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: | 9783031320156 |
| Publication date: | 6th July 2024 |
| Author: | Louis A Cox |
| Publisher: | Springer an imprint of Springer International Publishing |
| Format: | Paperback |
| Pagination: | 433 pages |
| Series: | International Series in Operations Research & Management Science |
| Genres: |
Operational research Stochastics Risk assessment Management decision making Machine learning Management and management techniques Artificial intelligence |
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.
AI-ML for Decision and Risk Analysis features in the following genres: Operational research, Stochastics, Risk assessment, Management decision making, Machine learning, Management and management techniques, Artificial intelligence
AI-ML for Decision and Risk Analysis is available in Paperback
AI-ML for Decision and Risk Analysis was written by Louis A Cox and published by Springer an imprint of Springer International Publishing
AI-ML for Decision and Risk Analysis has 433 pages
Yes it is part of International Series in Operations Research & Management Science series
£179.99