Index
This content is currently only available as a PDF
Citation
Pratt, L. (2019), "Index", Link, Emerald Publishing Limited, Leeds, pp. 211-218. https://doi.org/10.1108/978-1-78769-653-220191011
Publisher
:Emerald Publishing Limited
Copyright © Lorien Pratt, 2019
INDEX
Absolutdata
, 4, 170
AGI. See Artificial General Intelligence (AGI)
Ahmed, Nafeez Masada
, 136, 163
AHP. See Analytic hierarchy process (AHP)
AI. See Artificial Intelligence (AI)
AIG
, 4
Alibaba
, 4
Amazon
, 75
Analytic hierarchy process (AHP)
, 101–102
Artificial General Intelligence (AGI)
, 3, 175
robots
, 2
Artificial Intelligence (AI)
, 2, 68, 72–79
in context
, 78–79
decision intelligence bridges from
, 60–63
DI as software engineering discipline for
, 161
ethics and responsibility
, 160–161
expert systems
, 77
history: winters and summers
, 73–74
market
, 73
natural language processing (NLP)
, 76
reinforcement learning (RL) systems
, 77
supervised learning
, 76–78
understanding the core of
, 74–76
unsupervised learning
, 77
Asaro, Peter
, 82
Asimov, Isaac
, 63
Bateson, Gregory
, 65, 79, 89
Bateson, Nora
, 43, 70, 71, 72, 170
Berry, Benjamin
, 139
Big Data
, 2, 67–70, 91
Blackman, Reid
, 160–161
Bloomberg
, 44
BPM. See Business process management (BPM)
Brant, Steve
, 91, 183–184
Brewer, Joe
, 88, 109
Brown, John Seely
, 110
Brynjolfsson, Erik
, 120
Busigence
, 4, 170
Business process management (BPM)
, 112, 167
Business tracking discipline, DI as
, 164
Cable company sustainable energy generation
, 142–143
CAD. See Computer-assisted design (CAD)
Call detail records (CDRs)
, 150
Carter Center
, 143
Casart, Jim
, 19, 83, 164, 183, 184
Causal decision diagram (CDD)
, 10, 26–27, 33, 66, 70, 72, 80, 83, 109–110, 130, 135, 155, 156, 157, 159, 161, 162
“A ha” moment
, 49–50
examples
, 44, 50–51
as framework for integrating other technologies
, 44–49
invented
, 42–43
origins of
, 41–42
telecom customer care
, 50–51
Causal reasoning
, 79–81
CDD. See Causal decision diagram (CDD)
CDRs. See Call detail records (CDRs)
Chatbots, DI as generative model for
, 162
Churchill, Winston
, 167
Civilization
, 1–2, 65–67
CJAs. See Community Justice Advisors (CJAs)
Classic mistakes/best practices
, 146–154
allowing perfection
, 151
confusing levers with externals
, 149
confusing predictions with decisions
, 154
confusing proxies with outcomes
, 149
decision modeling requires sophisticated technical
background
, 153–154
expecting consensus
, 152
failing
to brainstorm outcomes
, 148
to communicate outcomes
, 146–147
miscommunications regarding delegated authority
, 147–148
over-reliance on data
, 149–151
responsibility without corresponding authority and vice versa
, 152–153
Cognitive capacity
, 1–2
Cognitive science
, 82
Colorado Bureau of Investigation
, 76
Colossus: the Forbin Project
, 175
Colucci, Michele
, 177
Community Justice Advisors (CJAs)
, 143
Companies, organizational influence mapping in
, 157
Complexity ceiling
, 52–57
breaking through
, 54–55
dimensions of complexity
, 56
handle complexity
, 56
leads to unintended consequences
, 56–57
solutions to complexity
, 57–60
Complexity science
, 83
Complex systems
, 82–83
Computer-assisted design (CAD) computer simulation
, 103
Context, right decision in changing
, 63–64
Copernican revolution
, 9
The Crisis of Civilization
, 163
Customer care, telecom
, 50–51
Customer’s likelihood to recommend (L2R)
, 34, 36–37
Cybernetics
, 81–82
Cyber resurrectionist
, 91
DA. See Decision analysis (DA)
Data
Big Data
, 2, 67–70, 91
decision intelligence and
, 114–115
decisions before data
, 51–52
Element Data
, 5
emerging data scientist specialist roles
, 172–174
“monochromatic tonality” of data
, 71
over-reliance on
, 149–151
terabytes of
, 2
trump data, system dynamics
, 91–92
warm data
, 70–72
Da Vinci Co-op
, 111
Davis, Charles
, 97, 176
Decision analysis (DA)
, 101
DecisionCloud
, 170
Decision engineering
, 4
