Abbott, Jim, 136
Abdul-Jabbar, Kareem, 212
Abecasis, Goncola, 78–79
ability: credit for recombination and, 112–13; diversity vs., 105; hiring by, 98; prediction error and, 72; tools and, 98, 104
AB InBev, 197
academic research: authors’ cognitive diversity and conventional papers, 180–81; authors’ cognitive diversity and novel papers, 180–81; teams and, 176, 177. See also scientific research; social science research
access and legitimacy paradigm, 265n20
acquired diversity, 54
additive improvements, 107
adjacent possibles: categorization and, 62–63; perspective and, 61; value of diversity and, 119
advertising, identity diversity bonuses and, 190–91
affirmative action, xiv, 196–97, 201, 265–66n22
African Americans: disparities in education, income, and employment and, 193–94; identity diversity and women, 141; institutional discrimination and, 267n5; mathematics and science careers and, 26
age identity, 136
Ahern, Kenneth, 168
airplane design, potential diversity bonuses and, 46–47
algorithm development, 36
algorithms: Cinematch, 32, 34, 35, 51; go, 118; machine learning, 70–71, 81–83; Netflix Prize, 33; predicting race and gender, 259n26; random forest, 81, 82
Allen, Danielle, xvi
Allstate, 197
Alphabet of the Famous Test, 90–93
Al Qaeda, xiii
Alternative Uses Task, 87
Amazon shipping costs, potential diversity bonuses and, 47
American Bar Association, 213
American corporations, repatriation of monies, 199–200
analogy, identity and choice of, 135
analytic tradecraft, diversity bonuses and, 123–24
Anthony, Susan B., 56
Antonio, A. L., 233
Apostle, Tom, 99
Apple, 175; creativity and, 85; diversity initiative at, 209; emojis, 87; iPhone, 96; repatriation of monies, 199–200
artificial intelligence, diversity and, 81–83
Asian Americans, identity diversity and, 138–39, 140, 148
assume a can opener heuristic, 110
assume what you need heuristic, 110
Atlanta Hawks, 212
backgammon, strategies for, 117–18
Bakke case, 197
Bank of England, workforce diversity and, 104
Bank of New York Mellon, workforce diversity and, 265n11
Bath, Patricia, 157–58
behavior: diversity bonuses and, 5, 6; identity diversity and group, 230–36
behavioral diversity, 54
Bell, Bradford S., 179
Bell, Robert, 36
Bell Canada, 218
BellKor’s Pragmatic Chaos team, 34–37, 51
Ben and Jerry’s, 175
Bendor, Jon, 15
Bernard, Robert O., 259n31
Berry, Wendell, 196
Best Buy, prediction markets and, 84
best practices, improvements in, 97, 105–7
BET (Black Entertainment Television), 158–59, 160
Better Homes and Gardens Real Estate, 216
Bezos, Jeff, 199
bias: accumulation of, 214–16; discrimination and, 267n6; selection, 138–39, 182; Tom Hanks Is Busy experiment, 152–53
bias reduction, 213–16
bias variance decomposition theorem, 254n8
Big Chaos team, 34
Biggby’s, 265n11
Bill of Rights, xiii
binders, representation of women and, 152
bioprospecting, 125
Black Board of Directors Association, 119
Black Lives Matter movement, 130
BlackRock, 205–7
Bletchley Park, 50
board diversity: business case for, 204–5; CEO hiring and, 119–20; financial performance and, 165; women and, 165–66, 167–71, 172, 204–5, 261nn14, 16
Boeing, 46–47, 186, 197, 198, 207, 213
Bork, Robert, 124
bottom-line benefits of diversity, xiv, 1, 197–99. See also business case for diversity and inclusion
Boulding, Kenneth, 110
Bourke, Juliet, 120–21
Bourke model, 120–23
Bowie, David, 159
breakthrough solutions, problem solving and, 96–97, 98–105
Bridgewater and Associates, 217, 220
bromine extraction, 97
Brusselator limit cycle model, 109, 257n53
Bullock, Sandra, 151, 152, 153
bundle-of-sticks analogy, 141, 259n11
business: defined, 264n3; mission of, 184
business case for diversity and inclusion, xv, 184–208, 223–24; business case in full, 203–5; demographic trends and, 185; diversity and inclusion on Wall Street, 205–7; growing belief in diversity bonuses, 187–88; identity and repertoires, 192–93; multiple tasks and, 120–23; need for evidence of, 243–44; normative and demographic arguments, 193–95; organizations and identity diversity bonuses, 188–92; special power of diversity bonuses, 195–203
Calculus (Apostle), 99
calculus problems, solving, 98–99
candidate selection, 124
Cantor, Nancy, 195
Cartesian coordinate system, 60–61
casting decisions, race and, 152–53
categories, identity, 136–38
categorization: cognitive repertoires and, 60, 62–63; defined, 62; identity-based, 146, 161; models and, 65; prediction and, 75–81; tradecraft and, 123
