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The book offers an introduction to the technical foundation of decentralized insurance models, for advanced undergraduate students, graduate students and practitioners. The book is self-contained and anyone with a basic knowledge of probability and statistics should be able to follow through the entire book. It adopts a minimalist approach to describe the essential elements and first principles so that readers can get a gist of these models without being overwhelmed with too much technicality. It can be used as a reference for business model designs. The inclusion of exercises and practical examples makes the book suitable for advanced courses on decentralized insurance and risk sharing. There is a mix of industry practices and academic models presented in this book. The exposition starts with an overview of historic and current business practices and preliminaries on the mathematics and economics of risk and insurance. A bird's-eye view of traditional insurance is provided as a benchmark for various topics to be used in contrast with decentralized insurance. The book then continues with decentralized insurance practices around the world, including online mutual aid originated in China, takaful from the Islamic world, peer-to-peer insurance in the West, catastrophe risk pooling for Caribbean countries, etc. Theories of aggregate risk pooling and peer-to-peer risk exchanges are provided for readers to appreciate the mathematical foundation of risk sharing. A unified framework of decentralized insurance is presented to show a structured approach to the economic design of decentralized business models. The book ends with a technical review of blockchain and decentralized finance (DeFi) insurance applications.

Statistical science --- Mathematical statistics --- Probability theory --- waarschijnlijkheidstheorie --- statistiek --- Social sciences—Mathematics. --- Probabilities. --- Statistics. --- Mathematics in Business, Economics and Finance. --- Applied Probability. --- Applied Statistics. --- Assegurances --- Estadística matemàtica --- Models matemàtics --- Finance --- Business & Economics --- Insurance --- Statistical methods. --- Mathematical models.

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This book introduces fuzzy set theory to social science researchers. Fuzzy sets are categories with blurred boundaries. With classical sets, objects are either in the set or not, but objects can belong partially to more than one fuzzy set at a time. Many concepts in the social sciences have this characteristic, and fuzzy set theory provides methods for systematically dealing with them. A primary reason for not going beyond programmatic statements and rather unsophisticated uses of fuzzy set theory has been the lack of practical methods for combining fuzzy set concepts with statistical methods. This monograph takes that topic as its major focus, and provides explicit guides for researchers who would like to harness fuzzy set concepts while being able to make statistical inferences and test their models. Real examples and data-sets from several disciplines illustrate the techniques and applications, demonstrating how a combination of fuzzy sets and statistics enable researchers to analyze their data in new ways.

Mathematical logic --- MATHEMATICS --- Set Theory --- Social sciences --- Fuzzy sets --- Social Sciences - General --- Social Sciences --- Mathematics --- Fuzzy sets. --- Mathematics. --- #SBIB:303H10 --- kwantitatieve methoden --- maatschappijwetenschappen, methoden --- operations research --- Methoden en technieken: algemene handboeken en reeksen --- Sciences sociales --- Ensembles flous --- Mathématiques --- Sets, Fuzzy --- Fuzzy mathematics --- Set theory --- Social sciences - Mathematics

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Operational research. Game theory --- Consensus (Social sciences) --- Social choice --- Social policy --- Game theory --- Combinatorial analysis --- Mathematics --- Analyse combinatoire --- Mathématiques économiques --- Théorie des jeux --- Mathématiques économiques --- Théorie des jeux --- Social choice. --- Consensus (Social sciences) - Mathematics --- Social choice - Mathematics --- Social policy - Mathematics

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Social sciences --- Game theory --- Mathematics --- Congresses --- 305.6 --- AA / International- internationaal --- NBB congres --- 519.83 --- 519.83 Theory of games --- Theory of games --- Behavioral sciences --- Human sciences --- Sciences, Social --- Social science --- Social studies --- Civilization --- Mathematics&delete& --- Risicotheorie, speltheorie. Risicokapitaal. Beslissingsmodellen --- Théorie des jeux --- Sciences sociales --- Mathématiques. --- Social sciences - Mathematics - Congresses --- Game theory - Congresses --- Théorie des jeux --- Mathématiques.

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This open access book discusses the statistical modeling of insurance problems, a process which comprises data collection, data analysis and statistical model building to forecast insured events that may happen in the future. It presents the mathematical foundations behind these fundamental statistical concepts and how they can be applied in daily actuarial practice. Statistical modeling has a wide range of applications, and, depending on the application, the theoretical aspects may be weighted differently: here the main focus is on prediction rather than explanation. Starting with a presentation of state-of-the-art actuarial models, such as generalized linear models, the book then dives into modern machine learning tools such as neural networks and text recognition to improve predictive modeling with complex features. Providing practitioners with detailed guidance on how to apply machine learning methods to real-world data sets, and how to interpret the results without losing sight of the mathematical assumptions on which these methods are based, the book can serve as a modern basis for an actuarial education syllabus.

Actuarial science. --- Statistics. --- Machine learning. --- Artificial intelligence—Data processing. --- Social sciences—Mathematics. --- Actuarial Mathematics. --- Statistics in Business, Management, Economics, Finance, Insurance. --- Machine Learning. --- Data Science. --- Mathematics in Business, Economics and Finance. --- Learning, Machine --- Artificial intelligence --- Machine theory --- Statistical analysis --- Statistical data --- Statistical methods --- Statistical science --- Mathematics --- Econometrics --- Statistics --- Insurance --- Deep Learning --- Actuarial Modeling --- Pricing and Claims Reserving --- Artificial Neural Networks --- Regression Modeling --- Assegurances --- Estadística