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AI-ML for decision and risk analysischallenges and opportunities for normative decision theory /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	AI-ML for decision and risk analysisby Louis Anthony Cox Jr.
其他題名:	challenges and opportunities for normative decision theory /
作者:	Cox, Louis A.
出版者:	Cham :Springer International Publishing :2023.
面頁冊數:	xxiii, 433 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
標題:	Decision makingData processing.
電子資源:	https://doi.org/10.1007/978-3-031-32013-2
ISBN:	9783031320132$q(electronic bk.)

AI-ML for decision and risk analysischallenges and opportunities for normative decision theory /
Cox, Louis A.

AI-ML for decision and risk analysischallenges and opportunities for normative decision theory /[electronic resource] :by Louis Anthony Cox Jr. - Cham :Springer International Publishing :2023. - xxiii, 433 p. :ill., digital ;24 cm. - International series in operations research & management science,v. 3452214-7934 ;. - International series in operations research & management science ;v. 140..

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. Clarifying the Meaning of Exposure-Response Curves with Causal AI -- 13. Pushing Back on AI: A Dialogue with ChatGPT -- Index.

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.

ISBN: 9783031320132$q(electronic bk.)

Standard No.: 10.1007/978-3-031-32013-2doiSubjects--Topical Terms:

235779
Decision making
--Data processing.

LC Class. No.: HD30.23 / .C68 2023

Dewey Class. No.: 658.4030028563

AI-ML for decision and risk analysischallenges and opportunities for normative decision theory /
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520 $a 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.
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