Bertrand's random-chord paradox purports to illustrate the inconsistency of the principle of indifference when applied to problems in which the number of possible cases is infinite. This paper shows that Bertrand's original problem is vaguely posed, but demonstrates that clearly stated variations lead to different, but theoretically and empirically self-consistent solutions. The resolution of the paradox lies in appreciating how different geometric entities, represented by uniformly distributed random variables, give rise to respectively different nonuniform distributions of random chords, and hence to different probabilities. The principle of indifference appears consistently applicable to infinite sets provided that problems can be formulated unambiguously.

Contrary to the assumptions of empiricist philosophies of science, the theory-laden character of data will not imply the inherent failure (subjectivity, circularity, or rationalization) of instruments to expose nature's secrets. The success of instruments is credited to scientists' capacity to create artificial technological analogs to familiar physical systems. The design of absorption spectrometers illustrates the point: Progress in designing many modern instruments is generated by analogically projecting theoretical insights from known physical systems to unknown terrain. An experimental realism is defended.

The basic problem of perceptual change is how to account for both variation and constancy in perceiving the world. Is order learned? How deep does plasticity go in that respect? I argue that different kinds of perceptual plasticity have been confused in recent debates, notably between J. Fodor and P. M. Churchland. By focusing on changes in the use of concepts, the issues in the Fodor-Churchland debate can be resolved. Beyond that debate, I propose a generalized encoding approach to perception as a way of accounting for a significant form of perceptual change.

Individualists claim that wide explanations in psychology are problematic. I argue that wide psychological explanations sometimes have greater explanatory power than individualistic explanations. The aspects of explanatory power I focus on are causal depth and theoretical appropriateness. Reflection on the depth and appropriateness of other wide explanations of behavior, such as evolutionary explanations, clarifies why wide psychological explanations sometimes have more causal depth and theoretical appropriateness than narrow psychological explanations. I also argue for the rejection of eliminative materialism.

This paper takes a critical look at the idea that evolutionary theory is a statistical theory. It argues that despite the strong instrumental motivation for statistical theories, they are not necessary to explain deterministic systems. Biological evolution is fundamentally a result of deterministic processes. Hence, a statistical theory is not necessary for describing the evolutionary forces of genetic drift and natural selection, nor is it needed for describing the fitness of organisms. There is a computational advantage to the statistical theory of population genetics, but population genetics succeeds only by eliminating causes from its account of evolutionary change.

In Kvart (1991a), I discussed the analysis of causal relevance presented in A Theory of Counterfactuals (1986) (and first in 1975). I explained there in what respect the notion captured by the analysis of Kvart (1986) is a mere approximation to the requisite notion of causal relevance. In this paper I present another analysis of causal relevance, devoid of the shortcoming of its predecessor.

The present analysis of causal relevance is, again, grounded in a chancelike notion of objective probability. The correlative notion of causal independence is analyzed as holding in case there is a so-called causal impact series, which is defined here. The basic notions used for this analysis are the notion of a differentiator, which upsets an equiprobability case, and that of a blocker, which restores it.

The distinction between analog and digital representation is reexamined; it emerges that a more fundamental distinction is that between symbolic and analog simulation. Analog simulation is analyzed in terms of a (near) isomorphism of causal structures between a simulating and a simulated process. It is then argued that a core concept, naturalistic analog simulation, may play a role in a bottom-up theory of adaptive behavior which provides an alternative to representational analyses. The appendix discusses some formal conditions for naturalistic analog simulation.