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This book discusses entropy and the Second Law of Thermodynamics in such a way that everyone can understand its subject matter. Entropy is one of the most interesting concepts in physics. Although it is a well-defined concept, it is still perceived by even well-known scientists as a concept cloaked in mystery. It is also the most misused, and often abused, concept in physics. In order to understand entropy, one needs to understand the Shannon measure of information, and in order to grasp this idea, one must be familiar with some basic concepts of probability. Therefore, this book consists of three chapters: the first discusses probability, the second addresses Information Theory, and the third considers entropy and the Second Law of Thermodynamics. Readers will discover that the Second Law is nothing but a law of probability.

Entropy. --- Entropy --- Thermodynamics --- Information theory --- Uncertainty (Information theory) --- Entropie --- Thermodynamique --- Information, Théorie de l' --- Incertitude (théorie de l'information) --- Study and teaching. --- Étude et enseignement

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In the literature of decision analysis it is traditional to rely on the tools provided by probability theory to deal with problems in which uncertainty plays a substantive role. In recent years, however, it has become increasingly clear that uncertainty is a mul­ tifaceted concept in which some of the important facets do not lend themselves to analysis by probability-based methods. One such facet is that of fuzzy imprecision, which is associated with the use of fuzzy predicates exemplified by small, large, fast, near, likely, etc. To be more specific, consider a proposition such as "It is very unlikely that the price of oil will decline sharply in the near future," in which the italicized words play the role of fuzzy predicates. The question is: How can one express the mean­ ing of this proposition through the use of probability-based methods? If this cannot be done effectively in a probabilistic framework, then how can one employ the information provided by the proposition in question to bear on a decision relating to an investment in a company engaged in exploration and marketing of oil? As another example, consider a collection of rules of the form "If X is Ai then Y is B,," j = 1, . . . , n, in which X and Yare real-valued variables and Ai and Bi are fuzzy numbers exemplified by small, large, not very small, close to 5, etc.

Probability theory --- Statistical decision --- Fuzzy sets --- Uncertainty (Information theory) --- Prise de décision (Statistique) --- Ensembles flous --- Incertitude (Théorie de l'information) --- Management --- Business & Economics --- Management Theory --- 519.226 --- Decision problems --- Game theory --- Operations research --- Statistics --- Management science --- Sets, Fuzzy --- Fuzzy mathematics --- Set theory --- Inference and decision theory. Likelihood. Bayesian theory. Fiducial probability --- Fuzzy sets. --- Statistical decision. --- 519.226 Inference and decision theory. Likelihood. Bayesian theory. Fiducial probability --- Prise de décision (Statistique) --- Incertitude (Théorie de l'information)

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This volume, like its predecessors, reflects the cutting edge of research on the automation of reasoning under uncertainty.A more pragmatic emphasis is evident, for although some papers address fundamental issues, the majority address practical issues. Topics include the relations between alternative formalisms (including possibilistic reasoning), Dempster-Shafer belief functions, non-monotonic reasoning, Bayesian and decision theoretic schemes, and new inference techniques for belief nets. New techniques are applied to important problems in medicine, vision, robotics, and natural language und

Artificial intelligence. --- Uncertainty (Information theory) --- Reasoning. --- Intelligence artificielle --- Incertitude (Théorie de l'information) --- Raisonnement --- Measure of uncertainty (Information theory) --- Shannon's measure of uncertainty --- System uncertainty --- Information measurement --- Probabilities --- Questions and answers --- AI (Artificial intelligence) --- Artificial thinking --- Electronic brains --- Intellectronics --- Intelligence, Artificial --- Intelligent machines --- Machine intelligence --- Thinking, Artificial --- Bionics --- Cognitive science --- Digital computer simulation --- Electronic data processing --- Logic machines --- Machine theory --- Self-organizing systems --- Simulation methods --- Fifth generation computers --- Neural computers

