Mostly Harmless – Uncertainty, Algorithms, and Decision-Making
The book attempts to bridge the gap between algorithmic processes and practical decision-making in uncertain circumstances. It moves beyond mathematical solutions to acknowledge the complexity of real-world decisions. The author, Nicholas Mitsakos, argues that decision-making is essentially a form of statistical analysis that involves models or algorithms, and being thorough in this understanding confers a significant advantage. is a comprehensive text discussing decision-making under uncertainty and the algorithms that can enhance the decision-making process. It covers a wide range of topics, including applications in various fields such as air traffic control, drug discovery, autonomous driving, and more. The introduction sets the stage by discussing the importance of understanding and managing uncertainty in decision-making. It defines algorithms in the context of systematic thinking and approaches to problem-solving, emphasizing the significance of defining problems clearly to find obvious solutions.