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)

It is often claimed that there can be no such thing as a logic of scientific discovery, but only a logic of verification. By 'logic of discovery' is usually meant a normative theory of discovery processes. The claim that such a normative theory is impossible is shown to be incorrect; and two examples are provided of domains where formal processes of varying efficacy for discovering lawfulness can be constructed and compared. The analysis shows how one can treat operationally and formally (...) phenomena that have usually been dismissed with fuzzy labels like 'intuition' and 'creativity'. (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)

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)

Moses Maimonides, scholar, physician, and philosopher, was the most influential Jewish thinker of the Middle Ages. In this magisterial new biography, the work of many years, Herbert Davidson provides an exhaustive guide to Maimonides' life and works. After considering Maimonides' upbringing and education, Davidson expounds all of his voluminous writings in exhaustive detail, with separate chapters on rabbinic, philosophical, and medical texts. This long-awaited volume is destined to become the standard work on this towering figure of Western intellectual history.

The task of axiomatizing physical theories has attracted, in recent years, some interest among both empirical scientists and logicians. However, the axiomatizations produced by either one of these two groups seldom appear satisfactory to the members of the other. It is the purpose of this paper to develop an approach that will satisfy the criteria of both, hence permit us to construct axiomatizations that will meet simultaneously the standards and needs of logicians and of empirical scientists.

First published in 1889. This re-issues the second, revised edition of 1926. Chuang Tzu was to Lao Tzu, the author of Tao Tê Ching, as Hui-neng, the sixth Patriarch of Zen Buddhism, was to Bodhidharma, and in some respects St.Paul to Jesus; he expanded the original teaching into a system and was thus the founder of Tao-ism. Whereas Lao Tzu was a contemporary of Confucius in the sixth century B.C, Chuang Tzu lived over two hundred years later. He was one (...) of the greatest minds produced by China; philosopher, metaphysician, moralist and poet. It is impossible to understand the spiritual depth of the Tao Tê Ching without the aid of Chuang Tzu. (shrink)

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)

It is shown how a causal ordering can be defined in a complete structure, and how it is equivalent to identifying the mechanisms of a system. Several techniques are shown that may be useful in actually accomplishing such identification. Finally, it is shown how this explication of causal ordering can be used to analyse causal counterfactual conditionals. First the counterfactual proposition at issue is articulated through the device of a belief-contravening supposition. Then the causal ordering is used to provide modal (...) categories for the factual propositions, and the logical contradiction in the system is resolved by ordering the factual propositions according to these causal categories. (shrink)

Fourteen subjects were tape‐recorded while they undertook to find a law to summarize numerical data they were given. The source of the data was not identified, nor were the variables labeled semantically. Unknown to the subjects, the data were measurements of the distances of the planets from the sun and the periods of their revolutions about it—equivalent to the data used by Johannes Kepler to discover his third law of planetary motion.Four of the 14 subjects discovered the same law as (...) Kepler did (the period varies as the 3/2 power of the distance), and a fifth came very close to the answer. The subiects' protocols provide a detailed picture of the problem‐solving searches they engaged in, which were mainly, but not exclusively, in the space of possible functions for fitting the data, and provide explanations as to why some succeeded and the others failed.The search heuristics used by the subjects are similar to those embodied in the BACON program, computer simulation of certain scientific discovery processes. The experiment demonstrates the feasibility of examining some of the processes of scientific discovery by recreating, in the laboratory, discovery situations of substantial historical relevance. It demonstrates also, that under conditions rather similar to those of the original discoverer, a law can be rediscovered by persons of ordinary intelligence (i.e., the intelligence needed for academic success in a good university). The data for the successful subjects reveal no “creative” processes in this kind of a discovery situation different from those that are regularly observed in all kinds of problem‐solving settings. (shrink)

Contrary to Aubert's claim, my paper on election predictions does not seek to draw empirical conclusions from mathematical premisses alone. The empirical premiss, approximated by the continuity assumption, is that sufficiently small changes in the predicted vote will cause only small changes in the actual vote. The technical criticisms by ?fsti and ?sterberg of the reaction function are answered by specifying the function's domain. Other criticisms are also answered, and the reply concludes by placing the election prediction theorem in the (...) context of other theorizing about human expectations and outguessing phenomena. (shrink)