This book taps the best American thinkers to answer the essential American question: How do we sustain our experiment in government of, by, and for the people? Authored by an extraordinary and politically diverse roster of public officials, scholars, and educators, these chapters describe our nation's civic education problem, assess its causes, offer an agenda for reform, and explain the high stakes at risk if we fail.

Bayesian nets are widely used in artificial intelligence as a calculus for causal reasoning, enabling machines to make predictions, perform diagnoses, take decisions and even to discover causal relationships. This book, aimed at researchers and graduate students in computer science, mathematics and philosophy, brings together two important research topics: how to automate reasoning in artificial intelligence, and the nature of causality and probability in philosophy.

While there are several arguments on either side, it is far from clear as to whether or not countable additivity is an acceptable axiom of subjective probability. I focus here on de Finetti's central argument against countable additivity and provide a new Dutch book proof of the principle, To argue that if we accept the Dutch book foundations of subjective probability, countable additivity is an unavoidable constraint.

William Alston’s argument against epistemological deontologism rests upon two key premises: first, that we lack a suitable amount of voluntary control with respect to our beliefs, and, second, the principle that “ought” implies “can.” While several responses to Alston have concerned rejecting either of these two premises, I argue that even on the assumption that both premises are true, there is room to be made for deontologism in epistemology. I begin by offering a criticism of Richard Feldman’s invaluable work on (...) ‘role-oughts’, whereupon I development my own positive view in light of Feldman’s shortcomings. The upshot is that while we as epistemic agents are not responsible for the beliefs we form, we are nonetheless responsible for the various bodily or mental activities that typically bear a causal influence on belief formation. (shrink)

Bayesian probability is normally defined over a fixed language or eventspace. But in practice language is susceptible to change, and thequestion naturally arises as to how Bayesian degrees of belief shouldchange as language changes. I argue here that this question poses aserious challenge to Bayesianism. The Bayesian may be able to meet thischallenge however, and I outline a practical method for changing degreesof belief over changes in finite propositional languages.

According to epistemic internalism, the only facts that determine the justificational status of a belief are facts about the subject’s own mental states, like beliefs and experiences. Externalists instead hold that certain external facts, such as facts about the world or the reliability of a belief-producing mechanism, affect a belief’s justificational status. Some internalists argue that considerations about evil demon victims and brains in vats provide excellent reason to reject externalism: because these subjects are placed in epistemically unfavorable settings, externalism (...) seems unable to account for the strong intuition that these subjects’ beliefs are nonetheless justified. I think these considerations do not at all help the internalist cause. I argue that by appealing to the anti-individualistic nature of perception, it can be shown that skeptical scenarios provide no reason to prefer internalism to externalism. (shrink)

The central questions raised by Allan Bloom's The Closing of theAmerican Mind are often overlooked. Among the most important ofBloom's themes is the impact of nihilism upon education. Bloom condemnsnihilism. Interestingly, we find among his critics two alternativejudgments. Richard Schacht, citing Nietzsche, asserts that nihilism,while fruitless in and of itself, is a necessary prerequisite tosomething higher. Harry Neumann, affirming the accuracy of nihilism,declares that both Bloom and Nietzsche reject nihilism out of ignoranceborn of weakness. All three philosophers understand that the (...) purpose ofeducation emerges from one's position on nihilism. If nihilism is true,then it is senseless and cowardly to teach one's students that there aregrounds for moral judgments. On the other hand, if one believes thatthere is an objective higher and lower in moral matters, then one cannotat the same time consistently endorse nihilism or the atheism upon whichit rests. There is reason to believe that a consistent nihilism isimpossible and hence that the concept is bankrupt. But then something istrue, and there are grounds for moral judgment. Education must respondaccordingly. But even Bloom with his emphasis on the Great Books fallsshort of what is required. An education which aims to defeat nihilismmust, at the very least, hold out the promise that through thecultivation of reason one may indeed arrive at the truth. (shrink)

Birch, Ginsburg, and Jablonka, in an article in this issue of Biology and Philosophy, provided a much-needed condensation of their well-reasoned theory of Unlimited Associative Learning. This theory compellingly identifies the conscious animals and the time when the evolutionary transition to consciousness was completed. The authors convincingly explained their use of UAL as a “transition marker,” identified two more features by which UAL can be recognized, showed how UAL’s learning features relate to consciousness, and how investigating consciousness is analogous to (...) investigating life. My constructive criticisms are that I wish they had taken a stand on which organisms and entities are not conscious, and I feel they made too many predictions by which to test the UAL theory. That is, some of the proposed tests are unnecessary because parts of UAL are already supported by enough evidence, and other proposed tests are so ambitious as to be nearly impossible to complete. Finally, UAL has a major, detailed, sub-hypothesis that says such learning led to stress and to the evolution of mechanisms to cope with this stress; but this idea is speculative and requires the most basic testing to judge whether it could be valid. (shrink)

When a proposition is established, it can be taken as evidence for other propositions. Can the Bayesian theory of rational belief and action provide an account of establishing? I argue that it can, but only if the Bayesian is willing to endorse objective constraints on both probabilities and utilities, and willing to deny that it is rationally permissible to defer wholesale to expert opinion. I develop a new account of deference that accommodates this latter requirement.

