Kurt Gödel criticizes Rudolf Carnap's conventionalism on the grounds that it relies on an empiricist admissibility condition, which, if applied, runs afoul of his second incompleteness theorem. Thomas Ricketts and Michael Friedman respond to Gödel's critique by denying that Carnap is committed to Gödel's admissibility criterion; in effect, they are denying that Carnap is committed to any empirical constraint in the application of his principle of tolerance. I argue in response that Carnap is indeed committed to an empirical requirement vis-à-vis tolerance, a fact that becomes clear upon closer scrutiny of Carnap's relevant writings.

When scientists are asked to give expert advice on risk-related questions, such as the authorization of medical drugs, deliberation often does not eliminate all disagreements. I propose to model these remaining discrepancies as differences in risk assessments and/or in risk acceptability thresholds. The normative question I consider, then, is how the individual expert views should best be aggregated. I discuss what “best” could mean, with an eye to some robustness considerations. I argue that the majority rule, which is currently often used in expert panels, has significant drawbacks.

Agreement between “independent” measurements of a theoretically posited quantity is intuitively compelling evidence that a theory is, loosely speaking, on the right track. But exactly what conclusion is warranted by such agreement? I propose a new account of the phenomenon’s epistemic significance within the framework of Bayesian epistemology. I contrast my proposal with the standard Bayesian treatment, which lumps the phenomenon under the heading of “evidential diversity.”

Professor Feigl's admirably lucid and concise appraisal of the major considerations which are still significant in the controversy between realism and phenomenalism includes the important reminder that the problem at hand should be viewed as concerning, not the truth or falsity of two conflicting hypotheses, but rather the comparative adequacy of two alternative proposals for the rational reconstruction of scientific knowledge. Feigl advocates the approach of semantic realism in preference to a phenomenalistic type of reconstruction on the grounds that in his critical examination, the latter has been “found wanting,” whereas semantic realism—and only semantic realism—can provide a satisfactory systematic account of the meaning and the experiential foundation of scientific hypotheses. While I fully agree with Feigl's general appraisal of the issues involved, I have doubts as to the superiority of the proposed realistic interpretation, and I should like to state these here in outline.

This is an interesting, well argued, and highly readable book; anyone interested in the central philosophical problems with which it deals will benefit from studying it.

Stalnaker defines inquiry as the process of forming, testing, and revising beliefs. His goal is to lay the groundwork for a theory of inquiry, by elaborating and defending a certain apparatus in terms of which the process of inquiry should be described.

Contemporary empiricism has attempted to ground its analysis of science in a falsificationism based in selection theory. This paper links these evolutionary epistemologies with commitments to certain epistemological and ontological assumptions found in the later work of K. Popper, D. Campbell, and D. Hull. I argue that their assumptions about the character of contemporary empiricism are part of a shared paradigm of epistemological explanation which results in unresolved tensions within their own projects. I argue further that their claim to be doing a science of science is not defensible. Hull's selectionism is analyzed to show how this epistemological agenda has played itself out in late empiricism. I suggest some directions that Hull might take toward an historical epistemology.

The contentions of this paper are essentially two. One is that truth does not consist of verifiability—and still less of verification—in the sense in which this has been maintained by some pragmatists, operationalists, and positivists. The other is that in a certain other sense of “verifiability”, which will be described, truth is the same thing as verifiability. The paper, it should be understood, attempts only to make clear what is and what is not the relation between truth and verifiability. It does not attempt in addition to carry the analysis of verification itself to the point where it would, by implication, furnish a terminal answer to the question, what is truth. The latter task, because of space limitations, must be reserved for some future occasion.

When an experimenter uses the experimental method to investigate the effects of the experimenter's expectancy it may be that this research, too, is affected by his expectancy and thus there is an expectancy paradox. To the extent that the experimenter expectancy effect accounts for the variation in the dependent variable and is general, that is to say, universal in psychological research, the expectancy paradox is ineluctable. However, an analysis of the research reviews extant in this area yields the conclusion that expectancy effects are neither inexorable nor highly general in psychological research and this provides the basis for its extrication from the expectancy paradox.

