INDEX

Aadhaar, 125, 127–128, 135

Abrahamson, Shaun, 147

abundance mindset, 33–34

Accela, 147

accountability, 112, 209, 216, 217

adaptive partial approval, 105–109

Adobe, 127

agility, 40–41, 214

Airbnb, 9, 91–99, 104–105, 120, 121, 214

Amazon, 10, 76, 136, 152–153

Amazon Marketplace, 120, 122, 126

Amazon Web Services, 119, 136

ambidexterity, 219–220

Amsterdam, 91–99, 104–105

Andreessen Horowitz, 147

Android, 119, 192

Apple, 181, 192, 193, 198, 213

application programming interfaces (APIs), 134, 177, 198

apps, 14

See also specific apps

benefits-enrollment, 45–46

Covid-19 tracing, 11, 183–199

Ardern, Jacinda, 7

Arrow, Kenneth, 211

artificial intelligence (AI), 3, 16, 99–100, 140–141, 152

automation, 99–100, 151

autonomous vehicles, 99–104, 107–110

bad ideas, 65–67, 217–218

Baptiste, Stonly, 147

Bassin, Ian, 172–174, 176

Bates, Tambrein, 24–25, 29

Bay, Jason, 11, 181–182, 185–197, 199

Beaumont, Kevin, 161

benefits-enrollment app, 45–46

Benkler, Yochai, 13, 60

Berkovich, Gai, 115–118, 137

Berkshire Partners, 147

best practices, 34–36, 40, 70

Bezos, Jeff, 152

BigApps competition, 14, 63

big-bang adoption strategy, 126, 127

bike sharing, 133–134

Bill & Melinda Gates Foundation, 210

Binagwaho, Agnes, 15

bin Laden, Osama, 28

Biobot Analytics, 146

Black Lives Matter, 123

Blank, Steve, 213–216

blockchain voting, 158, 159, 165–171

Bloomberg, Michael, 14, 224

Bloomberg Philanthropies, 76, 210

blue rules, 223

Bluetooth handshakes, 190

booth capturing, 159

Borrmann, Rudi, 145

Boston, 12–13, 107–108

Boston Marathon attack, 1–3, 35, 55, 224

Boston Strong, 132

Boston Women’s March, 176

Bouganim, Ron, 146–147

Bracken, Mike, 144

brainstorming, 57–58

Brandeis, Louis, 200

Brenner, Clara, 147

Bridgespan, 208, 210

Brin, Sergey, 129

Brown, Tim, 32, 48

budget process, 221–222

build-measure-learn method, 85–88, 105

bureaucracy, 29, 214, 215

bureaucrats, 31

California Public Employees Retirement System (CalPERS), 212

Cambridge Analytica, 135

Can Democracy Work? (Miller), 162

Cantillon, Richard, 30

Carter, Ash, 152

Carter, Melvin, 18, 44–45, 53, 55, 223–224

Carvalho, Alexander de, 148

Case Method Institute, 174–175

Chan, Priscilla, 209

change

See also innovation

making happen, 223

rate of, 36–37

Chari, Vasant, 48

Chen, Jimmy, 43–50, 56, 66, 67, 200

Chesky, Brian, 94

China, 16, 164

choices, creating, 32–34

citizen-centered design, 46–50

citizens, possibility, 70, 112, 178

Civic Lab, 175, 176

Clark, Ralph, 146

Clickworkers project, 13, 60

Clinton, Bill, 11–12

cloud computing, 16

Coats, Dan, 164, 165

Code for America, 14, 145

Coding It Forward, 145, 220

Communications Decency Act, 98, 109

community policing, 123, 124

competency trap, 204

competitions, 58

complementary products, 124

Connected Citizens Program (CCP), 115–119, 128–130, 135

Consumer Financial Protection Bureau, 143–144

contact-tracing protocols, 181–199

contests, 58, 59, 60

