Choice Computing: Machine Learning and Systemic Economics for Choosing

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.