Caterpillar, 207
causality, evidence between diversity and performance and, 167–71
causal models, prediction and, 70–71
causal studies, demonstrating diversity bonuses, 164
CEO hiring, 119–20
Cerf, Vint, 219
CERN, 164
chemical systems, well-mixed, 108–10
Chicago Daily Tribune (newspaper), 96–97
Chipotle, McDonald’s investment in, 120–21
Cinematch algorithm, 32, 34, 35, 51
citations in scientific research papers, 180–82, 263–64n48
civil rights, diversity and, xiii
Cleveland Cavaliers, 212
cloud framework, 11, 134, 142–43, 149
cognitive differences, stereotypical identity-based, 145–49
cognitive diversity, 2–3; assumption of from identity diversity, 230–36; Bourke model and, 121, 122–23; defined, 14; drug discovery and, 126; evidenced in patents, 180; evidenced in scientific papers, 180–82; identity diversity as source of, xvi, 229–30; identity diversity bonuses and, 188–89, 224–25; identity diversity in technical fields and, 154–56; inclusion and, 25; informational diversity and, 226, 227; linked to identity diversity, 9–10, 11–12, 24–26, 134–36, 143–50, 238–41; performance in knowledge economy and, 7–11; perspective taking and, 172; relevance to task and, 163–64, 169; social category diversity and, 228; team performance and, 11–12; venture capital evaluations and, 124–25. See also evidence of cognitive and identity diversity benefits
cognitive diversity bonus. See diversity bonuses
cognitive economy, diversity bonuses and, 101. See also knowledge economy
cognitive nonroutine tasks: diversity bonuses and, 39, 40–43; inclusive cultures and, 218
cognitive repertoires, 8, 52–67; building diverse, 133; as collection of tools, 15–17; defined, 11; gender and, 240; the group as the union of repertoires, 66–67; growing belief in hiring for diverse, 187–88; heuristics, 58–59; identity bundles and, 150–61; identity diversity and, 150–62; identity diversity bonuses and, 192–93; inclusive culture and, 216; information and, 56–57; knowledge and, 57–58; linking identities and, 143–50; mapping to outcomes, 55; mental models and frameworks, 63–66; overview, 52–56; of potential employees, 213, 266n3; representations, 59–63; teams and, 163
cognitive routine tasks, 39, 41; additive nature of, 45; diversifying workforce for, 195; diversity bonuses and, 43
cohorts, selecting, 8–9
Cold Stone Creamery, 216
collaborative filtering, 33
collective action problem, 196
collective error, in diversity prediction theorem, 72–74, 247–48, 249
collective intelligence, diversity bonuses and, 50
college admissions, diversity bonuses and, 128–29
committees, decisions by, 119–20
community of communities, trust and, xv–xvi
complexity: diversity bonuses and, 27–28
complex tasks: diverse teams and, 209–10; diversity bonuses and, 15–24, 46–48, 68, 221
conditions for diversity bonuses, 37–48; no bonus tasks, 42–46; tasks with diversity bonuses, 46–48
Condorcet’s jury theorem, 258n69
congruence effect, on functionally diverse teams, 238–41
consumer product advertising, diversity and, 153
contributions, identity diversity and, 5
Cope, George, 218
correlations, evidence for diversity bonuses and, 164, 165–67
costs of practicing diversity and inclusion, 172–73, 212–13
creative search, 112
creative tasks, 3, 85–96; Alphabet of the Famous Test, 90–93; diversity bonuses and, 85–96, 88–89; innovation and, 111–13; measuring creativity, 86–87; Medici effect, 93–95; no test exists, 95–96; subadditive nature of, 94; superadditivity and, 94; trend toward teams in, 162–63
critical thinking in groups, social category diversity and, 233
cross-functional teams, 237–43
crowd beats the average law, 247
crowdsourced prediction contest, 37
crystallized intelligence, 143–44
Cuarón, Alfonso, 151–52
cultural diversity, economic performance and, 166
culture: supporting diverse teams, 3, 4; supporting diversity, 5
Cummins, 207
Curie, Marie, 175
Curie, Pierre, 175
Curry, Stephen, 212
daily bonuses, 48–49
data: diversity bonuses and large amounts of, 78; information as interpretable, meaningful, 56; machine learning techniques and, 71; mental models and, 65–66; models and, 68, 254n1
Davies, Robertson, 159
da Vinci, Leonardo, 132
de Bono, Edward, 120
decision making: Bourke model of, 120–23; stages of, 120
Deming, W. Edwards, 3
demographic arguments for diversity and inclusion, 193–96, 198; financial service firms and, 206
