Decision Intelligence: Human–Machine Integration for Decision-Making

280 Pages·2023·8.282 MB·English

by  Miriam O'Callaghan

Decision Intelligence
Revealing the limitations of human decision-making, this book explores how
Artificial Intelligence (AI) can be used to optimize decisions for improved busi-
ness outcomes and efficiency, as well as looking ahead to the significant contribu-
tions 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.

Decision Intelligence
Human–Machine Integration
for Decision-Making
Miriam O’Callaghan
First edition published 2023
by CRC Press
4 Park Square, Milton Park, Abingdon, Oxon, OX14 4RN
and by CRC Press
6000 Broken Sound Parkway NW, Suite 300, Boca Raton, FL 33487-2742
© 2023 Miriam O’Callaghan
CRC Press is an imprint of Informa UK Limited
The right of Miriam O’Callaghan to be identified as author of this work has been asserted in accordance
with sections 77 and 78 of the Copyright, Designs and Patents Act 1988.
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British Library Cataloguing-in-Publication Data
A catalogue record for this book is available from the British Library
ISBN: 978-1-032-38410-8 (hbk)
ISBN: 978-1-032-38409-2 (pbk)
ISBN: 978-1-003-34492-6 (ebk)
DOI: 10.1201/b23322
Typeset in Minion
by KnowledgeWorks Global Ltd.
Contents
List of Acronyms, xiii
Preface, xv
Acknowledgments, xix
Chapter 1 ◾ D ecision Intelligence: Introduction and Overview 1
INTRODUCTION TO DI 1
Defining Decision Intelligence 3
DI Evolution and Landscape 4
WHY WE NEED DI 6
DI to Optimize Decisions 6
DI for Improved Business Outcomes and Efficiency 7
HOW DI WORKS AND HOW IT LOOKS 8
Types of Business Decisions 9
Decision-Making Process 10
DI Forms 11
Decision Assistance 11
Decision Support 13
Decision Augmentation 13
Decision Automation 14
Infrastructure Design – Data Architecture for DI 14
STATE OF DI ADOPTION 17
Factors Affecting DI Adoption Decisions 18
CONCLUSION 19
v
vi ◾ Contents
CASE STUDY: AI-POWERED RECOMMENDATION
SYSTEM DELIVERING CONSISTENT ENERGY SAVING
AT GOOGLE DATA CENTERS 20
QUESTIONS 22
QUESTIONS FOR DISCUSSION 22
REFERENCES 23
Chapter 2 ◾ H umans vs. Machines in Decision-Making 25
HUMANS IN DECISION-MAKING 25
Behavioral Economics of Decision-Making 26
Neuroscience and Neuroeconomics Perspectives 30
COMPUTERS IN DECISION-MAKING 32
Basic Programming Methods 33
The Evolution of AI-Powered Decision-Making 35
Machine Learning 38
Supervised Machine Learning 39
Unsupervised Machine Learning 40
Reinforcement Learning 46
Classical Machine Learning 48
Neural Networks and Deep Learning 51
HUMAN VS. COMPUTER – WHO IS BETTER AT
DECISION-MAKING? 53
CONCLUSION 54
CASE STUDY: JOHNS HOPKINS MANAGES PATIENT
FLOW DURING COVID-19 WITH AI-POWERED
CAPACITY COMMAND CENTER 54
QUESTIONS 56
QUESTIONS FOR DISCUSSION 57
REFERENCES 58
Chapter 3 ◾ S ystems and Technologies for Decision-Making 61
ORGANIZATION AS A SYSTEM 61
DECISION-MAKING SYSTEM IN THE ORGANIZATION 62
Decision-Making Environments 65
Human Agents 68
Contents ◾ vii
SUPPORTING TECHNOLOGIES FOR MODERN DI SYSTEMS 70
AutoML 70
Computer Vision 71
Audio Processing 71
Natural Language Processing (NLP) 72
TECHNOLOGICAL SYSTEMS FOR DECISION-MAKING 75
Decision Support Systems 75
Intelligent Agents 78
KINDS OF INTELLIGENT AGENTS 79
Recommender Systems 82
CONCLUSION 83
CASE STUDY: RECOMMENDER SYSTEM FOR COVID-19
RESEARCH – INNOVATIVE DEEP NEURAL NETWORK
MODELS 84
QUESTIONS 86
QUESTIONS FOR DISCUSSION 87
REFERENCES 88
Chapter 4 ◾ I ntelligent Agents: Theoretical Foundations 89
MULTIDISCIPLINARITY OF INTELLIGENT AGENTS 89
AGENTS FOR SIMPLE DECISIONS 91
Decision Networks 92
Calculating Utilities to Determine the Optimal Decision 94
Expected Utilities of the Two Decisions and MEU 95
The Value of Information 95
Expected Utilities of the Two Decisions and MEU 97
AGENTS FOR COMPLEX DECISIONS 97
Dynamic Decision Networks 101
Solving MDPs with Value Iteration and Policy Iteration 103
Value Iteration 103
Policy Iteration 108
Monte Carlo Methods 109
MULTIAGENT DECISION-MAKING 114
Pure Strategy and Saddle Point Equilibrium 115
viii ◾ Contents
Mixed Strategy and Nash Equilibrium 117
Dominant Strategy Equilibrium 119
Pareto-Optimal Outcome 120
CONCLUSION 121
CASE STUDY: DESIGNING AGENT FOR COMPLEX
ENVIRONMENT – MULTIAGENT PATH PLANNING
WITH NONLINEAR MODEL PREDICTIVE CONTROL 122
QUESTIONS 124
QUESTIONS FOR DISCUSSION 124
REFERENCES 126
Chapter 5 ◾ D ecision-Making Building Blocks, Tools,
and Techniques 127
DATA FOR DECISION-MAKING 128
DECISION ANALYSIS 130
Decision Tables 133
Decision Trees 136
DECISION MODELING 139
Predictive Modeling 140
Regression Models 142
Classification Models 144
Time Series Models 144
Outliers Models 145
Clustering Models 146
Prescriptive Modeling 147
Heuristics Models 149
Optimization Models 150
LINEAR PROGRAMMING OPTIMIZATION 151
Simulation Models 155
TEXT ANALYTICS TECHNIQUES FOR DECISION-MAKING 157
CONCLUSION 161
CASE STUDY: DETECTING ANOMALIES AND
PREVENTING EQUIPMENT FAILURES IN STEEL
WITH NOODLE.AI ASSET FLOW 161
Contents ◾ ix
QUESTIONS 163
QUESTIONS FOR DISCUSSION 163
REFERENCES 164
Chapter 6 ◾ D ecision Intelligence Market: Vendors
and Solutions 167
DI SOLUTIONS 168
DI VENDORS 169
Peak 169
Tellius 172
Xylem 175
Noodle.ai 178
Aera Technology 181
Diwo 185
Quantellia 187
CONCLUSION 189
CASE STUDY: SISU HELPS SAMSUNG JUMPSTART
A $1 BILLION PRODUCT LAUNCH 190
QUESTIONS 191
QUESTIONS FOR DISCUSSION 192
REFERENCES 192
Chapter 7 ◾ D ecision Intelligence Framework for
Organizational Decision-Making 195
WHY WE NEED A FRAMEWORK FOR DECISION-MAKING 196
Deciding How to Decide 197
DI FRAMEWORK 198
Preparation and Planning 199
The Seven-Step Process 200
Step 1: Setting Key Goals 200
Step 2: Defining the Decision 201
Step 3: Rating the Decisions on Importance and
Complexity Levels 203
Step 4: Prioritizing and Classifying Decisions to
Determine the PI-AI Mix 204