Should Probabilistic Design Replace Safety Factors? Content Type Journal Article Pages 151-168 DOI 10.1007/s13347-010-0003-6 Authors Neelke Doorn, Department of Technology, Policy and Management, Delft University of Technology, PO Box 5015, 2600 GA Delft, The Netherlands Sven Ove Hansson, Department of Philosophy and the History of Technology, Royal Institute of Technology, Teknikringen 78 B, 100 44 Stockholm, Sweden Journal Philosophy & Technology Online ISSN 2210-5441 Print ISSN 2210-5433 Journal Volume Volume 24 Journal Issue Volume 24, Number 2.

Safety is a concern in almost all branches of engineering. Whereas safety was traditionally introduced by applying safety factors or margins to the calculated maximum load, this approach is increasingly replaced with probabilistic risk assessment (PRA) as a tool for dimensioning safety measures. In this paper, the two approaches are compared in terms of what they aim at and what they can, in fact, achieve. The outcome of this comparison suggests that the two approaches should be seen as complementary (...) rather than mutually exclusive. PRA is particularly useful for priority setting and for the effect evaluation of safety measures; however, in most applications, uncertainties prevent PRA from providing an objective probability of failure or value of damage. Safety factors are indispensible for dealing with dangers that cannot be assigned meaningful probabilities. (shrink)

Are special relativity and probabilism compatible? Dieks argues that they are. But the possible universe he specifies, designed to exemplify both probabilism and special relativity, either incorporates a universal "now" (and is thus incompatible with special relativity), or amounts to a many world universe (which I have discussed, and rejected as too ad hoc to be taken seriously), or fails to have any one definite overall Minkowskian-type space-time structure (and thus differs drastically from special relativity as ordinarily understood). Probabilism and (...) special relativity appear to be incompatible after all. What is at issue is not whether "the flow of time" can be reconciled with special relativity, but rather whether explicitly probabilistic versions of quantum theory should be rejected because of incompatibility with special relativity. (shrink)

“Probabilistic Causation” designates a group of theories that aim to characterize the relationship between cause and effect using the tools of probability theory. The central idea behind these theories is that causes change the probabilities of their effects. This article traces developments in probabilistic causation, including recent developments in causal modeling. A variety of issues within, and objections to, probabilistic theories of causation will also be discussed.

Are probabilism and special relativity compatible? Dieks argues that they are. But the possible universe he specifies, designed to exemplify both probabilism and special relativity, either incorporates a universal “now”, or amounts to a many world universe, or fails to have any one definite overall Minkowskian-type space-time structure. Probabilism and special relativity appear to be incompatible after all. What is at issue is not whether “the flow of time” can be reconciled with special relativity, but rather whether explicitly probabilistic (...) versions of quantum theory should be rejected because of incompatibility with special relativity. (shrink)

Sarah Moss argues that degrees of belief, or credences, can amount to knowledge in much the way that full beliefs can. This essay explores a new kind of objective Bayesianism designed to take us some way toward securing such knowledge-constituting credences, or "probabilistic knowledge." Whatever else it takes for an agent's credences to amount to knowledge, their success, or accuracy, must be the product of _cognitive ability_ or _skill_. The brand of Bayesianism developed here helps ensure this ability condition (...) is satisfied. Cognitive ability, in turn, helps make credences valuable in other ways: it helps mitigate their dependence on epistemic luck, for example. What we end up with, at the end of the day, are credences that are particularly good candidates for constituting probabilistic knowledge. What's more, examining the character of these credences teaches us something important about what the pursuit of probabilistic knowledge demands from us. It does _not_ demand that we give hypotheses equal _treatment_, by affording them equal credence. Rather, it demands that we give them equal _consideration_, by affording them an equal chance of being discovered. (shrink)

Are probabilism and special relativity compatible? Dieks argues that they are. But the possible universe he specifies, designed to exemplify both probabilism and special relativity, either incorporates a universal “now”, or amounts to a many world universe, or fails to have any one definite overall Minkowskian-type space-time structure. Probabilism and special relativity appear to be incompatible after all. What is at issue is not whether “the flow of time” can be reconciled with special relativity, but rather whether explicitly probabilistic (...) versions of quantum theory should be rejected because of incompatibility with special relativity. (shrink)