demographic trends, business case for diversity and inclusion and, 185
“Demography and Diversity in Organizations” (O’Reilly), 237
Dennis, Cathy, 163
design: cognitive diversity and, 14; diversity bonuses and, 127
design patents, 112
Difference, The (Page), 1, 211
disciplines, diversity spanning, 131
discrimination: bias reduction and, 213–14, 267n6; institutional, 267n5; race and workplace, 213, 267n6
discrimination and fairness paradigm, 265n20
disruption, performance and, 261–62n18
dissent, inclusive culture and, 217
Dittmar, Amy, 168
diverse teams: characteristics of successful, 221–22; complex tasks and, 209–10; creating, 211–13; culture supporting, 3, 4; empirical studies of performance and, 171–74; potential for diversity bonuses and, xiv–xv, 131, 225–26. See also teams
diverse workforce: financial service firms and, 206; Microsoft and, 205
diversity: ability vs., 105; acquired, 54; behavioral, 54; common good and, xiii; costs of, 172–73, 212–13; credit for recombination and, 112–13; culture supporting, 5; defining, 226–28; disagreement over value of, xi–xiii; excellence and, 9–10; informational, 54, 171, 226–27; inherent, 54; knowledge, 171; knowledge integration and, 113–16; motivation for, xv–xvi; necessity of proving benefits of, 243–44; performance and, 7; personality, 54; prediction and, 72–75; presentation on, 1–2, 4–5; reasons for promotion of, 185; skill, 171; social category, 54, 171, 172, 226–27, 228, 233; too much information and too much knowledge and, 23–24; value, 54. See also business case for diversity and inclusion; cognitive diversity; identity diversity; organizational diversity, value of
Diversity 1.0, Diversity 2.0, Diversity 3.0, 266n27
diversity and inclusion programs, 209–22; bias reduction, 213–16; inclusive cultures, 216–18; people or team analytics, 218–20; practice and, 211–13; progress and practice, 221–22; six Ms, 210
diversity and inclusion statements, 5
diversity bonuses, xiv–xvii, 2, 13–51; adopting approach seeking, 198–99; assumptions for emergence of, 244–45; behavior producing, 5, 6; business case as multiple tasks, 120–23; categorization and, 62–63; cognitive repertoires and, 53, 54–56; complexity and, 27–28; complex tasks and, 15–24, 68, 221; conditions for, 37–48; creative tasks and, 85–96; daily bonuses, 48–49; diverse teams and potential for, 225–26; diversity prediction theorem and, 73–75; domain-specific bonuses, 123–30; evidence for team diversity and, 174–82; financial service firms and, 206; growing belief in, 187–88; heuristics and, 59; hiring practices and, 3–4, 8–9, 13–14, 124; identity and cognitive diversity and, 24–27; (inapt) portfolio analogy, 28–31; innovation and, 111–13; in knowledge economy, 1, 7–11; knowledge integration and, 113–16; Netflix Prize example, 31–37, 50–51; originating, 49; predictive tasks and, 70–85, 173–74, 183; problem solving and, 96–108; relevance of, 10; scientific research and, 50, 108–10; situational factors important for capturing, 237–43; social justice and, 6–7; strategic play and, 116–20; toolbox model and, 27–28, 99–105. See also business case for diversity and inclusion; frameworks; identity diversity bonuses
diversity-bonus logic, 68–69, 130–32, 161; cognitively diverse team performance on complex tasks and, 195–96; connection with business case, 199–205
diversity fatigue, 4
Diversity LSAT, 114–16
diversity prediction theorem, 72–75, 81, 82, 247–49, 254n10
DNA sampling, 257n55
domain-specific bonuses, 123–30
Dow, Willard, 97
Draper, Foy, 195
DREAMers, xiii
Dreamliner airplane, design of, 46–47
drug discovery, diversity bonuses and, 125–26
Dutch, tallness among, 137, 258n7
Dyson, Frank Watson, 70
earnings before interest and taxes (EBIT), executive board diversity and, 165
East of Eden (Steinbeck), 175
eBay, 214
econometricians, 102
economic growth, problem solving and, 96
Economist (magazine), 4
Eddington, Arthur, 70
Edison, Thomas, 39–40, 50, 175
education, categories used and, 80
Eli Broad Institute (MIT), 78–79
Eliot, T. S., 6
Ellison, Ralph, 51
Ely, Robin, 265n20
emergency room doctors, diversity bonuses and, 48
Emerson, Ralph Waldo, 159
emojis, designing, 87
energy usage and transmission improvements, potential diversity bonuses and, 47–48
engineering, use of teams in, 176, 177, 178, 263n45
equity funds, success of team-run, 178
error, diversity prediction theorem and, 72–74, 247–48, 249
essentialism, identity and, 136–37