contextual intelligence, 154

convergent thinking, 32–33, 34

Cook, Tim, 193

Covid-19 pandemic, 18, 56, 60, 181–183, 199–200

Covid-19-tracing app, 11, 183–199

creativity, 8, 25

crises

anticipating, 18

responding to, 19, 38

cross-side network effects, 121

crowdsourcing, 13–14, 44, 58–60, 73–74, 77

cyberthreats, 165, 166

Data.gov, 14, 84–85

data security, 188, 195

de Blasio, Bill, 49, 160

December 8th problem, 40–41

De Coster, Tanja, 97

Defense Advanced Research Projects Agency (DARPA), 24–25

delusion, 7–8, 182, 200–205

democracy, 10, 16, 151, 157–161

attacks on, 164–166

definition of, 174

dire data on, 162–164

entrepreneurship, 170–176

network effects of, 169–170

private foundations and, 209–210

spread of, 169–170

startups, 174, 175–176

democratic recession, 163

“demosclerosis,” 29

Denrell, Jerker, 216

design

thinking, 46–50, 57

user-driven, 50–55

desire lines, 53–54

DeWine, Mike, 60

Diamond, Larry, 163

Dickerson, Mikey, 142, 143

digital technology, 12–14, 16

See also tech sector

Dillard, Sarah, 83, 85

direct-giving movement, 56

direct network effects, 121, 131

disability-rights movement, 54

discrimination, 205

divergent thinking, 32–33

Dodell, Rachel, 145

donations

See also philanthropy

direct, 56

distribution of, 2, 35–36, 55–56

Downes, Patrick, 132

drones, 26, 28, 149–150

Dropbox, 83

Drucker, Peter, 6

drug-approval process, 105–106

Ducey, Doug, 101

eBay, 126–127

Ebola, 184

economic elites, 162–163

Eefting, Albert, 93, 95, 96

e-government, 12

18F, 48, 143

Eisenmann, Tom, 82, 83, 85

Eisnor, Di-Ann, 117, 129

elections, foreign intervention in, 164–165, 171

electronic voting, 166

See also mobile voting

empathy, 44, 47

end users, designing for, 50–55

entrepreneurs

as opportunity driven, 30–32, 39

private, 208

in public office, 10

risk-taking by, 87–88, 208, 215

success and failure of, 215

trisector, 220

entrepreneurship, 6

See also startups

democracy, 170–176

hypothesis-driven, 85

as learned, 30

possibilities and, 5

public, 4–8, 15, 17, 19

redefining, 30–31

trisector, 139–156, 178

European Union, 12, 195

evangelism strategy, 126, 127

expectations, 224

experimentation, 73–89, 105–106, 222

experts, 37, 44, 56

exploitation, 218

exploration, 218

externalities, 208

extreme-value theory, 58

Eyoel, Yordanos, 175–176, 200, 203

Facebook, 4, 13, 109, 120, 121, 122, 127, 135, 141

facial recognition software, 152–153

Fahey, Katie, 175

failure

avoidance of acknowledging, 74

by entrepreneurs, 215

platform, 132–134

punishment for, 216–217

seeing too soon, 204

of startups, 5, 83

Farmer, John Paul, 145

Farrahi, Katayoun, 187

Feinberg, Ken, 55–56

Fitzgerald, Paige, 117, 128–130, 135, 200

FixMyStreet, 14

Florence, Justin, 172

Floyd, George, 18

flyboards, 23–24, 26, 38–39

“follow the rabbit” strategy, 126

foolishness, 67, 205

foreign intervention, in US elections, 164–165, 171

Frank, Leila, 95

