Choice Computing: Machine Learning and Systemic Economics for Choosing

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Focusing on the integration of behaviorism into machine learning, the book explores innovative pathways for developing models that prioritize human choice. Divided into three parts, it begins by examining human decision-making and choice architecture, followed by a detailed analysis of human choosing models to inform machine learning development. The final section addresses machine learning-based choice architecture. By adopting this choice-based paradigm, readers can create solutions that enhance human experience and adaptability, fostering significant value for businesses and society.