What can reason do for us and what can't it do? This is the question examined by Herbert A. Simon, who received the 1978 Nobel Prize in Economic Sciences "for his pioneering work on decision-making processes in economic organizations." The ability to apply reason to the choice of actions is supposed to be one of the defining characteristics of our species. In the first two chapters, the author explores the nature and limits of human reason, comparing and evaluating the (...) major theoretical frameworks that have been erected to explain reasoning processes. He also discusses the interaction of thinking and emotion in the choice of our actions. In the third and final chapter, the author applies the theory of bounded rationality to social institutions and human behavior, and points out the problems created by limited attention span human inability to deal with more than one difficult problem at a time. He concludes that we must recognize the limitations on our capabilities for rational choice and pursue goals that, in their tentativeness and flexibility, are compatible with those limits. (shrink)

Cognitive science is, of course, not really a new discipline, but a recognition of a fundamental set of common concerns shared by the disciplines of psychology, computer science, linguistics, economics, epistemology, and the social sciences generally. All of these disciplines are concerned with information processing systems, and all of them are concerned with systems that are adaptive—that are what they are from being ground between the nether millstone of their physiology or hardware, as the case may be, and the upper (...) millstone of a complex environment in which they exist. Systems that are adaptive may equally well be described as “artificial,” for as environments change, they can be expected to change too, as though they were deliberately designed to fit those environments (as indeed they sometimes are).The task of empirical science is to discover and verify invariants in the phenomena under study. The artificiality of information processing systems creates a subtle problem in defining empirical invariants in such systems. For observed regularities are very likely invariant only within a limited range of variation in their environments, and any accurate statement of the laws of such systems must contain reference to their relativity to environmental features. It is a common experience in experimental psychology, for example, to discover that we are studying sociology—the effects of the past histories of our subjects—when we think we are studying physiology—the effects of properties of the human nervous system. Similarly, business cycle economists are only now becoming aware of the extent to which the parameters of the system they are studying are dependent on the experiences of a population with economic events over the previous generation.In artificial sciences, the descriptive and the normative are never far apart. Thus, in economics, the “principle of rationality” is sometimes asserted as a descriptive invariant, sometimes as advice to decision makers. Similarly, in psychology, the processes of adaptation (learning) have always been a central topic, at one time a topic that dominated the whole field of research. Linguistics, too, has suffered its confusions between descriptive and normative attitudes towards its subject. But we must avoid the error, in studying information processing systems, of thinking that the adaptive processes themselves must be invariant; and we must be prepared to face the complexities of regression in the possibility that they themselves may be subject to improvement and adaptation.It might have been necessary a decade ago to argue for the commonality of the information processes that are employed by such disparate systems as computers and human nervous systems. The evidence for that commonality is now overwhelming, and the remaining questions about the boundaries of cognitive science have more to do with whether there also exist nontrivial commonalities with information processing in genetic systems than with whether men and machines both think. (shrink)

The central concept of Carnap's probabilistic theory of induction is a triadic relation, c, the probability or degree of confirmation of the hypothesis, h, on evidence, e. The relation is a purely logical one. The value of c can be computed from a knowledge of h, of e, of the structure of the language, and of the inductive rule to be employed.

The purpose of this note is to examine a recent axiomatization of classical particle mechanics, and its relation to an alternative axiomatization I had earlier proposed. A comparison of the two proposals casts some interesting light on the problems of operationalism in classical celestial mechanics.1. Comparison of the Two Axiomatizations. The basic differences between the two proposals arise from the nature of the undefined terms. Both systems take the set of particles, time, and position as primitive notions. Both systems assume (...) that there exists a set of particles having continuous, twice-differentiable paths over some time interval. In addition, CPM takes mass and force as primitive notions, and assumes that with each particle there is associated a mass and a set of forces such that Newton's Second Law is satisfied. A system with these properties is called in CPM “a system of particle mechanics.” If, in addition, the set of forces in the system satisfies Newton's Third Law, the system is called in CPM “Newtonian.”. (shrink)

In Executive Order No. 10521, March 17, 1954, President Eisenhower stated: “…only a small fraction of the Federal Funds is being used to stimulate and support the vital basic research which makes possible our practical scientific progress. I believe strongly that this Nation must extend its support of research in basic science.”.

In a previous paper it was proposed that theories of empirical phenomena should satisfy conditions of finite and irrevocable testability. Roughly speaking, a theory is finitely testable if, for every set of observations that falsifies the theory, there exists a finite subset of observations that falsifies it. A theory is irrevocably testable if, for any finite set of observations that falsifies the theory, any superset of observations that includes that set also falsifies it—a theory falsified by observations cannot be resuscitated (...) by adding new observations to the original set. (shrink)

Professor Aubert's ?three?stage rocket? (Inquiry, Vol. 26 [1983], No. 1) has reached periodic orbit. His comments on my earlier reply to his critique of my election predictions paper simply repeat arguments I have already refuted. In this note, I limit myself largely to pointing out Professor Aubert's misconceptions of what my position actually is. I find no reasons for revising the views stated in my original election predictions paper, nor any reasons for thinking that paper violated norms of scientific method (...) that prevail in the natural sciences. (shrink)