The axiom of comparability has been a fundamental part of mathematical choice theory from its beginnings. This axiom was a natural first assumption for a theory of choice originally constructed to explain decision making where other assumptions such as continuous divisibility of choice spaces could legitimately also be made. Once the generality of application of formal choice theory becomes apparent, it also becomes apparent that both continuity assumptions and the axiom of comparability may be unduly restrictive and lead to the neglect of decision situations which are important and which can be handled on a modified axiom set. These considerations bear on the philosophical analysis of the concept of rational decision.

Understanding the role mechanistic constraints play in shaping evolution can relieve the tension between the generally accepted intuition that there are no strict laws in biology and empirical findings showing that evolutionary processes are biased toward preferred outcomes. Mechanistic constraints explain why some evolutionary outcomes are more probable than others and allow for predictions in specific lineages. At the same time, mechanistic constraints are neither necessary nor universal in the way laws are traditionally characterized: they remain contingent on the past evolution of the biological mechanisms underpinning them and only constrain the future evolution of the organisms possessing them.

Although the introduction of the modern axiomatic method in physics is attributed to Hilbert, it is only recently that physicists and mathematicians have applied it significantly, i.e. on a basis extensive enough to promise fruitful results. Carnap, for one, stresses the importance of the axiomatic method, yet he considers its application in physics as a task for the future.

Biology deals, notoriously, with complex systems. In discussing biological methodology, all three papers in this symposium honor the complexity of biological subject matter by preferring models and theories built to reflect the details of complex systems to models based on broad general principles or laws. Rheinberger's paper, the most programmatic of the three, provides a framework for the epistemology of discovery in complex systems. A fundamental problem is raised for Rheinberger's epistemology, namely, how to understand the referential continuity of the theoretical terms and concepts employed in typical case studies involving complex systems.

Based on the historical case of galvanic experimentation in Germany, I identify five types of experimentation which explored and shaped the new phenomenon rather than tested theoretical predictions. Verification evaluated initial reports of Galvani's phenomenon. Simplification reduced the experimental protocol to the fewest and most basic steps. Optimization found experimental conditions that magnified the observed effect. Exploration tested a wide variety of metals, animals or configurations. Application modified the experiment to address unresolved related problems. Attempts to derive laws of the phenomenon or establish its underlying causes were unsuccessful primarily due to the great variability of the experimental results.

The Black-Scholes(-Merton) model of options pricing establishes a theoretical relationship between the “fair” price of an option and other parameters characterizing the option and prevailing market conditions. Here I discuss a common application of the model with the following striking feature: the (expected) output of analysis apparently contradicts one of the core assumptions of the model on which the analysis is based. I will present several attitudes one might take toward this situation and argue that it reveals ways in which a “broken” model can nonetheless provide useful (and tradeable) information.

The meaning of consciousness (i.e. the mind-brain problem), has interested thinkers throughout recorded time, and yet it is quite obvious that its understanding is still exceedingly remote. This is evident from the fact that even the presently used definitions give rise to contradictions. As implied by the title, the purpose of this paper is to remove some of the main difficulties concerned with this definition by using epistemological methods which have recently been developed. It is hoped that by clarifying the definition of consciousness, or, more accurately, by clarifying the process leading to a scientifically rigorous definition of consciousness, some of the paradoxes surrounding the concept might be removed.

When can a Bayesian investigator select an hypothesis H and design an experiment (or a sequence of experiments) to make certain that, given the experimental outcome(s), the posterior probability of H will be lower than its prior probability? We report an elementary result which establishes sufficient conditions under which this reasoning to a foregone conclusion cannot occur. Through an example, we discuss how this result extends to the perspective of an onlooker who agrees with the investigator about the statistical model for the data but who holds a different prior probability for the statistical parameters of that model. We consider, specifically, one-sided and two-sided statistical hypotheses involving i.i.d. Normal data with conjugate priors. In a concluding section, using an “improper” prior, we illustrate how the preceding results depend upon the assumption that probability is countably additive.