by Ajay Agrawal
Telang, R., 444, 450, 457, 457n42
Somanchi, S., 444
Teodoridis, F., 150, 161
Song, J., 30
Tetenov, A., 527
Sonnenschein, H., 589n1
Thaler, R., 604
Sopher, B., 590n2
Thomas, K., 443
Sorensen, O., 161
Thomas, P., 528
Spencer, B. J., 473
Thompson, W. R., 82
Spezio, M., 594n10
Thrnton, B., 598
Spiegel, M., 589n1
Tibshirani, R., 93, 525, 527
Spiegel, Y., 412
Tirole, J., 106, 112, 264, 268, 269
Spier, K., 494, 499, 502
Topol, E. J., 94, 95
Spiess, J., 169n8, 511, 512
Tory, J., 485n23
Spindler, M., 522
Toulis, P., 80
Srivastava, N., 80
Trajtenberg, M., 4, 39, 116, 119, 132, 150,
Stahl, D. O., 593
169, 176n2
Stantcheva, S., 360
Trefl er, D., 465, 467, 479, 485n23
Stein, A., 494
Troske, K. R., 562
Stern, A., 325
Tschantz, M. C., 433
Stern, S., 122, 141
Tucker, C. E., 148, 425, 426, 427, 428, 429,
Stevenson, B., 194n5
448, 449, 482, 483, 483n19
Stigler, G. J., 439, 582
Turing, A. M., 123, 385
Stiglitz, J. E., 30n9, 354, 360, 362, 364,
Tuzel, S., 210
364n11, 366n14, 366n15, 368n19, 369,
Tversky, A., 596
370, 370n21, 371, 373, 376n27, 376n28,
378, 412
Ugander, J., 538
Stillwell, D., 429
Uyarra, E., 480
Stinchcombe, M., 74
Stivers, A., 442n4
Vadlamannati, K. C., 483
Stole, L., 268
Valentinyi, Á., 204, 241n4
Strehl, A., 527
Van der Laan, M. J., 522, 527
Streitwieser, M. L., 562
Van Seijen, H., 62, 84
Strotz, R. H., 427
Varian, H. R., 93, 310, 410, 413, 424, 425,
Stucke, M. E., 414
440, 511
Stutzman, F., 451
Venables, A. J., 480
Sufi , A., 208
Vesteger, M., 420
Summers, L. H., 175
Vickrey, W., 567
Sutskever, I., 68, 74, 125
Vijverberg, W. P. M., 483
Sutton, J., 467, 589n1
Vincent, N., 270
Swaffi
eld, J., 293n2
Vines, P., 446
Swaminathan, A., 528
Vinge, V., 238, 253, 253n12, 373n23
Sweeney, L., 433
Viscusi, K., 496, 498
Swire, P. P., 441n2
Vishnu, A., 151n1, 168
Syverson, C., 23, 29, 43, 210, 319, 320
Von Hippel, E., 499
Author Index 623
Von Weizacker, C. C., 376
Wright, J. R., 594
Von Winterfeldt, D., 595
Wu, L., 43
Vuong, Q., 514
Xiao, M., 593n8
Wager, S., 523, 525, 527, 528
Xie, D., 241n4, 295
Wagman, L., 416, 424, 441n2, 448, 459,
Xu, H., 577
483n19
Wallach, I., 116
Yakovlev, E., 538
Wan, M., 534
Yang, S., 42, 47
Wang, J., 475, 477, 594n10
Yellen, J., 378
Warren, S. D., 431, 459
Yeomans, M., 517
Waseem, M., 270
Yildirim, P., 107n9
Wattal, S., 450
Yu, M., 465, 479
Weil, D., 459
Yudkowsky, E., 253n12
Weingast, B. R., 577
Weiss, A., 354
Zanna, L.-F., 373n24
Weitzman, M., 149, 151, 157, 171, 171n9,
Zeevi, A., 529
172, 241
Zeileis, A., 525
Western, B., 322
Zeira, J., 152, 198, 212n7, 238, 239, 241
Westlake, S., 18
Zervas, G., 577n5
Whinston, M. D., 148
Zhang, M. B., 210
White, H., 74, 511
Zheng, Y., 329
Williams, H., 122, 364n11
Zhou, D., 529, 581
Williams, R., 124
Zhou, X., 580
Wilson, P. W., 593
Zhu, X., 471
Winter, S., 161
Zierahn, U., 321
Wolfers, J., 195n5
Zubizarreta, J. R., 523
Wooldridge, J. M., 522
Zweimüller, J., 295
Wright, G., 42n19
Zwiebel, J., 268
Wright, G. C., 303
Subject Index
Note: Page numbers followed by “f ” or “t” refer to fi gures or tables, respectively.
