This is Chat GPT’s summary of the draft of my new book (published soon).

The book 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.

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.

The following chapters delve into the concept of agents in decision-making, defining an agent as any entity that acts based on observations of its environment, whether physical or software-based. The text explores how agents interact with their environments through an observe-act cycle and how dynamic environments can change the value and impact of actions.

The book is structured to discuss uncertainty in artificial intelligence and decision-making, outlining four sources of uncertainty: outcome, model, state, and interaction uncertainties. It touches upon various applications of decision-making frameworks in different domains, like aircraft collision avoidance, autonomous driving, breast cancer screening, financial portfolio allocation, distributed wildfire surveillance, and Mars exploration.

Subsequent sections provide a more technical discussion of the methods of designing decision-making agents, including explicit programming, supervised learning, optimization, planning, and reinforcement learning. It also covers historical perspectives on automation in decision-making, contributions from different fields to the development of decision-making algorithms, and their societal impacts.

The last accessed portion details the probabilistic reasoning, representation of uncertainty, joint and conditional distributions, and Bayesian networks, explaining how these concepts contribute to the process of making informed decisions under uncertainty. The document also plans to cover inference methods and the computational complexity associated with decision-making algorithms.

The text combines theoretical foundations with practical applications, aiming to equip readers with a comprehensive understanding of how to make better decisions when faced with uncertainty.