ethnic diversity, financial performance and, 165
ethnicity, 136
evaluation tasks, cognitive repertoires and, 55
evidence of cognitive and identity diversity benefits, 162–83; causality, 167–71; fifty years of empirical research, 171–74; growth of teams, 162–63; overview, 164–66; team performance, 174–82
experience: creativity and richness of, 86; prediction and diverse, 76–77
experimental studies, evidence for diversity bonuses from, 165, 171–74
Facebook, 194
facts, mental models and, 65–66
fame problem, 87
farnesyltransferase (FTase) inhibitor tasks, 92–93
Federal Open Market Committee, 70
Federal Reserve, 188
Fierke, Carol, 92
financial service firms: cognitive diversity and, 186; diversity and inclusion and, 205–7
Fischer, Franz, 97
Fisher case, 197
Fitzgerald, F. Scott, 175
fluid intelligence, 143–44, 173–74
folk medicines, drugs derived from, 125, 126
folk models, 64
Ford, Henry, 93
Ford Foundation, 267n6
Ford Motor Company, 70, 84, 148, 197, 198, 265n11
Foresight Group, 22
frameworks: cloud, 11, 134, 142–43, 149; iceberg, 11, 134, 138–40; mental models and, 63–66; timber-framed house, 11, 134, 140–42, 148, 261n14
Freeman, Richard, 179
friendships, identity groups and, 144
functional background diversity on teams, 237–43
Furman, Jill, 211
games, strategic play, 116–20
Gardipee, Flo, 257n55
Gardner, Howard, 53
Gawande, Atul, 59
gender: attitudes toward risk and, 192; behavior and diversity in, 135; board diversity and, 165–66, 167–71, 172, 204–5, 261nn14, 16; cognitive repertoires and, 240; disparities in education and employment and, 193–94; essential characteristics and, 136
gender identity, 136
General Electric, 84
General Mills, 190–91
genotype identification models, 78–79
geography, identity and, 151
ghost atoms, 109–10
Glenn, John, xi
GM (General Motors), 198
goals, cross-functional teams and common and clear, 241
Golgi apparatus, analogies for, 135
Good Judgment Project, 173
Goodyear Tire Company, 187
Google, 175, 207; corporate blunder, 191; diversity initiatives at, 209; diversity of nationalities and, 265n11; emphasis on cognitive diversity and, 186; hiring of Vint Cerf, 219; hiring practices, 9, 95, 187, 188; identity attributes and searches, 151; inclusive culture and, 217; lack of minority employees, 194; prediction and, 70; prediction markets and, 84; reliance on teams, 217; upside-down video uploads, 144–45
Google Ads, 147
Google Translate software, 81
Grand Prize Team, 35
Gravity (film), 151–52
Grossmann, Marcel, 175
groups: composition of high-ability, 104–5; creativity of, 88–89; features of predictive, 74–75; to make iterative improvements, 106–7; role of identity diversity in, 228–29; special understandings of identity, 144–45; as union of repertoires, 66–67. See also diverse teams; homogeneous groups; teams
group selection: identity diversity and, 154–56; no test exists for, 95–96
group size, ability measure and, 256–57n51
Hamilton (musical), 211
Hanks, Tom, 152–53
Hastings, Reed, 31
Haubegger, Christy, 159–60
Hayek, Friedrich A., 75
Hemingway, Ernest, 175
heuristics: cognitive repertoires and, 58–59; defined, 58; identity and, 193; problem solving and combining, 107–8; scientific research and, 108, 110
Hidden Figures (film), xi, 195, 218
high dimensionality in tasks, 46–48
hiring: by ability, 98; bias reduction and, 213–14, 215–16; CEO, 119–20; diversity bonuses and diversity in, 3–4, 8–9, 13–14, 124; at financial service firms, 206; growing belief in hiring for diverse cognitive repertoires, 187–88; International Monetary Fund and diversity in, 103–4; for routine tasks, 45–46; rubrics for, 9, 188, 214; for teams, 219; using teams for, 188
homeland security industry, cognitive diversity and, 186
home mortgage crisis, lack of identity diversity on Wall Street and, 149–50
homogeneous groups: diversity and, 149–50; familiarity and trust and, 45; performance of, 211
Huang, Wei, 179
human egg fertilization, 153–54
Hurwicz, Leo, 110
Hussein, Saddam, 123
IBM, 207
iceberg framework, 11, 134, 138–40
idea generation, creativity and, 89, 93–94
identity: categories of, 136–38; links between knowledge and, 90–93
identity attributes: causal effects of manipulating race and gender, 167; inseparability of, 141–42
identity-based opportunity bonuses, 157–60
identity-bundle-to-cognitive-repertoire mapping, 150