A recent development in the philosophy of religion has been the attempt to justify belief in God using Bayesian confirmation theory. My dissertation critically discusses two prominent spokesmen for this approach--Richard Swinburne and J. L. Mackie. Using probabilistic confirmation theory, these philosophers come to wildly divergent conclusions with respect to the hypothesis of theism; Swinburne contends that the evidence raises the overall probability of the hypothesis of theism, whereas Mackie argues that the evidence disconfirms the existence of God. After (...) a careful analysis of the individual authors, this dissertation critically examines the assumptions of the project. The authors consider arguments for the existence of God separately--the cosmological argument, the argument from design, apparent miracles, the problem of evil, and arguments from religious experience--and then conjoin them to assess the cumulative effect of the arguments. The assumptions are that the probability calculus may be interpreted for a successful inductive argument for or against the existence of God, that inference to best explanation is applicable to the justification of religious belief, that the justification of belief in God is analogous to the justification of a scientific theory, and that a classical foundationalist conception of rationality is correct. It is the contention of this dissertation that all of these assumptions are incorrect. All of the major interpretations of the probability calculus are examined and it is argued that there are either no interpretations for the probability assignments or no interpretations which will advance an argument for or against the existence of God. It is also argued that the usual methods for interpreting the explanatory power of theism will not determine any non-zero probabilities; however, density measuring will provide a non-zero interpretation. In the final chapter it is argued that a subjective Bayesian conception of rationality is neither a necessary nor a sufficient condition of rationality and that classical foundationalism is self-referentially inconsistent. An alternative approach to the rationality of religious belief is offered that proceeds along Reidian and Plantingan lines, belief in God is properly basic. (shrink)

This paper examines Patrick Suppes’ probabilistic theory of causality understood as a theory of causal inference, and draws some lessons for empirical economics and contemporary debates in the foundations of econometrics. It argues that a standard method of empirical economics, multiple regression, is inadequate for most but the simplest applications, that the Bayes’ nets approach, which can be understood as a generalisation of Suppes’ theory, constitutes a considerable improvement but is still subject to important limitations, and that the currently (...) fashionable ‘design-based approach’ suffers from the same flaws Suppes anticipated a long time ago. It then sketches an alternative in response, one that differs drastically from the formalisms Suppes endorsed but is consistent with his pragmatic general take on science. (shrink)

Larry Wright and others have advanced causal accounts of functional explanation, designed to alleviate fears about the legitimacy of such explanations. These analyses take functional explanations to describe second order causal relations. These second order relations are conceptually puzzling. I present an account of second order causation from within the framework of Eells' probabilistic theory of causation; the account makes use of the population-relativity of causation that is built into this theory.

In their article 'Causes and Explanations: A Structural-Model Approach. Part I: Causes', Joseph Halpern and Judea Pearl draw upon structural equation models to develop an attractive analysis of 'actual cause'. Their analysis is designed for the case of deterministic causation. I show that their account can be naturally extended to provide an elegant treatment of probabilistic causation.

It is argued in this paper that although much attention has been paid to causal chains and common causes within the literature on probabilistic causality, a primary virtue of that approach is its ability to deal with cases of multiple causation. In doing so some ways are indicated in which contemporary sine qua non analyses of causation are too narrow (and ways in which probabilistic causality is not) and an argument by Reichenbach designed to provide a basis for (...) the asymmetry of causation is refined. The importance of referring causal claims to an abstract model is also emphasized. (shrink)

ABSTRACT Joseph Halpern and Judea Pearl draw upon structural equation models to develop an attractive analysis of ‘actual cause’. Their analysis is designed for the case of deterministic causation. I show that their account can be naturally extended to provide an elegant treatment of probabilistic causation. 1Introduction 2Preemption 3Structural Equation Models 4The Halpern and Pearl Definition of ‘Actual Cause’ 5Preemption Again 6The Probabilistic Case 7Probabilistic Causal Models 8A Proposed Probabilistic Extension of Halpern and Pearl’s Definition 9Twardy and (...) Korb’s Account 10Probabilistic Fizzling 11Conclusion. (shrink)

Key Terms in Logic offers the ideal introduction to this core area in the study of philosophy, providing detailed summaries of the important concepts in the study of logic and the application of logic to the rest of philosophy. A brief introduction provides context and background, while the following chapters offer detailed definitions of key terms and concepts, introductions to the work of key thinkers and lists of key texts. Designed specifically to meet the needs of students and assuming no (...) prior knowledge of the subject, this is the ideal reference tool for those coming to Logic for the first time. (shrink)

Key Terms in Logic offers the ideal introduction to this core area in the study of philosophy, providing detailed summaries of the important concepts in the study of logic and the application of logic to the rest of philosophy. A brief introduction provides context and background, while the following chapters offer detailed definitions of key terms and concepts, introductions to the work of key thinkers and lists of key texts. Designed specifically to meet the needs of students and assuming no (...) prior knowledge of the subject, this is the ideal reference tool for those coming to Logic for the first time. (shrink)