Decision Intelligence (DI)
, 4, 68–69, 168–169, 170, 181, 182
as analysis framework for AI
, 160–161
as basis for new form of dynamic “Wikipedia,”
, 162–163
as breakthrough technology to solve “wicked”
problems
, 161–162
bridges from AI/ML theory to practice
, 60–63
as business tracking discipline
, 164
combating wealth inequality through
, 178–180
as context layer
, 161
continuous improvement
, 110
consensus
, 27–29
as core of software
, 159
and data
, 114–115
ecosystem
, 170–172
for education
, 156
extends machine learning
, 46–49
as foundation for journalism in age of complexity
, 163–164
as generative model for chatbots
, 162
goal of
, 65–66
for government planning
, 164–165
implementation
, 110
integration
, 110
for intelligence analysis
, 165–166
as leadership and management discipline
, 159
LINK
, 13–16
mapping
, 110
as mechanism for human/machine collaboration
, 155–156
as mechanism for intelligence augmentation
, 155–156
as meeting discipline
, 162
multiple levels at
, 109–111
at NASA’s Frontier Development Laboratory
, 136–137
and new mythos
, 174–175
optimization
, 110
as personal decisions
, 162
in practice
, 22–24
real-time decision model tracking
, 111
as risk management framework
, 160
scenario comparison
, 110
sharing
, 110
from simulation to optimization
, 84
as software engineering discipline for AI
, 161
solutions renaissance
, 6–10
as technology that glues the tech stack to human stack
, 158–159
as tool to support decision making
, 157
understanding
, 109–110
users of
, 21–22
Decision intelligence deployments examples
, 136–146
cable company sustainable energy generation
, 142–143
decision intelligence
in development and conflict
, 143–146
for market decisions
, 142
at NASA’s FDL
, 136–137
innovation management
, 140–141
multi-link decision
, 139–140
utilities and operators
, 137–139
web-based interactive decision model for training decisions
, 141–142
Decision makers
, 66, 158–159
Decision making
decision intelligence as tool to support
, 157
DI as generative model for chatbots supporting
, 162
simulations
, 86–87
Decision model
, 79
benefits from
, 111–112
for training decisions
, 141–142
Decision model, building
, 116–135
analyzing levers
, 122–124
brainstorming externals
, 128
brainstorming levers
, 121–122
brainstorming outcomes
, 119–121
tangibles and intangible goals and outcomes
, 120–121
breadth before depth
, 130–131
convergent phase: analyzing outcomes and goals
, 124–128
collecting out-of-context comments respectfully
, 127–128
decision boundary
, 124
proxy goals
, 124–127
determining the role of machine learning
, 131–134
setup
, 116–118
starting the meeting
, 118–119
determining the project rules of engagement
, 118–119
using the model
, 134–135
wiring up the model
, 129–130
Decision modeling
benefits
, 112–113
examples
, 113–114
Decisions before data
, 51–52
Decision tree
, 79
“Deflector Selector” project
, 136–137
Deming, W. Edwards
, 96
Democratization
, 3
Democratization power of simplicity
, 63
Design and design thinking
, 103
DI. See Decision Intelligence (DI)
Discipline
decision intelligence as leadership and management
, 159
DI as business tracking
, 164
DI as meeting
, 162
Divergent-versus-convergent thinking
, 115–116
DNVGL
, 4, 170
Education, decision intelligence for
, 156
Edvinsson, Håkan
, 59, 130, 183, 184
eHealthAnalytics
, 4, 170
Element Data
, 4, 5
ElementData
, 176
Emerging data scientist specialist roles
, 172–174
Englebart, Doug
, 15, 101, 183–184
EPCOT. See Experimental Prototype City of Tomorrow (EPCOT)
Ergonomics
, 82
European telecommunications company
, 53
EvenClever
, 170
Evidence-based decision making
, 20–21
Experimental Prototype City of Tomorrow (EPCOT)
, 175
Facebook
, 75, 77, 81
Fair Isaac
, 4
False proxies
, 18–19
Fast-moving Consumer Goods (FMCG) company
, 140
FDL. See Frontier Development Laboratory (FDL)
Fenwick, Bill
, 184
Ferose V. R.