Freedom House, 163–164

free-riding, 208

Fresh EBT app, 56

Gaebler, Ted, 6, 11, 217–218

Gandhi, Indira, 158

Gates, Bill, 209

gedogen, 92, 95–96, 105

Geiger, Emily, 61

General Catalyst, 147

General Data Protection Regulation, 195

Generation Z, 3

Geurts, James, 24–29, 33–34, 36–42, 200, 203, 204, 225

Ghaeli, Newsha, 146

Gilens, Martin, 162

GiveDirectly, 56

Gómez-Mont, Gabriella, 75–77, 80–81, 85, 88, 105, 200, 224–225

Goodman, Carolyn, 199

Google, 12, 116, 117, 119–120, 128–129, 140–141, 152, 181, 198

Gore, Al, 11, 12

GovDelivery, 147

government

budget process, 221–222

experimentation in, 73–89

hot-stove, 216–217

investment in innovation by, 212–214

lack of faith in, 3, 4, 89, 176, 224–225

open, 14

organization of, 219–220

as a platform, 115–137, 169–170, 177

Possibility Government, 4–11, 19, 78–80, 183, 207–208, 218

Probability Government, 4, 78–80, 218

product managers in, 221

regulation, 91–110, 153

reinventing, 11–12

risk-taking by, 9, 211–215

selling to, 149–151

solving of problems by, 1–19, 207–217

startups and, 151–152, 214

tech and, 4, 12–14, 140–154

wiki, 44

Government Digital Service (GDS), 140, 144

GovTech Singapore, 11, 181, 183–198

GOV.UK, 144

Granicus, 147

H1N1, 184

habitat fragmentation, 148

hackathons, 61–66, 214

Hacking Heroin, 61–66

Hagensen, Carl, 27

Halderman, Alex, 170

Hall, Joseph Lorenzo, 165–166

Harris, Kamala, 144

Harvard Business School, 15, 52–53, 148

Hatch, Orrin, 4

HealthCare.gov, 140–143

Highland Capital, 147

Hinton, Anthony Ray, 55

honesty, 89

hot-stove government, 216–217

Houston, Drew, 83

How Democracies Die (Levitsky and Ziblatt), 162

human-centered design, 47–50

humility, 205

hypothesis-driven entrepreneurship, 85

ideas

bad, 65–67, 217–218

during Covid-19 pandemic, 182–183

creating, 32–34

execution of, 203–204

insider vs. outsider, 59–60

looking for, among nonexperts, 23–25

new, 8–9, 29–30, 41, 44–45, 57–67

politics of, 203

scaling successful, 9–10

super, 58–59

trying new, 9

from users, 50–55, 57

volume of, 57–60

IDEO, 32, 46–47, 51, 57

Immigration and Customs Enforcement (ICE), 152

India, 10, 124–125, 127–128, 135, 158–159

indirect network effects, 121

informal networks, 31

information economy, 11–12

information technology, 16

See also technology

InnoCentive, 60

innovation, 12–13

experimentation and, 73–89, 105–106, 222

by government, 212–214

investment in, 210, 212–214

teams, 219

user-driven, 50–54

innovation labs, 76

innovation platforms, 119, 124

insider ideas, 59

internet, 12

internet voting, 166

iOS, 119, 192

iPhone, 192

Ive, Jonathan, 48

Jacob, Nigel, 13, 220

Jobs, Steve, 14, 29

Josephine, 215

Kalanick, Travis, 160, 214

Kanter’s Law, 204

Kek, Joel, 188, 191, 197

Kelley, David, 46–47, 51

Kelley, Tom, 51

Kemp, Brian, 199

Kennedy, Robert, 7

Kersey, Donald, 168–169

Kickstarter, 127

Kim, Jini, 142

King, Martin Luther, Jr., 18

Kok, Ping Soon, 181–182, 188–189, 191