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"The acclaimed author of the influential bestseller The Black Swan, Nicholas Nassim Taleb takes a next big step with a deceptively simple concept: the "antifragile." Like the Greek hydra that grows two heads for each one it loses, people, systems, and institutions that are antifragile not only withstand shocks, they benefit from them. In a modern world dominated by chaos and uncertainty, Antifragile is a revolutionary vision from one of the most subversive and important thinkers of our time. Praise for Nicholas Nassim Taleb "[This] is the lesson of Taleb. and also the lesson of our volatile times. There is more courage and heroism in defying the human impulse, in taking the purposeful and painful steps to prepare for the unimaginable."--Malcolm Gladwell, author of The Tipping Point "[Taleb writes] in a style that owes as much to Stephen Colbert as it does to Michel de Montaigne."--The Wall Street Journal "The most prophetic voice of all. [Taleb is] a genuinely significant philosopher. someone who is able to change the way we view the structure of the world through the strength, originality and veracity of his ideas alone."--GQ "Changed my view of how the world works."--Daniel Kahneman, Nobel laureate"-- "Examples of Antifragility: When you stress your body by lifting a big weight, your body gets stronger. New York has the best restaurants in the world because particular restaurants are always going bust, making the aggregate stronger and stronger, or antifragile. Evolution is antifragile. Certain business and investment strategies are antifragile. Older things tend to be more antifragile than newer ones - because they've been exposed to more Black Swans"--

Civilisation --- Philosophy --- Uncertainty (Information theory) --- Forecasting. --- Complexity (Philosophy) --- Incertitude (Théorie de l'information) --- Prévision --- Complexité (Philosophie) --- Social aspects --- Aspect social --- AA / International- internationaal --- 10 --- 203 --- 305.6 --- 519.72 --- 659.113.5 --- Wijsbegeerte. --- Sociografie. Algemene beschrijving van de gemeenschappen (Sociologie). --- Risicotheorie, speltheorie. Risicokapitaal. Beslissingsmodellen. --- Information theory: mathematical aspects --- Forecasting, projections of results --- 659.113.5 Forecasting, projections of results --- 519.72 Information theory: mathematical aspects --- Incertitude (Théorie de l'information) --- Prévision --- Complexité (Philosophie) --- beleid, cultuur, gedrag, organisatie, maatschappelijk, strategie, verandering, wendbaarheid --- Forecasting --- Measure of uncertainty (Information theory) --- Shannon's measure of uncertainty --- System uncertainty --- Information measurement --- Probabilities --- Questions and answers --- Forecasts --- Futurology --- Prediction --- Emergence (Philosophy) --- Wijsbegeerte --- Sociografie. Algemene beschrijving van de gemeenschappen (Sociologie) --- Risicotheorie, speltheorie. Risicokapitaal. Beslissingsmodellen --- Wetenschap. --- Wetenschapsfilosofie. --- Antifragiel.

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Artificial intelligence. --- Uncertainty (Information theory) --- Expert systems (Computer science) --- Fuzzy sets. --- Mathematical models. --- Intelligence artificielle --- Incertitude (Théorie de l'information) --- Systèmes experts (Informatique) --- Ensembles flous --- Modèles mathématiques --- Artificial intelligence --- Fuzzy sets --- Mathematical models --- 681.3*I24 --- Measure of uncertainty (Information theory) --- Shannon's measure of uncertainty --- System uncertainty --- Information measurement --- Probabilities --- Questions and answers --- Models, Mathematical --- Simulation methods --- Sets, Fuzzy --- Fuzzy mathematics --- Set theory --- Knowledge-based systems (Computer science) --- Systems, Expert (Computer science) --- Computer systems --- Soft computing --- AI (Artificial intelligence) --- Artificial thinking --- Electronic brains --- Intellectronics --- Intelligence, Artificial --- Intelligent machines --- Machine intelligence --- Thinking, Artificial --- Bionics --- Cognitive science --- Digital computer simulation --- Electronic data processing --- Logic machines --- Machine theory --- Self-organizing systems --- Fifth generation computers --- Neural computers --- Knowledge representation formalisms and methods: frames and scripts; predicate logic; relation systems; representation languages; procedural and rule-based representations; semantic networks (Artificial intelligence) --- Expert systems (Computer science). --- Uncertainty (Information theory). --- 681.3*I24 Knowledge representation formalisms and methods: frames and scripts; predicate logic; relation systems; representation languages; procedural and rule-based representations; semantic networks (Artificial intelligence) --- Incertitude (Théorie de l'information) --- Systèmes experts (Informatique) --- Modèles mathématiques