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Decision Intelligence
Human–Machine Integration for Decision Making




  • Available for pre-order on April 5, 2023. Item will ship after April 26, 2023
ISBN 9781032384092
April 26, 2023 Forthcoming by Chapman & Hall
274 Pages 36 B/W Illustrations

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Book Description

Revealing the flaws in human decision making, this book explores how AI can be used to optimise decisions for improved business outcomes and efficiency, as well as looking ahead into the significant contributions Decision Intelligence (DI) can make to society and the ethical challenges it may raise.

Offering an impressive framework of Decision Intelligence (DI), 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 democratise AI, this book provides a systematic approach to AI intelligence and human involvement.

Replete with case studies on DI application, as well as wider discussions on the social implications of the technology, this book appeals to both students of AI and data solutions and businesses considering DI adoption. 

Table of Contents

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

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Author(s)

Biography

Dr. Miriam O'Callaghan, Associate Professor of Management, William Woods University