identity diversity, 2, 133–61, 226; assumption of cognitive diversity from, 230–36; benefits of, 228–37; business case for, 184–85; categorization and, 80; the cloud analogy, 134, 142–43; contributions and, 5; defined, 15, 133–34; functional differences and, 230; iceberg analogy, 134, 138–40; identity-based cognitive differences by task and, 150–61; identity primer and, 136–38; linked to cognitive diversity, 9–10, 11–12, 24–27, 134–36, 143–50, 238–41; overview, 133–36; perspective taking and, 172; political discussions and, 130; resistance to increasing, 236–37; routine tasks and, 172; scientific research and, 153–57; as source of cognitive diversity, xvi, 229–30; team performance and, 11–12; timber-framed house analogy, 134, 140–42; truth verification and, 115–16. See also evidence of cognitive and identity diversity benefits
identity diversity bonuses, 224–25; cognitive repertoires and, 192–93; organizations and, 188–92. See also diversity bonuses
identity groups, special understandings of, 144–45
IDEO, 191
IHOP, 191
immigration, debate over, xii–xiii
implicit bias, identity-diverse groups and, 188
inclusion: bottom-up, 5; cognitive diversity and, 25; type of tasks and, 3. See also diversity
inclusive cultures, 216–18
indecomposability of complex tasks, 46–48
inferences: categorization and, 62; cloud framework and, 142–43
information: cognitive repertoires and, 56–57; defined, 56; identity and collection of, 145–49; knowledge vs., 57; models and, 65; prediction and, 75–81
informational diversity, 54, 171, 226–27
in-group, social category diversity and, 228
inherent diversity, 54
innovation: cognitive repertoires and, 55; diversity bonuses and, 111–13; identity-based opportunity bonuses and, 157–60
institutional discrimination, 267n5
integration problems, solving, 98–99
Intel, 217
intelligence: crystallized, 143–44; fluid, 143–44, 173–74
Intelligence Advanced Research Projects Activity, 173
intelligence analysis, 262n27
intelligence community, identity diversity bonuses and, 189–90, 265n9
interactions, identity diversity and, 160
International Monetary Fund (IMF), potential hires, 103–4
International Panel on Climate Change, 70, 78
Internet, 111; as information source, 56–57
intersectionality theory, 141–42
inventions, problem solving and, 96–97, 98–105
investment portfolios, diversity bonuses and, 28–31
Invisible Man (Ellison), 51
iPod, 85
IQ scores, 52–53
ISIS, xiii
Issacs, Ryan, 257n68
iterative improvement, problem solving and, 105–7
jobs, categories of, 39–40
Jobs, Steve, 175
John S. and James L. Knight Foundation, 267n6
Johnson, James, 130
Johnson, Katherine, 218
Johnson, Magic, 212
Jolie, Angelina, 151
Juicy J, 163
Jurvetson, Steve, 124–25
jury decision making, racial diversity and, 233
Kaggle, 37
Kauffman, Stuart, 61
Knight, Jack, 130
knowledge: cognitive repertoires and, 57–58; defined, 57; identity and collection of, 145–49; information vs., 57; links between identity and, 90–93
knowledge diversity, 171
knowledge economy: diversity bonuses and, 1, 7–11; people and, 199–203
knowledge integration, diversity bonuses and, 113–16, 257n68
knowledge structures: in mathematics, 19–21; on obesity, 22–23
Knox, Grahame, 113
Koren, Yehuda, 36
Kotecha, Savan Harish, 163
Kozlowski, Steve W. J., 179
Kresge Foundation, 267n6
Kristof, Nicholas, xii–xiii
Krysztofowitz, Wladislaw, xii
Laserphaco Probe, 157
Latina (magazine), 160
Latinas, mathematics and science careers and, 26
Latinos, disparities in education, income, and employment and, 193–94
law school admissions: affirmative action in, 197, 266n22; identity diversity and, 128–29
leadership support, for cross-functional teams, 242–43
learning curves, 44–45
Levin, Richard, 71
Lewis, Earl, 195
liberal model, 66
Liljenquist, K. A., 234–35, 236
Lincoln, Abraham, 13–14
literature, identity diversity and interpretation of, 129–30
logic problems, Diversity LSAT, 114–16, 257n68
Los Angeles Lakers, 211–12
Lost at Sea exercise, 113–14
machine learning algorithms, 70–71; artificial intelligence and, 81–83
managing teams to achieve diversity bonuses, 210
manual nonroutine tasks, 39, 41, 42, 43, 45
manual routine tasks, 39–42
mapping outcomes to cognitive repertoires, 55
marketers, identity diversity bonuses and, 191
markets: allocation of goods and, 38; prediction, 83–85
Marshall, Thurgood, 213
mastery of craft, 43–45
mathematics: identity diversity and cognitive diversity in, 24–26; relationships between topics in, 20–21
May, Theresa, xii
