Choice Computing: Machine Learning and Systemic Economics for Choosing

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Focusing on the intersection of human behavior and machine learning, this book explores revolutionary models that adapt behaviorism principles. It is structured into three parts: the first examines human choice and architects, the second analyzes human decision-making to inform machine learning models, and the third investigates choice-based architecture in machine learning. By introducing a choice-based paradigm, the book aims to empower readers to develop innovative products that address problems in a more human-centric manner, ultimately enhancing value for businesses and society.