This book explains and illustrates recent developments and advances in decision-making and risk analysis. It demonstrates how artificial intelligence (AI) and machine learning (ML) have not only benefitted from classical decision analysis concepts such as expected utility maximization but have also contributed to making normative decision theory more useful by forcing it to confront realistic complexities. These include skill acquisition, uncertain and time-consuming implementation of intended actions, open-world uncertainties about what might happen next and what consequences actions can have, and learning to cope effectively with uncertain and changing environments. The result is a more robust and implementable technology for AI/ML-assisted decision-making.
The book is intended to inform a wide audience in related applied areas and to provide a fun and stimulating resource for students, researchers, and academics in data science and AI-ML, decision analysis, and other closely linked academic fields. It will also appeal to managers, analysts, decision-makers, and policymakers in financial, health and safety, environmental, business, engineering, and security risk management.
Part I. Received Wisdom.- 1.Rational Decision and Risk Analysis and
Irrational Human Behavior.- 2.Data Analytics and Modeling for Improving
Decisions .- 3. Natural, Artificial, and Social Intelligence for
Decision-Making.- Part 2: Fundamental Challenges for Practical Decision
Theory.- 4.Answerable and Unanswerable Questions in Decision and Risk
Analysis.- 5.Decision Theory.- 6.Learning Aversion in Benefit-Cost Analysis
with Uncertainty.- Part 3: Ways forward 7.Addressing Wicked Problems and Deep
Uncertainties in Risk Analysis.- 8.Muddling Through and Deep Learning for
Bureaucratic Decision-Making.- 9.Causally Explainable Decision
Recommendations using Causal Artificial Intelligence.- Part 4: Public Health
Applications.-
10. Re-Assessing Human Mortality Risks Attributed to
Agricultural Air Pollution: Insights from Causal Artificial Intelligence.-
11.Toward more Practical Causal Epidemiology and Health Risk Assessment Using
Causal Artificial Intelligence.-
12. Clarifyingthe Meaning of
Exposure-Response Curves with Causal AI.-
13. Pushing Back on AI: A Dialogue
with ChatGPT.- Index.
Louis Anthony Cox Jr. is a Professor of Business Analytics at the University of Colorado, USA; Chief Digital Intelligence Officer at Entanglement, Inc.; and President of Cox Associates, a Denver-based applied research company specializing in artificial intelligence and machine learning; health, safety, and environmental risk analysis; epidemiology; policy analytics; data science; and operations research. Dr. Cox is Editor-in-Chief of Risk Analysis: An International Journal. He is a member of the National Academy of Engineering, a Fellow of the Institute for Operations Research and Management Science (INFORMS), and a Fellow of the Society for Risk Analysis (SRA). He has authored and co-authored over 200 journal articles and numerous books and chapters in these fields. He holds over a dozen US patents on applications of artificial intelligence, signal processing, statistics, and operations research in telecommunications. His current research interestsinclude computational statistical methods for causal inference in public health risk analysis, data mining, and advanced analytics for risk management, business, and public policy applications.
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