Mazda Miata design, 127
McKee, Bonnie, 163
McKinsey, 217; analyses of management teams, 165
McNamara, Robert, 252n1
measurement: of creativity, 86–87; of diversity, 255n29; of performance, 210
Medici effect, 93–95
medicine: diversity bonuses and practice of, 48–49; drug discovery, 125, 126; practitioner identity diversity and, 191–92; surgeons and clusters of tools, 102; surgical intervention predictions, 71
Melville, Herman, 129
mental models, 63–66; identity and, 192–93; prediction and, 75–81; tradecraft and, 123
mentors for underrepresented employees, 210
meringue making, mastery of, 43–44
merit, tying inclusive behaviors to, 210
meritocracy: diversity and, 206–7; of ideas, 217
meritocratic fallacy, 128
message of diversity and inclusion from the top, 210
Metcalfe, Ralph, 195
Michelangelo, 175
Microsoft Corporation, 204, 207
Middle East, discussion of politics in, 130
Milton, John, 23–24
Milton problem, 102
Miranda, Lin-Manuel, 211
Mismatch (Sander & Taylor), 266n22
mission: of business or organization, 184; of for-profit entities, 187; linking diversity to, 210; of team, 176; of universities, 186–87
Moby Dick (Melville), 129–30
model aggregation procedures, 36
models, 2; data and, 254n1; defined, 64; folk, 64; prediction and causal, 70–71; scientific research and, 108–10; strategic play and diversity of, 116–20. See also mental models
mood ring, invention of, 85, 255n22
Most Creative Group Problem, 88–89, 95–96
motivation for diversity and inclusion, xv–xvi. See also business case for diversity and inclusion
movie ratings, predicting consumers’. See Netflix Prize competition
Mozart, Wolfgang Amadeus, 175
MTV, 158–59
multiple intelligences, 53
NASA, xi, xiv, 70, 154, 155, 156, 186, 195, 209
National Academy of Sciences, 21, 176
National Basketball Association, 211
nationalism, xii
National Science Foundation, xv, 26, 182
natural experiment, gender requirements for Norwegian boards of directors, 167–71
negative correlation, 30
negative externalities, markets and, 38
neonatal intensive care units (NICUs), 48–49
nepotism, 214
Netflix Prize competition, 31–37, 50–51, 75, 78, 173, 253n11
Newton, Isaac, 175
New York Times (newspaper), xii
Nivet, Mark, 266n27
nonroutine tasks: cognitive, 39, 40–43, 218; diversity bonuses and, 39, 42; manual, 39, 41, 42, 43, 45
normative arguments for diversity and inclusion, 193–95, 197–98, 200–203
Norwegian boards of directors, gender requirements for, 167–71, 204, 261n14
no test exists results, 95–96, 104, 171, 219
not half-bad rule, 81
Nuclear Regulatory Commission, 218
Obama, Barack, 260n34
obesity epidemic, diversity and study of, 22–23, 24, 131
O*NET survey, 260n9
Opera Solutions team, 35
O’Reilly, Charles, 237
organizational culture supporting diversity, 186
organizational diversity, value of, 223–45; benefits of identity diversity, 228–37; defining diversity, 226–28; situational factors, 237–43
organizations: identity diversity bonuses and, 188–92; support for cross-functional teams, 242–43; support for diversity and inclusion, 186–87
originating diversity bonuses, 49
Our Compelling Interests: The Value of Diversity for Democracy and a Prosperous Society (Lewis & Cantor), xv
out-group, social category diversity and, 228
overfitting data, 33
Owens, Jesse, 195
panel, diversity bonuses and selection of, 127–28
parable of the bikes, 211
patents: creators’ cognitive diversity evidenced in, 180; recombinations predominating in, 112–13; success of team-generated, 175, 177–78
payoffs in games, 116
penicillin, discovery of, 125, 126
people and profits, diversity-bonus logic and, 199–203
PepsiCo, 207
Pérez, Armando Christian (Pitbull), 163
performance: causal relationship between diversity and, 167–71; disruption and, 261–62n18; diversity correlations with, 7, 11–12, 164, 165–67; functional background diversity and, 237–38; gender diversity and, 172. See also team performance
Perkins, Maxwell, 175
personality diversity, 54
perspectives: cognitive repertoires and, 59–62; defined, 60
perspective taking, cognitive and identity diversity and, 172
phantom islands, 110
Phillips, Katherine W., xv, 231–32, 234–35, 236. See also organizational diversity, value of
physical capabilities, identity and, 136
physical world, iceberg model and, 139
PIMCO, 213
plant patents, 112
play, strategic, 116–20
polar coordinate system, 60–61
politics, identity and cognitive diversity and discussions of, 130