Key Terms in Logic offers the ideal introduction to this core area in the study of philosophy, providing detailed summaries of the important concepts in the study of logic and the application of logic to the rest of philosophy. A brief introduction provides context and background, while the following chapters offer detailed definitions of key terms and concepts, introductions to the work of key thinkers and lists of key texts. Designed specifically to meet the needs of students and assuming no (...) prior knowledge of the subject, this is the ideal reference tool for those coming to Logic for the first time. (shrink)

This paper defends two theses about probabilistic reasoning. First, although modus ponens has a probabilistic analog, modus tollens does not – the fact that a hypothesis says that an observation is very improbable does not entail that the hypothesis is improbable. Second, the evidence relation is essentially comparative; with respect to hypotheses that confer probabilities on observation statements but do not entail them, an observation O may favor one hypothesis H1 over another hypothesis H2 , but O cannot (...) be said to confirm or disconfirm H1 without such relativization. These points have serious consequences for the Intelligent Design movement. Even if evolutionary theory entailed that various complex adaptations are very improbable, that would neither disconfirm the theory nor support the hypothesis of intelligent design. For either of these conclusions to follow, an additional question must be answered: With respect to the adaptive features that evolutionary theory allegedly says are very improbable, what is their probability of arising if they were produced by intelligent design? This crucial question has not been addressed by the ID movement. (shrink)

Key Terms in Logic offers the ideal introduction to this core area in the study of philosophy, providing detailed summaries of the important concepts in the study of logic and the application of logic to the rest of philosophy. A brief introduction provides context and background, while the following chapters offer detailed definitions of key terms and concepts, introductions to the work of key thinkers and lists of key texts. Designed specifically to meet the needs of students and assuming no (...) prior knowledge of the subject, this is the ideal reference tool for those coming to Logic for the first time. (shrink)

Combining genetic algorithms that produce complex, fluid, biomorphic shapes with probabilistic systems that incorporate randomness, the designers attempt to mimic adaptive systems in natural evolution in order to arrive at intelligent design solutions. The design processes are said to be interactive and sensitive to varying conditions, behaving like an exceptionally perceptive and adaptive organism during an evolutionary process (Somol 2004: 8687); this process can be compared to the recent attempt by the architectural avant-garde to move beyond the (...) semiotic interests of deconstructionism and tap into the authority of the natural sciences. In some sense, an analogy can be drawn to natural evolution. In the natural world, adaptation and change result from an iterative progress, from one generation to the next, driven by mutation, recombination, and the survival of the fittest in the sense of selection. Following the model of nature in architecture, as proposed by some proponents of parametricism, should not be expected to make buildings more precisely adapted to particular circumstances and optimally functional. Evolutionary theory is used in contemporary architecture as a form of legitimization. It can also cast light on ethical issues, not reducing the human mind's creative intelligence to some sort of genetic determinism, developing in the free market of social choice. Therefore, searching for better ways of managing resources should be at the core of an adaptive concept of architecture. (shrink)

While speakers have been shown to adapt to the knowledge state of their addressee in choosing referring expressions, they often also show some egocentric tendencies. The current paper aims to provide an explanation for this “mixed” behavior by presenting a model that derives such patterns from the probabilistic combination of both the speaker's and the addressee's perspectives. To test our model, we conducted a language production experiment, in which participants had to refer to objects in a context that also (...) included a visually misleading object whose function was either known to both partners, or known just to the speaker but not the addressee. Modeling results indicate that the experimental findings cannot be explained by assuming that speakers tailor a referring expression solely to their own perspective or to the perspective of their addressee. Instead, accounting for the behavioral pattern requires an approach where both perspectives influence the choice of referring expressions. Nevertheless, in our situation, speakers consider their partner's perspective more than their own. (shrink)

We introduce a set of biologically and computationally motivated design choices for modeling the learning of language, or of other types of sequential, hierarchically structured experience and behavior, and describe an implemented system that conforms to these choices and is capable of unsupervised learning from raw natural-language corpora. Given a stream of linguistic input, our model incrementally learns a grammar that captures its statistical patterns, which can then be used to parse or generate new data. The grammar constructed in (...) this manner takes the form of a directed weighted graph, whose nodes are recursively (hierarchically) defined patterns over the elements of the input stream. We evaluated the model in seventeen experiments, grouped into five studies, which examined, respectively, (a) the generative ability of grammar learned from a corpus of natural language, (b) the characteristics of the learned representation, (c) sequence segmentation and chunking, (d) artificial grammar learning, and (e) certain types of structure dependence. The model's performance largely vindicates our design choices, suggesting that progress in modeling language acquisition can be made on a broad front—ranging from issues of generativity to the replication of human experimental findings—by bringing biological and computational considerations, as well as lessons from prior efforts, to bear on the modeling approach. (shrink)

