. The results obtained through ANN indicated that sample pooling is not only an effective way but also regarded as a “Gold standard” for testing samples when the prevalence of the disease is low in the population and the chances of getting a positive result are less. We further demonstrated through algorithms that pooling samples from 16 individuals is better than pooling samples of 8 individuals when there is a high likelihood of getting negative test results. These findings provide ground to the fact that if sample pooling will be employed on a larger scale, testing capacity will be considerably increased within limited available resources without compromising the test specificity. It will provide healthcare units and enterprises with solutions through scientifically proven algorithms, thus, saving a considerable amount of time and finances. This will eventually help in containing the spread of the pandemic in densely populated areas including vulnerably confined groups, such as nursing homes, hospitals, cruise ships, and military ships. (shrink)

Filler-gap dependency resolution is often characterized as an active process. We probed the mechanisms that determine where and why comprehenders posit gaps during incremental processing using Norwegian as our test language. First, we investigated why active filler-gap dependency resolution is suspended inside island domains like embedded questions in some languages. Processing-based accounts hold that resource limitations prevent gap-filling in embedded questions across languages, while grammar-based accounts predict that active gap-filling is only blocked in languages where embedded questions are grammatical islands. (...) In a self-paced reading study, we find that Norwegian participants exhibit filled-gap effects inside embedded questions, which are not islands in the language. The findings are consistent with grammar-based, but not processing, accounts. Second, we asked if active filler-gap processing can be understood as a special case of probabilistic ambiguity resolution within an expectation-based framework. To do so, we tested whether word-by-word surprisal values from a neural language model could predict the location and magnitude of filled-gap effects in our behavioral data. We find that surprisal accurately tracks the location of filled-gap effects but severely underestimates their magnitude. This suggests either that mechanisms above and beyond probabilistic ambiguity resolution are required to fully explain active gap-filling behavior or that surprisal values derived from long-short term memory are not good proxies for humans' incremental expectations during filler-gap resolution. (shrink)

Probability can be used to measure degree of belief in two ways: objectively and subjectively. The objective measure is a measure of the rational degree of belief in a proposition given a set of evidential propositions. The subjective measure is the measure of a particular subject’s dispositions to decide between options. In both measures, certainty is a degree of belief 1. I will show, however, that there can be cases where one belief is stronger than another yet both beliefs are (...) plausibly measurable as objectively and subjectively certain. In ordinary language, we can say that while both beliefs are certain, one belief is more certain than the other. I will then propose second, non probabilistic dimension of measurement, which tracks this variation in certainty in such cases where the probability is 1. A general principle of rationality is that one’s subjective degree of belief should match the rational degree of belief given the evidence available. In this paper I hope to show that it is also a rational principle that the maximum stake size at which one should remain certain should match the rational weight of certainty given the evidence available. Neither objective nor subjective measures of certainty conform to the axioms of probability, but instead are measured in utility. This has the consequence that, although it is often rational to be certain to some degree, there is no such thing as absolute certainty. (shrink)

We discuss two influential views of unification: mutual information unification (MIU) and common origin unification (COU). We propose a simple probabilistic measure for COU and compare it with Myrvold’s (2003, 2017) probabilistic measure for MIU. We then explore how well these two measures perform in simple causal settings. After highlighting several deficiencies, we propose causal constraints for both measures. A comparison with explanatory power shows that the causal version of COU is one step ahead in simple causal settings. (...) However, slightly increasing the complexity of the underlying causal structure shows that both measures can easily disagree with explanatory power. The upshot of this is that even sophisticated causally constrained measures for unification ultimately fail to track explanatory relevance. This shows that unification and explanation are not as closely related as many philosophers thought. (shrink)

This dissertation aims to determine the optimal level of granularity for the variables used in probabilistic causal models. These causal models are useful for generating explanations in a number of scientific contexts. In Chapter 1, I argue that there is rarely a unique level of granularity at which a given phenomenon can be causally explained, thereby rejecting various causal exclusion arguments. In Chapter 2, I consider several recent proposals for measuring the explanatory power of causal explanations, and show that (...) these measures fail to track the comparative depth of explanations given at different levels of granularity. In Chapter 3, I offer a pragmatic account of how to partition the measure space of a causal variable so as to optimally explain its effect. My account uses the decision-theoretic notion of value of information, and indexes the relative depth of an explanation to a particular agent faced with a particular decision problem. Chapter 4 applies this same decisiontheoretic framework to answer the epistemic question of how to discover constitutive relationships in nature. In Chapter 5, I describe the formal details of the relationship between random variables that are meant to be coarse-grained and fine-grained representations of the same type of phenomenon. I use this formal framework to rebut a popular argument for the view that special science probabilities can be objective chances. Chapter 6 discusses challenges related to the causal interpretation of Bayes nets that use imprecise rather than precise probabilities. (shrink)