The orthodox view in statistics has it that frequentism and Bayesianism are diametrically opposed—two totally incompatible takes on the problem of statistical inference. This paper argues to the contrary that the two approaches are complementary and need to mesh if probabilistic reasoning is to be carried out correctly.

According to the bootstrapping problem, any view that allows for basic knowledge (knowledge obtained from a reliable source prior to one’s knowing that that source is reliable) is forced to accept that one can utilize a track-record argument to acquire justification for believing that one’s belief source is reliable; yet, we tend to think that acquiring justification in this way is too easy. In this paper I argue, first, that those who respond to the bootstrapping problem by denying basic knowledge (...) succumb to over-intellectualizing epistemology, and secondly, reliabilist views avoid over-intellectualization only at the expense of sanctioning bootstrapping as a benign procedure. Both of these outcomes are difficult to bear. To ward off each of these unsavory outcomes, I propose an alternative solution that draws on a distinction between two separate epistemic concepts: entitlement and justification. (shrink)

This paper uses a case study of a 1970s controversy in artificial-intelligence (AI) research to explore how scientists understand the relationships between research and practical applications. It is part of a project that seeks to map such relationships in order to enable better policy recommendations to be grounded empirically through historical evidence. In 1972 the mathematician James Lighthill submitted a report, published in 1973, on the state of artificial-intelligence research under way in the United Kingdom. The criticisms made in the (...) report have been held to be a major cause behind the dramatic slowing down (subsequently called an ‘AI winter’) of such research. This paper has two aims, one narrow and one broad. The narrow aim is to inquire into the causes, motivations and content of the Lighthill report. I argue that behind James Lighthill's criticisms of a central part of artificial intelligence was a principle he held throughout his career – that the best research was tightly coupled to practical problem solving. I also show that the Science Research Council provided a preliminary steer to the direction of this apparently independent report. The broader aim of the paper is to map some of the ways that scientists (and in Lighthill's case, a mathematician) have articulated and justified relationships between research and practical, real-world problems, an issue previously identified as central to historical analysis of modern science. The paper therefore offers some deepened historical case studies of the processes identified in Agar's ‘working-worlds’ model. (shrink)

Tome I: Afrikaans to Dutch -- Tome II: English -- Tome III: Estonian to Hebrew -- Tome IV: Hungarian to Korean -- Tome V: Latvian to Ukrainian -- Tome VI: Figures A-H -- Tome VII: Figures I-Z.

According to the Disjunction Problem, teleological theories of perceptual content are unable to explain why it is that a subject represents an F when an F causes the perception and not the disjunction F v G, given that the subject has mistaken G’s for F’s in the past. Without an adequate explanation these theories are stuck without an account of how non-veridical representation is possible, which would be an unsettling result. In this paper I defend Burge’s teleological theory of perception (...) against the Disjunction Problem, arguing that a perceptual state’s representing what I call an error-prohibiting disjunctive property is incompatible with the truth of perceptual anti-individualism. And because perceptual anti-individualism is at the heart of Burge’s theory, I conclude that Burgeans need not be concerned with the Disjunction Problem. (shrink)

Objective Bayesian probability is often defined over rather simple domains, e.g., finite event spaces or propositional languages. This paper investigates the extension of objective Bayesianism to first-order logical languages. It is argued that the objective Bayesian should choose a probability function, from all those that satisfy constraints imposed by background knowledge, that is closest to a particular frequency-induced probability function which generalises the λ = 0 function of Carnap’s continuum of inductive methods.

This paper develops connections between objective Bayesian epistemology—which holds that the strengths of an agent’s beliefs should be representable by probabilities, should be calibrated with evidence of empirical probability, and should otherwise be equivocal—and probabilistic logic. After introducing objective Bayesian epistemology over propositional languages, the formalism is extended to handle predicate languages. A rather general probabilistic logic is formulated and then given a natural semantics in terms of objective Bayesian epistemology. The machinery of objective Bayesian nets and objective credal nets (...) is introduced and this machinery is applied to provide a calculus for probabilistic logic that meshes with the objective Bayesian semantics. (shrink)

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