Pop, Denniz, 163
portfolio analogy, diversity and, 28–31
possibilities, creative task and set of, 85
practice in creating and supporting diverse teams, 211–13
Prada, 94–95
pragmatic theory team, 34
Pratt, Wes, 5
prediction: analytic tradecraft and, 123; cognitive diversity and, 15; evidence of diversity bonuses, 162; innovation and, 111–13; models and, 65; strategic play and, 116–18; in venture capital evaluations, 124–25
prediction markets, 83–85
predictive diversity, 247–49
predictive groups, features of, 74–75
predictive tasks, 3; artificial intelligence, 81–83; cognitive repertoires and, 55; diversity bonuses and, 70–85, 173–74, 183; diversity prediction theorem, 72–75; information, categories, and mental models and, 75–81; portfolio analogy and, 30–31; predictive markets, 83–85
Prigogine, Ilya, 257n53
problem difficulty, diversity bonuses and, 105
problem solving, 96–108; calculating probability of, 256n43; cognitive diversity and, 15; cognitive repertoires and, 55; combining heuristics, 107–8; diversity bonuses and, 96–108; innovation and, 111–13; iterative improvements and, 105–7; strategic play and, 116; toolbox model and, 98–105
problem solving teams, portfolio analogy and, 30–31
product design, identity diversity bonuses and, 191
profits and people, diversity-bonus logic and, 199–203
psychological tests measuring creativity, 86–87
qualitative predictions, 80
quantitative predictions, 76–80
quinine, discovery of medical properties of, 125
race: casting decisions and, 152–53; cognitive differences and, 147–48; disparities in education and employment and, 193–94; social construction of, 137; workplace discrimination and, 213, 267n6
race identity, 136
racial categories, 137
racial diversity, 135; economic performance and, 166; effect on jury decision making, 233; O*NET survey, 260n9
radical candor, inclusive cultures and, 217
Rand Corporation, 36
random forest algorithms, 81, 82
Raphael, 175
Reagan, Ronald, 124
real estate market, negative externalities and, 38
realist model, 66
recombinations: creativity and, 85–86; Medici effect and, 93–95; predominating in patent data, 112–13
references, cognitive diversity and academic paper and patent, 180–82, 263–64n48
REI, 184
representation: benefits of diverse, 229, 230; cognitive repertoires and, 59–63; scientific research and, 108–10; tradecraft and, 123
reproducibility, of experimental studies, 171, 262n19
return on equity (ROE), executive board diversity and, 165
Ride of the Valkyries (Wagner), 153–54
risk, gender and attitudes toward, 192
Romney, Mitt, 152
Roosevelt, Franklin Delano, 196
routine tasks: cognitive, 39, 41, 43, 45, 195; diversity bonuses and, 39–40, 42, 45; diversity imposing costs on, 45; identity diversity and, 172; manual, 39–42
rules of thumb, 58, 59; not half-bad rule, 81. See also heuristics
Sadoway, Donald, 98
Sandberg, Carl, 10
Sandberg, Martin (Max Martin), 163, 164
Sandberg, Sheryl, 199
Sander, Richard, 266n22
Sanger Institute (Harvard), 78–79
Scalia, Antonin, 196
Schembechler, Bo, 263n33
science, Summers on women in, 240
scientific research: diversity bonuses and, 50, 108–10; identity and, 135; identity diversity and, 153–57; teams and, 50, 175, 176, 177, 178, 179–82, 263n45. See also academic research; social science research
Scott, Diego, 191
Scott, Kim, 217
scripts, Kaggle and, 37
sculling, learning, 156
segregation, xi
Seller, Jeffrey, 211
semiroutine tasks, diversity bonuses and, 43–45
service sectors, identity diversity and, xv
sexual orientation, as identity category, 136
Silicon Valley, diversity and, xiv
Simon, Herb, 120
single-attribute mapping, 146
“The Sins of Kalamzoo” (Sandberg), 10
situational factors, diversity bonuses and, 237–43
skill diversity, 171
Slinky, invention of, 111
Smith, Patti, 175
social category diversity, 54, 171, 172, 226–27, 228, 233. See also identity diversity
social construction of race, 137
social identities, team diversity and, xiv
social justice: diversity and inclusion and, 2, 6–7; diversity bonus and, 196, 197–98, 200, 201–2; financial service firms and diversity and, 206
social network effects, bias and, 214
social relationships, identity diversity and motivation to maintain balance in, 234–35
social science research: relationship between models and data in, 254n1; teams and, 176, 177, 178. See also academic research; scientific research
Sommers, S., 233
songwriting, use of teams in, 162–63