We introduce a set of biologically and computationally motivated design choices for modeling the learning of language, or of other types of sequential, hierarchically structured experience and behavior, and describe an implemented system that conforms to these choices and is capable of unsupervised learning from raw natural-language corpora. Given a stream of linguistic input, our model incrementally learns a grammar that captures its statistical patterns, which can then be used to parse or generate new data. The grammar constructed in (...) this manner takes the form of a directed weighted graph, whose nodes are recursively (hierarchically) defined patterns over the elements of the input stream. We evaluated the model in seventeen experiments, grouped into five studies, which examined, respectively, (a) the generative ability of grammar learned from a corpus of natural language, (b) the characteristics of the learned representation, (c) sequence segmentation and chunking, (d) artificial grammar learning, and (e) certain types of structure dependence. The model's performance largely vindicates our design choices, suggesting that progress in modeling language acquisition can be made on a broad front—ranging from issues of generativity to the replication of human experimental findings—by bringing biological and computational considerations, as well as lessons from prior efforts, to bear on the modeling approach. (shrink)

We propose a new approach for tracing value change. Value change may lead to a mismatch between current value priorities in society and the values for which technologies were designed in the past, such as energy technologies based on fossil fuels, which were developed when sustainability was not considered a very important value. Better anticipating value change is essential to avoid a lack of social acceptance and moral acceptability of technologies. While value change can be studied historically and qualitatively, we (...) propose a more quantitative approach that uses large text corpora. It uses probabilistic topic models, which allow us to trace values that are latent. We demonstrate the approach for five types of value change in technology. Our approach is useful for testing hypotheses about value change, such as verifying whether value change has occurred and identifying patterns of value change. The approach can be used to trace value change for various technologies and text corpora, including scientific articles, newspaper articles, and policy documents. (shrink)

The argument from design stands as one of the most intuitively compelling arguments for the existence of a divine Creator. Yet, for many scientists and philosophers, Hume's critique and Darwin's theory of natural selection have definitely undermined the idea that we can draw any analogy from design in artifacts to design in nature. Here, we examine empirical studies from developmental and experimental psychology to investigate the cognitive basis of the design argument. From this it becomes clear (...) that humans spontaneously discern purpose in nature. When constructed theologically and philosophically correctly, the design argument is not presented as conclusive evidence for God's existence but rather as an abductive, probabilistic argument. We examine the cognitive basis of probabilistic judgments in relationship to natural theology. Placing emphasis on how people assess improbable events, we clarify the intuitive appeal of Paley's watch analogy. We conclude that the reason why some scientists find the design argument compelling and others do not lies not in any intrinsic differences in assessing design in nature but rather in the prior probability they place on complexity being produced by chance events or by a Creator. This difference provides atheists and theists with a rational basis for disagreement. (shrink)

This paper examines probabilistic versions of the fine-tuning argument for design (FTA), with an emphasis on the interpretation of the probability statements involved in such arguments. Three categories of probability are considered: physical, epistemic, and logical. Of the three possibilities, I argue that only logical probability could possibly support a cogent probabilistic FTA. However, within that framework, the premises of the argument require a level of justification that has not been met, and, it is reasonable to believe, (...) will not be met anytime soon. (shrink)

