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Decision Intelligence: Human–Machine Integration for Decision-Making PDF

280 Pages·2023·8.282 MB·English
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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. All rights reserved. No part of this book may be reprinted or reproduced or utilised in any form or by any electronic, mechanical, or other means, now known or hereafter invented, including photocopying and recording, or in any information storage or retrieval system, without permission in writing from the publishers. For permission to photocopy or use material electronically from this work, access www.copyright.com or contact the Copyright Clearance Center, Inc. (CCC), 222 Rosewood Drive, Danvers, MA 01923, 978-750-8400. For works that are not available on CCC please contact mpkbookspermissions@tandf. co.uk Trademark notice: Product or corporate names may be trademarks or registered trademarks, and are used only for identification and explanation without intent to infringe. 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

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