adoption, technological: implications of
351; inequality and, 320– 23; internal
speed of, for job market and inequality,
agreements and, 463; international
310– 12
macroeconomics and, 488; knowledge
adversarial artifi cial intelligence, 401
externalities and, 470– 71; likely produc-
aggregate productivity statistics, technolo-
tivity eff ects of, and acceleration of, 45–
gies and, 26– 28
46; longer- term prospects of, 381– 86;
AI. See artifi cial intelligence (AI)
market structure and, 262– 63; as “next
AlphaGo (Google), 63
big thing,” 175; political economy of,
Amazon Go concept, 67
11, 394– 95; prediction costs and, 92– 93;
applied artifi cial intelligence, 208
privacy and, 425– 26; privacy concerns
artifi cial intelligence (AI), 1; and automa-
and, 423– 24; for promoting trust in
tion of production, 239– 50; as basis
online marketplaces, 576– 81; recent
for learning, 120– 21; benefi t of more,
approach to, 93; for reducing search
318– 20; bibliometric data on evolution
frictions, 581– 83; return of Malthus
of, 128– 32; capital shares and, 270– 74,
and, 381– 86; revolution, international
272– 73f; in context, 84– 85; defi ned,
eff ects of, 393– 94; in Schumpeterian
3– 4, 62– 67, 93, 122, 237, 468; econo-
model with creative destruction, 276–
mies of scale from data and, 468– 69;
79; sectoral reallocation and, 263– 64;
economies of scale from overhead
statistics on, 465– 66, 466t; studies on
of developing AI capabilities, 469–
economic eff ect of, 556– 58; theory of
70; evolution of, 122– 25; expected
privacy in economics and, 424– 26; as
productivity eff ects of, 45– 46; fi rm
tool, 16– 17; world’s largest companies
organization and, 264– 70; future of
and exposure to, 465– 67, 467t. See also
research on economics of, 17; as general
machine learning (ML)
purpose technology, 4– 7, 39– 41; in idea
artifi cial intelligence capital, measuring,
production function, 250– 52; impact
46– 50
of, on innovation, 125– 28; impact of
artifi cial intelligence– general purpose tech-
long- term decline in labor force partici-
nology (GPT) era: education strategies
pation rate and,
323– 25; implications
for, 179– 82; human- enhancing innova-
of, 349– 53; income distribution and,
tions vs. human- replacing innovations
625
626 Subject Index
artifi cial intelligence– general purpose tech-
consumer attitude, 448– 49
nology (GPT) era ( continued)
consumer privacy: challenging issues in,
for, 184– 85; professionalization of
457– 59; consumer attitude and, 448– 49;
personal services strategies for, 182– 84;
consumer risk and, 443– 48; data risk
top skills required for employment in,
and, 442– 43; nature of problem of, 442;
180– 81, 181t
policy landscape in United States, 454–
artifi cial intelligence revolution, inter-
57; supply side actions and, 450– 54.