I I set out my view that all inference is essentially deductive and pinpoint what I take to be the major shortcomings of the induction rule.II The import of data depends on the probability model of the experiment, a dependence ignored by the induction rule. Inductivists admit background knowledge must be taken into account but never spell out how this is to be done. As I see it, that is the problem of induction.III The induction rule, far from providing a (...) method of discovery, does not even serve to detect pattern. Knowing that there is uniformity in the universe is no help to discovering laws. A critique of Reichenbach's justification of the straight rule is constructed along these lines.IV The induction rule, by itself, cannot account for the varying rates at which confidence in an hypothesis mounts with data. The mathematical analysis of this salient feature of inductive reasoning requires prior probabilities. We also argue, against orthodox statisticians, that prior probabilities make a substantive contribution to the objectivity of inductive methods, viz. to the design of experiments and the selection of decision rules.V Carnap's general criticisms of various estimation rules, like the straight rule and the ‘impervious rule’, are seen to be misguided when the prior densities to which they correspond are taken into account.VI Analysis of Hempel's definition of confirmation qua formalization of the enumerative (naive) conception of instancehood. We show that from the standpoint of the quantitative measure P(H/E):P(H) for the degree to which E confirms H, Hempel's classificatory concept yields correct results only for sampling at large from a finite population with a two-way classification all of whose compositions are equally probable. We extend the analysis to Goodman's paradox, finding cases in which grue-like hypotheses do receive as much confirmation as their opposite numbers. We argue, moreover, the irrelevancy of entrenchment, and maintain that Goodman's paradox is no more than a straightforward counter-example to the enumerative conception of instancehood embodied in Hempel's definition.VII We rebutt the objection that prior probabilities, qua inputs of Bayesian analysis, can only be obtained by enumerative induction (insofar as they are objective). The divergence in the prior densities of two rational agents is less a function of subjectivity, we maintain, than of vagueness.VIII Our concluding remarks stress that, for Bayesians, there is no problem of induction in the usual sense. (shrink)

If intelligent design (id) is to compete with evolutionary theory (et), it must meet the modified falsifiability challenge, that is, make some deductive or probabilistic observational assertions. It must also meet the modified translatability challenge, which it fails if et makes all the observational assertions of id, while id does not make all the observational assertions of et. I discuss four prominent but diverse formulations of id and show that each either fails one of the two challenges or (...) is analytically false. (shrink)

Due to the logical relations between theism and intelligent design (id), there are two challenges to theism that also apply to id. In the falsifiability challenge, it is charged that theism is compatible with every observation statement and thus asserts nothing. I argue that the contentious assumptions of this challenge can be avoided without loss of precision by charging theism (and thus id) directly with the lack of observational assertions. In the translatability challenge, it is charged that theism can (...) be translated into a (non-theistic) set of observation statements without loss of cognitive content. I argue that the contentious assumptions of this challenge are avoided by the related charge that the (non-theistic) evolutionary theory makes all the observational assertions of id, while the converse does not hold. Elliott Sober has argued that id meets the falsifiability challenge, but, since it makes almost no observational assertions, is not testable. I point out two problems with Sober’s argument and show that id is both deductively and probabilistically testable. Sober’s argument, I suggest, inconsistently combines the modified falsifiability challenge with the modified translatability challenge. If his claims about id’s observational assertions are true, however, id succumbs to the modified translatability challenge. (shrink)

The design argument for the existence of God took a probabilistic turn in the 17 th and 18 th centuries. Earlier versions, such as Thomas Aquinas' 5 th way, usually embraced the premise that goal-directed systems (things that "act for an end" or have a function) must have been created by an intelligent designer. This idea – which we might express by the slogan "no design without a designer" – survived into the 17 th and 18 th (...) centuries, 1 and it is with us still in the writings of many creationists. The new version of the argument, inspired by the emerging mathematical theory of probability, removed the premise of necessity. It begins with the thought that goal-directed systems might have arisen by intelligent design or by chance; the problem is to discern which hypothesis is more plausible. With the epistemic concept of plausibility characterized in terms of the mathematical concept of probability, the design argument was given a new direction. The new probabilistic perspective did not extinguish the older idea of "no design without a designer." The two conflicting approaches coexisted, often with less than perfect clarity about their fundamental difference. At the same time, the details of how the probabilistic perspective ought to be articulated were slow in emerging. These characteristics of uneven development are exemplified in a debate that took place in the 18 th century among three probabilists – John Arbuthnot, Nicholas Bernoulli, and Abraham DeMoivre – on the proper explanation of human sex ratio. Arbuthnot proposed a probabilistic version of the design argument, claiming that it is intelligent design, not chance, that provides the better explanation of why slightly more boys than girls are born each year. Bernoulli rejected Arbuthnot's argument. DeMoivre defended Arbuthnot against Bernoulli's criticisms. The problem of explaining sex ratio, both in human populations and in the rest of nature, experienced another transformation after 1859 --it became a problem for the theory of evolution by natural selection. Darwin (1871) addressed the question in the first edition of The Descent of Man but withdrew his suggestion in the second (Darwin 1874). Carl Düsing (1884) provided a mathematical model that moved beyond the explanation Darwin offered. Fisher (1930) added the new idea of parental expenditure. Williams (1966) argued that the sex ratios found in nature provide the opportunity to test hypotheses of group selection against hypotheses of individual selection. Hamilton (1967) constructed a model that generalizes Fisher's approach and represents the effects of both group and individual selection. The theory of natural selection gradually developed the ability to predict sex ratios, not just explain them post hoc. (shrink)