, 179, 180, 183
FICO
, 170
Fisher, Ruth
, 39–40, 91–92, 184
FMCG company. See Fast-moving Consumer Goods (FMCG) company
Foresight
, 84–85
Forrester, Jay
, 91
Frontier Development Laboratory (FDL), NASA
, 136–137
“Deflector Selector” project
, 136–137
Fruehauf, Jennifer
, 183
Fuller, Buckminster
, 88, 109
Game theory
, 103–104
Gandhism
, 178–180
Gates, Bill
, 73
GCC. See Global Challenges Collaboration (GCC)
Getahun, Beza
, 184
Gilling.com
, 4
Global Challenges Collaboration (GCC)
, 168
Golon, Allie
, 183
Gongos
, 4, 170
Google
, 4, 67, 72–73, 90
Government planning, DI for
, 164–165
Governments, organizational influence mapping in
, 157
Groupon
, 4
Grubhub
, 4
Gupta, Arnab
, 100
Hanh, Thich Nhat
, 176
Harrison, George
, 119
Hauser, Avi
, 183
Headwind of disruption
, 168–170
Helmer, Nicole
, 171–172
Hobbes, Michael Hobbes
, 144–146
Hook, Anselm
, 86
Hopp, Faith
, 184
Horwood, Jennifer
, 183
Human collaboration, decision intelligence as mechanism for
, 155–156
Human–computer interaction (HCI)
, 82
Hype feedback loop
, 10–11
IA. See Intelligence augmentation (IA)
IDEO
, 103
InfoHarvest
, 4, 170
Inouye, Liesl
, 183
Intelligence analysis, DI for
, 165–166
Intelligence augmentation (IA)
, 97–101
decision intelligence as mechanism for
, 155–156
IntelliPhi
, 140
Interface
, 9
Interdependencies and whack-a-mole
, 87–89
Invisible Engines
, 92
Jaret, Jessica
, 184
Johnson, Margaret
, 183–184
Jones, Milo
, 67
Julian, Arlow
, 183
Jung, Carl
, 43
Källmark, Göran
, 183
Kemp, Linda
, 104–105, 183–184
Kerbel, Josh
, 6, 88–89
Key Performance Indicators (KPIs)
, 87–88
Klaus Schwab’s Fourth Industrial Revolution
, 170
KM. See Knowledge management (KM)
Knowledge Gardens
, 178
Knowledge management (KM)
, 104–105
Kort, Barry
, 91
Kozyrkov, Cassie
, 5, 9, 61, 169–170, 183–184
KPIs. See Key Performance Indicators (KPIs)
L2R. See Likelihood to recommend (L2R)
Ladd, Rick
, 104, 105, 183–184
Lamb, Alex
, xi–xiii
Landau, Valerie
, 100–101, 157, 183–184
Laszlo, Kathia Castro
, 176
Launch To Tomorrow (LTT) project
, 85, 165
LeCun, Yann
, 81
Leadership discipline, decision intelligence as
, 159
Legal and policy decisions
, 176–177
Levine, Jeanne
, 184
Likelihood to recommend (L2R)
, 34, 36–37
Link
cause-and-effect
, 16–20
DI
, 13–16
LTT project. See Launch To Tomorrow (LTT) project
Lumina Decision Systems
, 4
Machine collaboration, decision intelligence as mechanism for
, 155–156
Machine learning (ML)
, 72–79
decision intelligence bridges from
, 60–63
model
, 37–38
Maiorana, Charlotte
, 184
Malcolm, Nadine
, 183, 184
Management discipline, decision intelligence as
, 159
Management Science
, 85
Manney, PJ
, 174–175
Market decisions, decision intelligence for
, 142
Martin, Roger L.
