A unifying framework, based on Information Entropy and its maximization, to connect the phenomenology of evolutionary biology, community ecology, financial economics, and statistical physics, plus a forecasting method for important practical problems in these disciplines,
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Da: PBShop.store US, Wood Dale, IL, U.S.A.
HRD. Condizione: New. New Book. Shipped from UK. Established seller since 2000. Codice articolo CX-9780750339292
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Da: PBShop.store UK, Fairford, GLOS, Regno Unito
HRD. Condizione: New. New Book. Shipped from UK. Established seller since 2000. Codice articolo CX-9780750339292
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Da: Ria Christie Collections, Uxbridge, Regno Unito
Condizione: New. In English. Codice articolo ria9780750339292_new
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Da: Rarewaves.com USA, London, LONDO, Regno Unito
Hardback. Condizione: New. Codice articolo LU-9780750339292
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Da: California Books, Miami, FL, U.S.A.
Condizione: New. Codice articolo I-9780750339292
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Da: moluna, Greven, Germania
Condizione: New. KlappentextA unifying framework, based on Information Entropy and its maximization, to connect the phenomenology of evolutionary biology, community ecology, financial economics, and statistical physics, plus a forecasting method for impo. Codice articolo 598728105
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Da: Rarewaves.com UK, London, Regno Unito
Hardback. Condizione: New. Codice articolo LU-9780750339292
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Da: AHA-BUCH GmbH, Einbeck, Germania
Buch. Condizione: Neu. Neuware - This book aims at providing a unifying framework, based on Information Entropy and its maximization, to connect the phenomenology of evolutionary biology, community ecology, financial economics, and statistical physics. This more comprehensive view, besides providing further insight into problems, enables problem-solving strategies by applying proven methods in one discipline to formally similar problems in other areas. The book also proposes a forecasting method for important practical problems in these disciplines and is directed to researchers, students and practitioners working on modelling the dynamics of complex systems. The common thread is how the flux of information both controls and serves to predict the dynamics of complex systems. It is shown how maximizing the Shannon information entropy allows one to infer a central object controlling the dynamics of complex systems, such as ecosystems or markets. The resulting models, which are known as pairwise maximum-entropy models, can be used to infer interactions from data in a wide variety of systems. Here, two examples are analysed in detail. The first is an application to conservation ecology, namely the issue of providing early warning indicators of population crashes of species of trees in tropical forests. The second is about forecasting the market values of firms through evolutionary economics. An interesting lesson is that PME modelling often produces accurate predictions despite not incorporating explicit interaction mechanisms. Key features - Written to be suitable for a broad spectrum of readers and assumes little mathematical specialism. - Includes pedagogical features: Worked examples, case studies and summaries. - The interdisciplinary approach builds bridges between disciplines. - Oriented to solve practical problems. - Includes a combination of analytical derivations and numerical simulations with experiments. Codice articolo 9780750339292
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