The study of cultural evolution has taken on an increasingly interdisciplinary and diverse approach in explicating phenomena of cultural transmission and adoptions. Inspired by this computational movement, this study uses Bayesian networks analysis, combining both the frequentist and the Hamiltonian Markov chain Monte Carlo (MCMC) approach, to investigate the highly representative elements in the cultural evolution of a Vietnamese city’s architecture in the early 20th century. With a focus on the façade design of 68 old houses in Hanoi’s Old (...) Quarter (based on 78 data lines extracted from 248 photos), the study argues that it is plausible to look at the aesthetics, architecture, and designs of the house façade to find traces of cultural evolution in Vietnam, which went through more than six decades of French colonization and centuries of sociocultural influence from China. The in-depth technical analysis, though refuting the presumed model on the probabilistic dependency among the variables, yields several results, the most notable of which is the strong influence of Buddhism over the decorations of the house façade. Particularly, in the top 5 networks with the best Bayesian Information Criterion (BIC) scores and p<0.05, the variable for decorations (DC) always has a direct probabilistic dependency on the variable B for Buddhism. The paper then checks the robustness of these models using Hamiltonian MCMC method and find the posterior distributions of the models’ coefficients all satisfy the technical requirement. Finally, this study suggests integrating Bayesian statistics in the social sciences in general and for the study of cultural evolution and architectural transformation in particular. (shrink)

In this paper, I want to draw attention to a connection between rigid designation with its consequence that we are able to stipulate worlds and haecceitism, the doctrine that we have possible worlds alike in all qualitative features which nonetheless are metaphysically different, in that two individuals can have all their qualitative features swapped while remaining the same individuals. I shall argue that stipulation leads to haecceitism, which in turn depends upon commitment to haecceity ("primitive thisness"). Haecceitism is, I claim, (...) an unattractive doctrine, but there is one powerful argument for it, drawn from the desire to make sense of observed frequencies of probabilistic events in terms of possible configurations of phase spaces which may be taken to be a form of possible world. However, by paying close attention to classical and quantum statistics, we see that this argument fails. (shrink)

Probabilistic Relational Models (PRMs) are a framework for compactly representing uncertainties (actually probabilities). They result from the combination of Bayesian Networks (BNs), Object-Oriented languages, and relational models. They are specifically designed for their efficient construction, maintenance and exploitation for very large scale problems, where BNs are known to perform poorly. Actually, in large-scale problems, it is often the case that BNs result from the combination of patterns (small BN fragments) repeated many times. PRMs exploit this feature by defining these (...) patterns only once (the so-called PRM’s classes) and using them through multiple instances, as prescribed by the Object-Oriented paradigm. This design induces low construction and maintenance costs. In addition, by exploiting the classes’ structures, PRM’s state-of-the-art inference algorithm “Structured Variable Elimination” (SVE) significantly outperforms BN’s classical inference algorithms (e.g., Variable Elimination, VE; Local Conditioning, LC). SVE is actually an extension of VE that simply exploits classes to avoid redundant computations. In this article, we show that SVE can be enhanced using LC. Although LC is often thought as being outperformed by VE-like algorithms in BNs, we do think that it should play an important role for PRMs because its features are very well suited for best exploiting PRM classes. In this article, relying on Faÿ and Jaffray’s works, we show how LC can be used in conjunction with VE and deduce an extension of SVE that outperforms it for large-scale problems. Numerical experiments highlight the practical efficiency of our algorithm. (shrink)

Humans excel in categorization. Yet from a computational standpoint, learning a novel probabilistic classification task involves severe computational challenges. The present paper investigates one way to address these challenges: assuming class-conditional independence of features. This feature independence assumption simplifies the inference problem, allows for informed inferences about novel feature combinations, and performs robustly across different statistical environments. We designed a new Bayesian classification learning model that incorporates varying degrees of prior belief in class-conditional independence, learns whether or not independence (...) holds, and adapts its behavior accordingly. Theoretical results from two simulation studies demonstrate that classification behavior can appear to start simple, yet adapt effectively to unexpected task structures. Two experiments—designed using optimal experimental design principles—were conducted with human learners. Classification decisions of the majority of participants were best accounted for by a version of the model with very high initial prior belief in class-conditional independence, before adapting to the true environmental structure. Class-conditional independence may be a strong and useful default assumption in category learning tasks. (shrink)

