"I regard it as a truly seminal work in this field." — Professor William A. Wallace, author of Causality and Scientific Explanation. Non-technical and clearly written, this book focuses on the place of the casual principle in modern science. The author defines the terminology, describes various formulations, examines the two primary critiques of causality, and more.

Introduction: Science and Common Sense Long before the beginnings of modern civilization, men ac- quired vast funds of information about their environment. ...

Described by the philosopher A.J. Ayer as a work of 'great originality and power', this book revolutionized contemporary thinking on science and knowledge. Ideas such as the now legendary doctrine of 'falsificationism' electrified the scientific community, influencing even working scientists, as well as post-war philosophy. This astonishing work ranks alongside The Open Society and Its Enemies as one of Popper's most enduring books and contains insights and arguments that demand to be read to this day.

Recent discussions in the philosophy of science have devoted considerable attention to the analysis of conceptual issues relating to the methodology of explanation and prediction in the sciences. Part of this literature has been devoted to clarifying the very ideas of explanation and prediction. But the discussion has also ranged over various related topics, including the status of laws to be used for explanatory and predictive purposes, the logical interrelationships between explanatory and predictive reasonings, the differences in the strategy of (...) explanatory argumentation in different branches of science, the nature and possibility of teleological explanation, etc. The aim of the present article is to examine the issues involved in such questions from the specialized perspective afforded by one particular kind of physical systems--namely, systems, here to be characterized as discrete state systems, whose behavior has been studied extensively in the scientific literature under the general heading of Markov chains. These systems have been chosen as our focus because their behavior over time can be analyzed at once with great ease and with extraordinary precision. (shrink)

In the present paper some formal aspects of the hypothesis-directed stage of medical diagnosis are studied and an algorithm of the diagnostic problem solving process is described. A given field of medical knowledge is represented by a pair of graphs. The sentences describing observed symptoms and signs constitute the data on which the algorithm is based. In the first step, the set of true judgments is determined and the hypotheses which are impossible in a given situation are rejected. In consequence (...) the model of knowledge is modified and working hypotheses are chosen. Further steps consist in selection of hypotheses in view of testing. The set of expected symptoms is determined and these are classified according to their diagnostic value. The process ends when the conditions of arriving at a useful solution of a given problem are fulfilled. The description of the algorithm is based on a clinical example. The model aims at reflecting some of the most important structural features of the diagnosis and does not embrace the probability evaluation problems. (shrink)