Koppel, Ted, 46

Korski, Daniel, 148, 198–199

Kuang, Chris, 145

Lab126, 76

LabCDMX, 75–77, 80–81, 85

Laboratorio para la Ciudad. See LabCDMX

Lambertsen, Christian, 28

leaders

ambidextrous, 219–220

possibility, 69, 111–112, 177–178

trisector, 154–156

leadership, styles of, 31–32

lean methodology, 81–85, 87–88, 144

lean startups, 75, 82–85

Lein, Julie, 147

Levine, Uri, 118

Levitsky, Steven, 162, 163

Levy, Steven, 144

Lind, Robert, 211

LinkedIn, 13

Loong, Lee Hsien, 189

Lovegrove, Nick, 154

Lyft, 120

lying, 74–75, 88

managers, 31–32

Mancera, Miguel, 73–77

Manski, Charles, 105–106

Mapatón, 73–77, 80–81, 85

March, James, 66–67, 182, 203, 205, 216, 218

Mark43, 147

market failures, 208

marquee strategy, 126, 127

Matus, Mariana, 146

May, Jesse, 118

Mayer, Marissa, 155

Mayor’s Office of New Urban Mechanics, 13

Mazzucato, Mariana, 212–213

McGrory, Brian, 13

Menino, Thomas, 12, 13, 15, 29, 30, 32, 35–36, 41, 86

Merkel, Angela, 7

MERS, 184

Mexico City, 9, 73–77, 80–81

Meyer, Erie, 143–144

micromarket strategy, 126

Microsoft Azure, 136

Microsoft Windows, 119

military, 5, 25, 28, 37, 39, 150–152, 166, 214, 221

millennials, 3, 16, 162

Miller, James, 162–164

Mindlab, 76

minimally viable products (MVPs), 83, 87

Mobike, 133

mobile phones, 14, 45, 159–160

mobile voting, 10, 157–161, 165–172

Modi, Narendra, 169

Mosaic, 12

Moss, David, 174–175, 211, 212

motives, 203

Mounk, Yascha, 162, 163

Musk, Elon, 213

Nakasone, Paul, 165

NASA, 13, 60

national government

See also government

lack of faith in, 3, 4

as platform, 124–125

National Health Service (NHS), 198

Naval Combat Demolition Units (NCDUs), 27

NavalX, 40

Navy SEALS, 28

necessity, as mother of invention, 18–19

neighborhood watches, 123, 124

Neighborly, 147–148, 151

Nesta, 76

Netflix, 66

Netscape Navigator, 12

network effects, 120–122, 131, 134, 169–170, 177

New Urban Mechanics, 13, 76, 220

New York City, 14

New York Stock Exchange, 110

NHS. See National Health Service (NHS)

Nilekani, Nandan, 124–125, 127–128

Nintendo, 127

Nix, Sheila, 160–161

nonprofits, 208–211

Normandy invasion, 27

North Korea, 164

“nothing about us without us,” 54–55

Not-Invented-Here Syndrome, 29

Noveck, Beth, 60

nuTonomy, 108

NYCx, 76

Nye, Joe, 154

Obama, Barack, 7, 12, 14, 84, 139–142, 220

O’Brien, Andy, 53

Office of Civic Innovation (San Francisco), 76

Office of Emerging Technology, 108–109

Office of Strategic Services (OSS), 28, 38

ofo, 133–134

One Fund Boston, 2–3, 55–56, 85–87, 130–132

OpenGov, 148

open government, 14

OpenTable, 127

opioid epidemic, 60–66, 146

opportunities

entrepreneurship and, 30–32

problems as, 23–42

Optimus Ride, 108

O’Reilly, Charles, 219

O’Reilly, Tim, 122–123, 132–133

Organization for Economic Cooperation and Development (OECD), 3

organizations, ambidextrous, 219–220

Osborne, David, 6, 11, 217–218

Osgood, Chris, 13

OSS. See Office of Strategic Services (OSS)