sparse data, 32
Springsteen, Bruce, 175
Sprint, 191
state of the world, 29–30
status differences, cross-functional teams and diminishing, 242
status quo, business case for diversity and power of, 226
Steinbeck, John, 175
STEM, workforce diversity and, xv–xvi, 194
stereotype threat, identity-diverse groups and, 188
stereotypical identity-based cognitive differences, 145–49
storycentric design, 191
strategic play, diversity bonuses and, 116–20
strategizing tasks, cognitive repertoires and, 55
Streep, Meryl, 153
subadditivity, creativity and, 94
Summers, Larry, 240
superadditivity: creativity and, 94; improvements and, 107; recombinations and, 85–86
superforecasters, fluid intelligence and, 173–74
surgical intervention predictions, 71
synthetic oil process, 97
Tacitus, Cornelius, 90
tasks: classifying, 3, 39, 40; identities influencing how accomplished, 161; identity-based cognitive differences by, 150–61; inclusion and type of, 3; semiroutine, 43–45; strategizing, 55. See also complex tasks; creative tasks; predictive tasks; problem solving; routine tasks
Taylor, Stuart, 266n22
team analytics, 218–20
team performance: cognitive diversity and, 11–12, 179–82; empirical studies of, 171–74; identity diversity and, 11–12
teams, 174–82; Bourke model of decision making and, 120–23; cognitive repertoires and diversity of, 8, 53; complex tasks and diversity of, 17–23; cross-functional, 237–43; evidence for diversity bonuses and, 164–65; growth of, 162–63, 174–77; hiring decisions using, 188; hiring for, 219; individual cognitive tools and diversity of, 15–19; knowledge economy and, 11; selecting best performing, 13–14; success of, 177–78; toolbox model and, 99–101; use in engineering, 176, 177, 178, 263n45; use in scientific research, 50, 176, 177, 178, 179–82, 263n45. See also diverse teams; groups
technology, economic growth and, 96
testing data, 35
testing set, 33
theory, for diversity bonuses, 37–38
thinking, identity and, 135
thinking hats, 120
Third International Gender Innovation Summit North America, xv–xvi
Thomas, David, 265n20
three-point shot, in professional basketball, 211–12
timber-framed house framework, 11, 134, 140–42, 148, 261n14
Tom Hanks Is Busy experiment, 152–53
toolbox model, 15–19, 108; complexity and diversity bonuses and, 27–28; problem solving and, 98–105
tool clusters, 102–5
too much information (TMI), 23
too much knowledge (TMK), 23
tourist problem, 86–87
track-and-field relay team, as no-bonus team, 194–95
tradeoff framing, 196–97
training set, 33
Tropsch, Hans, 97
Trump administration, 199, 200
trust: community of communities and, xv–xvi; homogenous teams and, 45; on teams, 176; types of tasks and, 3
truth: discerning, 3; identity diversity and verification of, 115–16; tradecraft and, 123–24
Twitter, 194
UCLA, admissions at, 95
US Patent and Trademark Office, 112
U.S. Steel, 216
US Supreme Court: on affirmative action, 196–97; nominees to, 124
universities: affirmative action in admissions, 265–66n22; diversity and inclusion statements, 203; supporting diversity and inclusion, 186–87
University of California–Los Angeles, 8, 9
University of Delaware, 148
University of Michigan, 79; affirmative action cases at, xiv, 196–97; hiring using teams at, 188; mission of, 184
University of Wisconsin–Madison, mission statement, 186–87
utility patents, 112
value diversity, 54
Van Buskirk, Eliot, 37
Vandelay Industries team, 35
Vaughan, Dorothy, 218
venture capital, evaluating startups, 124–25
verification tasks, cognitive repertoires and, 55
Verizon, 190–91
Vermont Oxford Network, 48–49, 50
virtual world, iceberg model in, 139–40
Volinsky, Chris, 36
W. K. Kellogg Foundation, 267n6
Wagner, Richard, 153–54
Wall Street, diversity and inclusion on, 205–7
Wal-Mart, 203
Walter, Henry Russel (Cirkut), 163
Washington, Denzel, 152–53
Waste Management Corporation, 203–4
weak learners, 81
well-mixed systems, 108–10
Wernick, Marvin, 255n22
William Davidson Foundation, 267n6
Wolfe, Thomas, 175
women: board diversity and, 165–66, 167–71, 172, 204–5, 261nn14, 16; mathematics and science and discrimination against, 25–26; Summers on women in science, 240
workforce, classification of, 39–42
workforce diversity: lack of and need for, 193–95; STEM firms and, xv–xvi, 194
workplace discrimination, race and, 213, 267n6
Worthy, James, 212
Wozniak, Steve, 175
Wright brothers, 175
Wykoff, Frank, 195
YouTube, 144