1.1 The Concept of Evil The problem of evil, in the sense relevant here, concerns the question of the reasonableness of believing in the existence of a deity with certain characteristics. In most discussions, the deity is God, understood as an omnipotent, omniscient, and morally perfect person. But the problem of evil also arises, as Hume saw very clearly, for deities that are less than all-powerful, less than all-knowing, and less than morally perfect. What is the relevant concept of evil, (...) in this context? Here it is useful to distinguish between axiological concepts and deontological ones. First, there are judgments about whether certain states of affairs make the world better or worse, whether those states are good or bad, desirable or undesirable. Such normative concepts – of good and bad, or desirable and undesirable, states of affairs – are axiological concepts. Secondly, there are judgments about the rightness and wrongness of actions, about what one morally ought or ought not do, of what one’s duty is, of whether a certain action violates someone rights, and these concepts – of the rightness and wrongness of actions, of duties, of the rights of individuals, of what one should or should not do – are deontological concepts. Given this distinction, should the problem of evil be understood axiologically or deontologically? Often, and I think most commonly in this context, evils are equated with states of affairs that, all things considered, are bad or undesirable: they are states of affairs that make the world a worse place. But one can also interpret the problem of evil in a deontological fashion, equating evils with states of affairs that a person should have prevented, if he could have done so. Elsewhere (2008, 105-6), I have argued that the deontological interpretation is preferable. But for present purposes, either interpretation will be fine. 1.2 Evil and the Reasonableness of Belief in the Existence of God That many things in the world – tsunamis, earthquakes, the suffering of non-human animals and innocent children, human actions such as the carrying out of the Holocaust – certainly appear to be evil is not a controversial matter. But whether such things are really evils, all things considered, is disputed. Assume, however, that the world does continue genuine evils, and not merely apparent ones. How, then does the existence of such evils bear upon the reasonableness of belief in the existence of God? First, the existence of evils can be used to show that certain arguments for the existence of God cannot establish that conclusion, or even render it probable. For some arguments, of course, one need not appeal to evil to make that point. If someone argues for the existence of a first cause, or an unmoved mover, or a necessary being having its necessity of itself, and then claims that this is what everyone understands by the term “God”, one can point out that no reason has been offered for thinking that the entity in question is even a person, let alone omnipotent, omniscient, and perfectly good. By contrast, with the type of argument that Hume was concerned with when he discussed the problem of evil – namely, arguments from the existence of order in the world to the existence of a designer, or intelligent cause of that order – one does have a type of argument that, if sound, shows that there is a person who, even if not omnipotent and omniscient, is at least extremely powerful and very knowledgeable. But such arguments do not support for conclusions about the moral character of such a person, and so it is at this point that the existence of evils becomes crucial. For in providing a reason for holding that the one is not justified in attributing even moral goodness to such a being, let alone moral perfection, it supports the conclusion that that argument in question cannot be a successful argument for the existence of God. The existence of evils, then, can serve to block arguments for the existence of God. But a second, and very familiar possibility, is that the existence of evils can also provide the basis of arguments against the existence of God. A very important distinction here is between deductive, or logical incompatibility, versions of the argument from evil, and inductive, or evidential, or probabilistic versions of the argument. According to the former, there are facts about the existence of evils in the world that are logically incompatible with the existence of God. Thus, some have held that the mere existence of any evil whatsoever is incompatible with the existence of God; others, that that certain types of evil – such as natural evils – are incompatible with the existence of God; and others, that it is the amount of evil in the world, or the fact that some people suffer horrendous evils, that logically precludes the existence of God. Inductive (or evidential, or probabilistic) versions of the argument from evil, by contrast, do not claim that there are facts about the evils in the world that are logically incompatible with the existence of God. The claim is rather that there are facts about the evils found in the world that render the existence of God at the very least unlikely, and perhaps extremely so. 1.3 Hume’s Use of the Existence of Evils The preceding discussion suggests two very different ways in which Hume might be employing the existence of evils. First, he might be appealing to the existence of evils simply to undercut certain arguments for the existence of God. In particular, he might be appealing to evils to show that, whatever the merits of the argument from order to design may be, it can nether establish, nor render probable, the existence of a morally good deity. Secondly, Hume might be advancing a version of the argument from evil, and that in turn could take the form either of an argument that claims that facts about evil are logically incompatible with the existence of God, or of an argument that claims, more modestly, that the apparent evils found in the world render the existence of God unlikely. (shrink)