Ostrom, Elinor, 6

outsider ideas, 59–60

Page, Benjamin, 162

Pahlka, Jen, 14, 145, 223

Palantir, 152

Paley, Eric, 200

Panchadsaram, Ryan, 142, 143

PARC, 76

Park, Todd, 142, 143, 145, 220

partisanship, 163

Pate, Paul, 174

Pauling, Linus, 32

PayPal, 126–127

Peduto, Bill, 99–102, 104, 110, 200

pension funds, 212

The People vs. Democracy (Mounk), 162

pharmaceutical industry, 105–106

philanthropy, 208–212

piggyback strategy, 126–127

pilot programs, 222

Pittsburgh, 9, 99–102, 110

pivots, 83, 88

platforms, 115–137

definition of, 119

failure of, 132–134

as government, 135–137

government as, 122–133, 169–170, 177

innovation, 119, 124

network effects, 120–122, 131, 134, 169–170, 177

strategies for, 126–127

transaction, 119–120, 124, 131

types of, 119–120

users and, 119–122

platform thinking, 10, 118, 130

police brutality, 18

Policy Lab, 47–48

population scale, 10

possibility

vs. delusion, 200–205

at scale, 204–205

possibility citizens, 70, 112, 178

Possibility Government, 4–11, 19, 78–80, 82, 183, 207–208, 218

possibility leaders, 69, 111–112, 177–178

power, balancing, 5

presidential election (2016), 164–165

Presidential Innovation Fellows program, 143, 145, 220

privacy issues, 195–196, 197

private entrepreneurs, 10, 26, 140, 208

See also entrepreneurs

private foundations, 208–212

Private Kit: Safe Paths, 196

private sector, 79–80, 123, 146, 208, 213, 221

Probability Government, 4, 78–80, 218

problems

See also public problems

as opportunities, 23–42

understanding, 44

product development, 51

product managers, 221

Project Maven, 152

Propel, 45–46, 56

Protect Democracy, 171–174, 204

prototypes, 83

PS21, 76

psychological safety, 29

PUBLIC, 148

public budgets, 221–222

public engagement, 223–224

public entrepreneurship, 4–8, 15, 17, 19

public officials, 214, 216–217

public policies, 5, 162–163

public problems

private sector and, 208

role of foundations in solving, 208–212

solving, 1–19, 183, 207–217

surprise and, 37

understanding, 44

public safety, 123–124

public sector

See also government

employees, 79–80, 211, 221, 223

partnering with private, 210–211

talent crisis in, 3

PushBlack, 175

Putin, Vladimir, 3

racial injustice, 18

Reagan, Ronald, 7

regulation, 91–110

of Airbnb, 91–99, 104–105

of autonomous vehicles, 107–110

of technology, 153

of Uber, 99–102

Reich, Rob, 209–211

Reinventing Government (Osborne and Gaebler), 217

Reiter, Andrew, 149–150

Rent the Runway, 82–83

resources, 32, 41–42

Reynolds, Colleen, 61

Ricks, Karina, 100

ridesharing, 99–102

Ries, Eric, 75, 82, 83, 85, 213

risk aversion, 29, 78–80, 216–217

risk management, 211–212

risk-taking, 9, 29, 87–88, 102–104, 208, 210–213, 215

Rittgers, Annie, 60–67, 200, 203, 204

roadways, 122

Rodriguez, Diego, 47

Roosevelt, Franklin, 7

Russia, 164, 171

Rwanda, 15

Sadik-Khan, Janette, 53

Sahlman, Bill, 30

same-side network effects, 121, 131

Sandy Hook shooting, 2, 36

San Francisco, 108–109, 151–152

Santosham, Shireen, 145

SARS, 184

Sawhney, Nimit, 158–159, 161, 167–168, 170

scarcity mindset, 33, 34

Schippers, Nanette, 91, 92, 96, 104

school shootings, 2, 36

Schwartztol, Larry, 171–175

Scoggins, Clarissa, 187

Scott, Darrell, 175

SeamlessDocs, 147

SeeClickFix, 14

seed capital, 211