Philosophers are drawn to the Atomic Theory like a dog to an old shoe, but my results about realism and anti-realism in Tracking Truth, and the distinctive position I have carved out on their basis, are independent of the fate of my comments about that historical case. I will defend those comments against Stanford’s objections below, but first I will explain the argument of my chapter, because its results undermine not only historically important antirealist positions, but also the approach (...) via unconceived conceivables that Stanford’s criticisms (and his pessimistic induction) depend on. The issue is claims about equal evidential support that empiricist defenses of epistemological anti-realism, including Stanford’s, must and do appeal to. In Chapter 6 of Tracking Truth, I show that the best known probabilistic definitions of evidential support imply the anti-realist’s key equalsupport claims are either false or inconsistent with the view he wants to base on them. There are reasons to think that it is not even possible to define a probabilistic notion of support that will do the job the anti-realist needs. It follows from this, as I explain below, that the anti-realist has not defended the claim that we are in principle, or even probably, unable to confirm high-level theories – for this, I say, we must wait and see, and make our best estimates. This, and my claim that we have actually gotten beyond observables – a point made with pregnancy tests and tests for medical conditions – make room for realism. I do not endorse the kind of realist claim that says we generally have a right to believe our successful hypotheses that go beyond observables. Some successful hypotheses are better confirmed than others, and some are not at all. However, since the anti-realist’s general skeptical arguments fail, I also do not think we need such general claims in order to have a right to confidence in those particular hypotheses for which we do have good evidence. I call my approach to the epistemological realism-anti-realism debate “evidential.” The nub of the issue between the two camps has always been whether we have a right to believe our scientific theories – in some sense that includes at least some of their claims about unobservable matters – on the basis of our evidence.1 Answers to this question depend on what our evidence actually is, and on the definition or criteria for evidential support.. (shrink)

The rapidly increasing role of automation throughout the economy, culture and our personal lives has generated a large literature on the risks of algorithmic decision-making, particularly in high-stakes legal settings. Algorithmic tools are charged with bias, shrouded in secrecy, and frequently difficult to interpret. However, these criticisms have tended to focus on particular implementations, specific predictive techniques, and the idiosyncrasies of the American legal-regulatory regime. They do not address the more fundamental unease about the prospect that we might one day (...) replace judges with algorithms, no matter how fair, transparent, and intelligible they become. The aim of this paper is to propose an account of the source of that unease, and to evaluate its plausibility. I trace foundational unease with algorithmic decision-making in the law to the powerful intuition that there is a basic moral and legal difference between showing that something is true of many people just like you and showing that it is true of you. Human judgment attends to the exception; automation insists on blindly applying the rule. I show how this intuitive thought is connected to both epistemological arguments about the value of statistical evidence, as well as to court-centered conceptions of the rule of law. Unease with algorithmic decision-making in the law thus draws on an intuitive principle that underpins a disparate range of views in legal philosophy. This suggests the principle is deeply ingrained. Nonetheless, I argue that the powerful intuition is not as decisive as it may seem, and indeed runs into significant epistemological and normative challenges. At an epistemological level, I show how concerns about statistical evidence's ability to track the truth can be resolved by adopting a probabilistic, rather than modal, conception of truth-tracking. At a normative level, commitment to highly individualized decision-making co-exists with equally ingrained and competing principles, such as consistent application of law. This suggests that the “rule of law” may not identify a discrete set of institutional arrangements, as proponents of a court-centric conception would have it, but rather a more loosely defined set of values that could potentially be operationalized in multiple ways, including through some level of algorithmic adjudication. Although the prospect of replacing judges with algorithms is indeed unsettling, it does not necessarily entail unreasonable verdicts or an attack on the rule of law. (shrink)

Mulliken proposed an Aufbauprinzip for the molecules on the basis of molecular spectroscopy while establishing, point by point, his concept of molecular orbit. It is the concept of electronic state which becomes the lever for his attribution of electronic configurations to a molecule. In 1932, the concept of orbit was transmuted into that of the molecular orbital to integrate the probabilistic approach of Born and to achieve quantitative accuracy. On the basis of the quantum works of Hund, Wigner, Lennard-Jones (...) and group theory, he suggested the fragment method to establish the characteristics of molecular orbital for polyatomic molecules. These developments make it possible to bring elements of thought on the relation between a molecular whole and its parts . An operational realism combined with the second law of thermodynamics can pave the way for interesting tracks in the mereological study of chemical systems. (shrink)

This article argues against the view that affirmative action is wrong because it involves assigning group rights. First, affirmative action does not have to proceed by assigning rights at all. Second, there are, in fact, legitimate “group rights” both legal and moral; there are collective rights—which are exercised by groups—and membership rights—which are rights people have in virtue of group membership. Third, there are continuing harms that people suffer as blacks and claims to remediation for these harms can fairly treat (...) the (social) property of being black as tracking the victims of those harms. Affirmative action motivated in this way aims to respond to individual wrongs; wrongs that individuals suffer, as it happens, in virtue of their membership in groups. Finally, the main right we have when we are being considered for jobs and places at colleges is that we be treated according to procedures that are morally defensible. Morally acceptable procedures sometimes take account of the fact that a person is a member of a certain social group. (shrink)