1st Edition
Decision Intelligence Human–Machine Integration for Decision-Making
Free Shipping (6-12 Business Days)
shipping options
- Available for pre-order on April 5, 2023. Item will ship after April 26, 2023
Free Shipping (6-12 Business Days)
shipping options
- Available for pre-order on April 5, 2023. Item will ship after April 26, 2023
Revealing the limitations of human decision-making, this book explores how Artificial Intelligence (AI) can be used to optimize decisions for improved business outcomes and efficiency, as well as looking ahead to the significant contributions Decision Intelligence (DI) can make to society and the ethical challenges it may raise.
From the theories and concepts used to design autonomous intelligent agents to the technologies that power DI systems and the ways in which companies use decision-making building blocks to build DI solutions that enable businesses to democratize AI, this book presents an impressive framework to integrate artificial and human intelligence for the success of different types of business decisions.
Replete with case studies on DI applications, as well as wider discussions on the social implications of the technology, Decision Intelligence: Human–Machine Integration for Decision Making appeals to both students of AI and data sciences and businesses considering DI adoption.
List of Acronyms
Preface
Acknowledgements
Chapter 1 Decision Intelligence – Introduction and Overview
Introduction to DI
Defining Decision Intelligence
DI Evolution and Landscape
Why We Need DI
DI to Optimize Decisions
DI for Improved Business Outcomes and Efficiency
How DI Works and How It Looks
Types of Business Decisions
Decision Making Process
DI Forms
Decision Assistance
Decision Support
Decision Augmentation
Decision Automation
Infrastructure Design – Data Architecture for DI
State of DI Adoption
Factors Affecting DI Adoption Decisions
Conclusion
Case Study: AI-Powered Recommendation System Delivering Consistent Energy Saving at Google Data Centers
Questions for Discussion
References
Chapter 2 Humans Vs. Machines in Decision-Making
Humans in Decision-Making
Behavioral Economics of Decision-Making
Neuroscience and Neuroeconomics Perspectives
Computers in Decision-Making
Basic Programming Methods
The Evolution of AI-Powered Decision-Making
Machine Learning
Supervised Machine Learning
Unsupervised Machine Learning
Reinforcement Learning
Classical Machine Learning
Neural Networks and Deep Learning
Human Vs. Computer – Who is Better at Decision-Making?
Conclusion
Case Study: John Hopkins Manages Patient Flow During Covid-19 With AI Powered Capacity Command Center
Questions for Discussion
References
Chapter 3 Systems and Technologies for Decision-Making
Organization as a System
Decision Making System in the Organization
Decision Making Environments
Human Agents
Supporting Technologies for Modern DI Systems
AutoML
Computer Vision
Audio Processing
NLP (Natural Language Processing)
Technological Systems for Decision-Making
Decision Support Systems
Intelligent Agents
Recommender Systems
Conclusion
Case Study: Recommender System for Covid-19 Research – Innovative Deep Neural Network Models
Questions for Discussion
References
Chapter 4 Intelligent Agents – Theoretical Foundations
Multidisciplinarity of Intelligent Agents
Agents for Simple Decisions
Decision Networks
Agents for Complex Decisions
Dynamic Decision Networks
Solving MDPs With Value Iteration and Policy Iteration
Value Iteration
Policy Iteration
Monte Carlo Methods
Multiagent Decision-Making
Pure Strategy and Saddle Point Equilibrium
Mixed Strategy and Nash Equilibrium
Dominant Strategy Equilibrium
Pareto Optimal Outcome
Conclusion
Case Study: Designing Agent for Complex Environment – Multiagent Path Planning With Nonlinear Model Predictive Control
Questions for Discussion
References
Chapter 5 Decision-Making Building Blocks, Tools and Techniques
Data for Decision-Making
Decision Analysis
Decision Tables
Decision Trees
Decision Modeling
Predictive Modeling
Regression Models
Classification Models
Time Series Models
Outliers Models
Clustering Models
Prescriptive Modeling
Heuristic Models
Optimization Models
Simulation Models
Text Analytics Techniques for Decision Making
Conclusion
Case Study: Detecting Anomalies and Preventing Equipment Failures in Steel With Noodle.ai Asset Flow
Questions for Discussion
References
Chapter 6 Decision Intelligence Market – Vendors and Solutions
DI Solutions
DI Vendors
Peak
Tellius
Xylem
Noodle.ai
Aera Technology
Diwo
Quantellia
Conclusion
Case Study: Sisu Helps Samsung Jumpstart a $1 Billion Product Launch
Questions for Discussion
References
Chapter 7 Decision Intelligence Framework for Organizational Decision-Making
Why We Need a Framework for Decision-Making
Deciding How to Decide
DI Framework
Preparation and Planning
The 7-Step Process
Step 1: Setting key goals
Step 2: Defining the decision
Step 3: Rating the decision on importance and complexity levels
Step 4: Prioritizing and classifying decisions to determine the PI-AI mix
Step 5: Formulating decision implementation strategy
Step 6: Implementing the strategy
Step 7: Evaluating the strategy
Conclusion
Case Study: Dräger Improves Customer Service With Starmind - Less Time Searching, More Time for Customers
Questions for Discussion
References
Chapter 8 Recommendations for DI Implementation and Ethics
Recommendations for DI Implementation
DI Readiness Assessment
Strategic and Leadership Readiness
Infrastructural and Operational Readiness
Talent and Cultural Readiness
DI Readiness Audit
Ethics for DI
Biased Algorithms
Data Privacy and Protection
Accuracy of Data and Information
Job Loss
Initiatives of Large Corporations to Promote AI Ethics
Conclusion and the Future of DI
Case Study: AI for Greater Good – Stanford Medicine Uses Google Glass to Help Kids With Autism Socialize
Questions for Discussion
References
Biography
Dr. Miriam O'Callaghan, Associate Professor of Management, William Woods University
"Organizational decision making has been a very challenging area in the last decade. The availability of new technologies, such as AI, has contributed to this. Organizations are looking for guidance on understanding these new technologies and advice on how to incorporate them into their system. The content seems just right for a course covering a non-technical subject area as a textbook. The students, especially those with a business background, will benefit from this textbook greatly. Dr. O’Callaghan’s book covers just the right areas to guide the students seeking to understand and implement these decision-making processes in organizations…This book will serve as an excellent resource for big companies and startups where wise business decisions are needed the most. If new businesses are guided through good decision-making, they will have a successful future. Dr. O’Callaghan’s book does just that and where it is needed most."
-- Dr. Gulsebnem Bishop, Campbellsville University, Kentucky, UK
"Decision Intelligence is vital for any enterprise looking to reach new heights of growth because we can no longer deal with the number and speed of daily decisions we must make. Decision Intelligence will be a significant driver for operationalizing AI at scale within the enterprise. Today, AI is capable of understanding the business, recommending a decision, acting to execute it, and learning to improve the quality of future decisions. We will look back at this era as the inflection point in how companies make decisions and how teams strategize and collaborate. Miriam O’Callaghan’s book is an excellent summary and guide to understanding the emerging category of Decision Intelligence. It’s an instrumental book for a wide variety of audiences who wants to grasp the science behind this new technology and harness its potential."
Shariq Mansoor, Chief Technology Officer and Founder of Aera Technology
We offer free standard shipping on every order across the globe.
- Free Shipping (6-12 Business Days)