Probabilistic predictions of future risk govern much of society: healthcare, genetics, social media, national security, and finance. Both policy-makers and private companies are increasingly working to design institutional structures that seek to manage risk by controlling the behavior of citizens and consumers, using new technologies of predictive control that comb through past data to predict and shape future action. These predictions not only control social institutions but also shape individual character and forms of practical reason. Risk-based decision theory (...) shifts people's relationships to the future through knowledge of possible dangers and foregone opportunities, thus inspiring anxious solicitude and deceptive hopes for total security. This book uses virtue ethics to analyze these problems and to suggest ways to enjoy these technologies' positive benefits while constraining their dangerous aspects through a better understanding of practical reasoning and a theological analysis of our responsibility for future risks. (shrink)

The study of deductive reasoning has been a major paradigm in psychology for approximately the past 40 years. Research has shown that people make many logical errors on such tasks and are strongly influenced by problem content and context. It is argued that this paradigm was developed in a context of logicist thinking that is now outmoded. Few reasoning researchers still believe that logic is an appropriate normative system for most human reasoning, let alone a model for describing the process (...) of human reasoning, and many use the paradigm principally to study pragmatic and probabilistic processes. It is suggested that the methods used for studying reasoning be reviewed, especially the instructional context, which necessarily defines pragmatic influences as biases. (shrink)

This paper motivates and develops a novel semantic framework for deontic modals. The framework is designed to shed light on two things: the relationship between deontic modals and substantive theories of practical rationality and the interaction of deontic modals with conditionals, epistemic modals and probability operators. I argue that, in order to model inferential connections between deontic modals and probability operators, we need more structure than is provided by classical intensional theories. In particular, we need probabilistic structure that interacts (...) directly with the compositional semantics of deontic modals. However, I reject theories that provide this probabilistic structure by claiming that the semantics of deontic modals is linked to the Bayesian notion of expectation. I offer a probabilistic premise semantics that explains all the data that create trouble for the rival theories. (shrink)

Several authors believe that there are certain facts that are striking and cry out for explanation—for instance, a coin that is tossed many times and lands in the alternating sequence HTHTHTHTHTHT…. According to this view, we have prima facie reason to believe that such facts are not the result of chance. I call this view the striking principle. Based on this principle, some have argued for far-reaching conclusions, such as that our universe was created by intelligent design, that there (...) are many universes other than the one we inhabit, and that there are no mathematical or normative facts. Appealing as the view may initially seem, I argue that we lack sufficient reason to accept it. (shrink)

"Adaptation" has several meanings which have often been confused, including relations, processes, states, and intrinsic properties. It is used in comparative and historical contexts. "Adaptation" and "environment" may designate probabilistic concepts. Recognition of these points refutes arguments for the notions that: 1) all organisms are perfectly adapted; 2) organisms cannot be ill-adapted and survive or well-adapted and die; 3) adaptation is necessarily relative to the environment; 4) change in environment is necessary for evolution; 5) preadaptation implies teleology. Such notions (...) are associated with metaphysical ideas, and may affect the thinking of biologists. (shrink)

How were reliable predictions made before Pascal and Fermat's discovery of the mathematics of probability in 1654? What methods in law, science, commerce, philosophy, and logic helped us to get at the truth in cases where certainty was not attainable? The book examines how judges, witch inquisitors, and juries evaluated evidence; how scientists weighed reasons for and against scientific theories; and how merchants counted shipwrecks to determine insurance rates. Also included are the problem of induction before Hume, design arguments (...) for the existence of God, and theories on how to evaluate scientific and historical hypotheses. It is explained how Pascal and Fermat's work on chance arose out of legal thought on aleatory contracts. The book interprets pre-Pascalian unquantified probability in a generally objective Bayesian or logical probabilist sense. (shrink)

The popular impression of Bohmian mechanics is that it is standard quantum mechanics with the addition of some extra gadgets---exact particle positions and a guiding equation for particle trajectories---the advantages being that the gadgets pave the way for a resolution of the measurement problem that eschews state vector reduction while restoring the determinism lost in standard quantum mechanics. In fact, the Bohmian mechanics departs in significant ways from standard quantum mechanics. By itself this is not a basis for criticism; indeed, (...) it makes Bohmian mechanics all the more interesting. But Bohmian mechanics is not, as the popular impression would have it, empirically equivalent to standard quantum mechanics in terms of probabilistic predictions for the outcomes of measurements of quantum observables. Indeed, in physically important applications to systems for which standard quantum mechanics delivers empirically well-confirmed probabilistic predictions, the sophisticated form of Bohmian mechanics designed to prove the global existence of Bohmian particle trajectories fails to deliver unequivocal predictions---of even a probabilistic variety---for the future behavior of said systems. Possible responses to this lacuna are discussed. (shrink)