Choice Computing: Machine Learning and Systemic Economics for Choosing / / by Parag Kulkarni

• Record Nr.: UNINA9910590053703321
• Autore: Kulkarni Parag
• Pubbl/distr/stampa: Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2022
• ISBN: 9789811940590; 9789811940583
• Edizione: [1st ed. 2022.]
• Descrizione fisica: 1 online resource (254 pages)
• Collana: Intelligent Systems Reference Library, , 1868-4408 ; ; 225
• Disciplina: 006.31
• Soggetti: Computational intelligence; Machine learning; Computer science - Mathematics; Computational Intelligence; Machine Learning; Mathematics of Computing
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Note generali: Includes index.
• Nota di contenuto: Introduction -- Decoding Choosing -- ML of Choosing: Architecting Intelligent Choice Framework -- Machine Learning of Choice Economics -- Co-operative Choosing: Machines and Humans Thinking Together to Choose the Right Way -- Choice Architecture – Machine Learning Framework -- Artificial Consciousness and Choosing (Towards Conscious Choice Machines) -- Choice Computing and Creativity -- Experimental Choice Computing and Choice Learning Through Real-Life Stories -- Beyond Choice Computing.
• Sommario/riassunto: This book presents thoughts and pathways to build revolutionary machine learning models with the new paradigm of machine learning to adapt behaviorism. It focuses on two aspects – one focuses on architecting a choice process to lead users on the certain choice path while the second focuses on developing machine learning models based on choice paradigm. This book is divided in three parts where part one deals with human choice and choice architecting models with stories of choice architects. Second part closely studies human choosing models and deliberates on developing machine learning ; models based on the human choice paradigm. Third part takes you further to look at machine learning based choice architecture. The proposed pioneering choice-based paradigm for machine learning presented in the book will help readers to develop products – help readers to solve problems in a more humanish way and to negotiate with uncertainty in a more graceful but in an objective way. It will help to create unprecedented value for business and society. Further, it will unveil a new paradigm for modern intelligent businesses to embark on the new journey; the journey of transition from shackled feature rich and choice poor systems to feature flexible and choice rich natural behaviors.