, 33
Mastercard’s DI initiative
, 4
McChrystal, Stanley
, 87
McKinsey
, 67
McMullen, John
, 65
Meeting discipline, DI as
, 162
Microsoft
, 4, 69
Millennium Project
, 65
MITRE Corporation
, 86
ML. See Machine learning (ML)
Multi-link decision
, 139–140
NASA
, 5
NASA’s Frontier Development Laboratory (FDL)
, 136–137
National Oceanic and Atmospheric Administration (NOAA)
, 67
Natural language processing (NLP)
, 76, 176–177
Nemmers, Janet
, 183
Nitz, Elizabeth
, 184
NLP. See Natural language processing (NLP)
NOAA. See National Oceanic and Atmospheric Administration (NOAA)
Oliver, Ian
, 183
O’Neil, Ryan
, 86, 87
Online stochastic combinatorial optimization (OSCO)
, 86
Opera Solutions
, 100
Operations research (OR)
, 85–86
OpsPro
, 170
OR. See Operations research (OR)
Organizational influence mapping
in organizations/companies and governments
, 157
Organizational robots
, 175
Organizations, organizational influence mapping in
, 157
OSCO. See Online stochastic combinatorial optimization (OSCO)
Panjabi, Raj
, 144
Park, Jack
, 178, 184
Pearl, Judea
, 115, 149
Pfeffermann, Guy
, 184
Populating links
, 101–102
PowerNoodle
, 4, 170
Pratt, Annis
, 184
Pratt, Lorien
, 178–179, 182
Prospective models
, 21, 32
Prowler.io
, 4, 5, 77–78, 170
PureTech
, 4, 170
Quantellia
, 4, 87, 111, 139–140, 142–143, 144, 170, 173, 183
Raghavendra, Kamesh
, 179
Reality stack
, 158
RECAP project
, 67
Reductionism (analysis)
, 6
Return on its investment (ROI)
, 140
Rich, Rob
, 183–184
Risk management framework, decision intelligence as
, 160
Robots, organizational
, 175
ROI. See Return on its investment (ROI)
Ronis, Sheila, Dr.
, 84–85, 96
Rowling, J. K.
, 91–92
Saaty, Thomas L.
, 102
Salvatico, Yvette Montero
, 1, 52–53, 181, 183–184
SAP
, 5, 170, 171–172, 180, 183
Satavia
, 4, 170
SDP. See Society of Decision Professionals (SDP)
Sherer, James A.
, 176–177
Silberzahn, Philippe
, 67
Silicon Valley Sim Center
, 87
Simulation organizations
, 86–87
Simulations, decision making
, 86–87
Smith, Cymbre
, 184
Smith, Dave “Tex,”
, 111
Smith, Griffin
, 184
Smith, Richard
, 184
Snowden, Dave
, 83
Society of Decision Professionals (SDP)
, 101
Software engineering discipline, for AI
, 161
Solutions renaissance
, 7–8, 9–10
Spencer, Frank
, 1, 52–53, 61, 165, 167, 181, 183–184
Stavrou, Nick
, 183
Supporting decision making, DI as generative model for chatbots
, 162
Sustainable energy generation, cable company
, 142–143
Sympatico
, 170
Synthesis
, 6
System dynamics
, 90
System dynamics
, 91–96
fishing example
, 92–96
trump data
, 91–92
System Dynamics Society
, 91
Systems analysis
, 89–96
importance of
, 96
in management science
, 96
Tabarrock, Alex
, 91–92
Team of Teams (McChrystal)
, 87
Technology stack
, 158
Telecom company
, 41, 88, 139, 150
Telecom customer care
, 50–51
Terabytes of data
, 2
Thaker, Anand
, 140
Thinking, divergent-versus-convergent
, 115–116
Thomas, Sammy
, 183
TM Forum
, 68
Total quality management (TQM)
, 167
TQM. See Total quality management (TQM)
Transfer learning
, 96–97
TransparentChoice
, 4
TransVoyant
, 4
Trusteeship
, 178–180
Uber
, 4
Urbint
, 4
Value network analysis (VNA)
, 151
van Gelder and Monk
, 4
Vilas, Deb
, 184
Visual spatial
, 25
VNA. See Value network analysis (VNA)
Walt Disney
, 175
Ward Bank
, 34, 39
Warm data
, 70–72
Watts, Alan
, 174
Wealth equity index (WEI)
, 179
Web-based interactive decision model, for training decisions
, 141–142
Web of wicked problems
, 65–67
WEI. See Wealth equity index (WEI)
Whitelock, Karl
, 183–184
Wicked problems
DI as breakthrough technology to solve
, 161–162
web of
, 65–67
Wiener, Norbert
, 82
World Makers
, 86
World of Warcraft video game
, 156
Zangari, Mark
, 41, 96, 139–140, 168, 183
Zhao, Emily
, 184
- Prelims
- Introduction
- Chapter 1 Getting Serious about Decisions
- Chapter 2 Breaking through the Complexity Ceiling
- Chapter 3 Technologies, Disciplines, and Other Puzzle Pieces of the Solutions Renaissance
- Chapter 4 How to Build Decision Models
- Chapter 5 The Power of the Decision Model Framework
- Chapter 6 Looking to the Future
- Conclusion
- Acknowledgments
- Bibliography
- Index