Forecasting with Maximum Entropy : The interface between physics, biology, economics and information theory
Language: English
Published by IOP Publishing Ltd Nov 2022, 2022
- Hardcover
- New
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
AbeBooks seller since August 14, 2006
Condition: New
US$ 135.55
Quantity: 2 available
Add to basketItem description from seller
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.…
Seller Inventory # 9780750339292
- Title
- Forecasting with Maximum Entropy : The interface between physics, biology, economics and information theory
- Author
- Hugo Fort
- Publisher
- IOP Publishing Ltd Nov 2022
- Publication year
- 2022
- Condition
- Neu
- Binding
- Buch
- Language
- English
- ISBN 10
- 0750339292
- ISBN 13
- 9780750339292
- Item weight
- 603 grams
- Dimensions
- 254x178x14 mm
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
"Synopsis" may belong to another edition of this title.
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