seeding strategy, 126, 127

self-driving vehicles, 99–104, 107–110

self-interest, 208

Seneca, 147

Sengeh, David Moinina, 145

SenseTime, 16

Shabtai, Ehud, 118

sharing economy, 91–99

Shield AI, 149–150

Shinar, Amir, 118

short-term rental regulations, 91–99, 104–105

ShotSpotter, 146

Singapore, 181, 183–198

single-side strategy, 126, 127

Siodmok, Andrea, 48

Sittenfeld, P. G., 61

smartphones, 14, 45

See also mobile phones

Smith, Adam, 51

social media, 13–14, 151

Social Security, 212

societal problems. See public problems

SOFWERX, 24–27, 36, 38

solutions

best practices and, 34–36

brainstorming, 57–58

motives for, 203

scaling, 117–118

sources of new, 8–9

Special Operations Command (SOCOM), 24–29, 38, 40

Special Operations Forces (SOF), 24, 26, 36–38

startups

See also entrepreneurship

democracy, 174–176

failure rate of, 5, 83

government and, 151–152, 214

lean, 75, 82–85

limitations of, 214–215

problem solving by, 207–208

risk-taking by, 215

talent in, 143

techniques of, 75

status quo, 29, 31, 102–104, 112, 150, 205, 222

Stevenson, Howard, 30–32, 34

super ideas, 58–59

Supplemental Nutrition Assistance Program (SNAP), 45–47, 49, 50, 66

Suresh, Subra, 99

Suri, Rohan, 187

surprises, 36–38

surveillance capitalism, 151

systemic risks, 204–205

Talbert House, 61

talent, 178, 220–221

talent magnets, 177

taxation, 5, 209, 212

Tech for Good, 16

#techforgoodlash, 16

techlash, 16

technology

backlash against, 16

employees in sector, 152–153

government and, 4, 12–14, 140–154

regulation of new, 91–110

Tencent, 120

terrorist attacks, 1–3, 35

Tesla, 213, 214

The People, 175

think tanks, 41

Thomas, Matthew, 154

ThunderDrone, 26

tolerance, 95–96

TraceTogether app, 181, 183–198

traffic data, 115–119

transaction platforms, 119–120, 124, 131

transparency, 14, 89, 209

trisector entrepreneurs, 139–156, 178, 220

Trump, Donald, 139, 182, 199

trust, in government, 3, 4, 89, 176, 224–225

Tsai, Henry, 155–156

Tseng, Brandon, 149–150

Tumml, 147

Turner, Molly, 92–96, 98, 105, 200, 203

Tushman, Mike, 219

Tusk, Bradley, 160–161, 166–167, 200

Twitter, 14, 120, 127

Type I errors, 106

Type II errors, 106

Uber, 99–102, 109, 120, 136, 160, 214

United Kingdom, 47–48, 140, 144, 198

United States Digital Service (USDS), 139–141, 143–144

Urban Innovation Fund, 147

Urban Us, 147

US Congress, 5

US Constitution, 5

US Department of Defense, 24–25, 136, 141, 150, 215

US Digital Service Academy, 221

user-driven innovation, 50–55

US Navy, 40–41

US Special Operations Command, 24–29, 38, 40

value creation, 120

Van Dyck, Haley, 144

vehicle-related deaths, 103–104

venture capitalists, 146–147, 212

Vista, 147

Voatz, 160–161, 165–166, 168–172

von Hippel, Eric, 51

voter registration, 172–174

voter turnout, 167

VoteShield, 172–174

voting

electronic, 166

internet, 166

mobile, 157–161, 165–172

Warner, Mac, 159–160, 161, 174

Warren, Elizabeth, 40

Waze, 10, 115–120, 122, 128–130, 135, 137, 153–154

WeChat, 120

Welch, Jack, 36

wiki government, 44

Wikipedia, 13

World War II, 27

Xerox, 76

Yahoo, 12

Y Combinator, 146

Yee, Norman, 108

YouTube, 13

Zapata, Franky, 23–24, 26, 38–40

Ziblatt, Daniel, 162, 163

Zients, Jeffrey, 142

Zika virus, 60, 184

Zuckerberg, Mark, 4, 209