national eff ects of, 393– 94
See also privacy
Atomwise, case of, 115– 16, 120, 154
consumer surplus, 11; distribution of,
automatic teller machines (ATMs), security
391– 93
policy and, 416
contracting, 106– 7
automation, 3– 4, 105– 6; basic model, 336–
convolutional neural networks (CNNs),
41; Baumol’s cost disease and, 241– 50;
75– 76, 75n6
to date, and capital shares, 270– 74;
cooperation, evolution of, 414
decline in labor share and, 329– 31;
cost disease, Baumol’s, 8– 9, 238– 39; auto-
deepening of, 198, 204– 5, 216– 17; eco-
mation and, 241– 50
nomic adjustment and, 208– 9; employ-
creative destruction, 260– 61
ment and, 190– 91; excessive, 224– 26;
model of, 211– 14; of production, and
data, 61; acquisition methods, 403– 4; de-
artifi cial intelligence, 239– 50; produc-
creasing marginal returns of, 406, 407f;
tivity and, 210– 11; sector of economy
economics of, 14; importance of, 13–
aff ected by, 330– 33; studies on employ-
14; important characteristics of, 404– 6;
ment on, 555– 58; wages and, 200– 211;
localization rules, trade policies and,
winners, 190; work and, 200– 211; Zeira
485– 86; persistence of predictive power
model of growth and, 239– 41
of, 427– 28; privileged access to govern-
average treatment eff ects, 522– 24
ment, trade policies and, 486– 87;
types of, and generation of spillovers,
bandits (algorithms), problem of, 528– 29
431– 34
Baumol’s cost disease. See cost disease,
data access, 405– 6
Baumol’s
data generation, as pillar of artifi cial intel-
BenchSci search technology, 153
ligence, 62f, 65– 66
buy/ make decisions (fi rm boundaries),
data ownership, 405– 6
107– 8
data persistence, 426– 27; predictive power
and, 427– 28
capital accumulation, 198, 204– 5, 216
data pipeline, 402
capital shares, and automation to date,
data pyramid, 404, 405f
270– 74
data repurposing, 428– 31
causal inference, new literature on, 519– 34
data security: challenging issues in, 457–
Children’s Online Privacy Protection Act of
59; policy landscape in United States,
1998 (COPPA), 454
454– 57
cloud- computing facilities, 402– 3
data spillovers, 431– 34
cluster policies, 480– 81
data warehouses, 402– 3
clusters, regional, theory of, 479
decision- making, baseline model for, 95–
CNNs (convolutional neural networks), 75–
103; complexity and, 103– 8
76, 75n6
deepening of automation, 198, 204– 5,
collusion, strategies for facilitating, 413– 14
216– 17
combinatorial- based knowledge production
Deep Genomics, 154
function, 154– 61; potential uses of new,
deep learning, 3, 71– 77, 400; as general pur-
170– 71; with team production, 161– 67
pose invention in method of invention,
Communications Act (1986), 456
139– 43; as general purpose technology,
competition policy, innovations and, 141– 43
133– 39; as new discovery tool, 167– 69;
complexity, 103– 4
patent systems and, 142
Subject Index 627
deep learning networks, 94
GPT. See general purpose technology (GPT)
deep learning techniques, 25
Gramm- Leach- Bliley Act (GLBA), 454
deep neural networks (DNNs), 25– 26, 61,
growth: impact of artifi cial intelligence on,
63; structure in, 76– 77
7– 9. See also economic growth
demand, importance of, 301– 2
destruction, creative, 260– 61
Health Insurance Portability and Account-
diff erence- in-diff erence models, 530– 31
ability Act of 1996 (HIPAA), 454
digital information, 334– 35
HEI (human- enhancing innovations),
direct network eff ects, 412
184– 85
displacement eff ect, 8, 198, 208, 214
heterogeneous treatment eff ects, 524– 28
DNNs. See deep neural networks (DNNs)
hierarchical Poisson factorization, 510
domain structure, as pillar of artifi cial intel-
HIPAA. See Health Insurance Portabil-
ligence, 62f, 63– 65
ity and Accountability Act of 1996
double machine learning, 523– 24
(HIPAA)
HRI (human- replacing innovations), 184– 85
economic growth: artifi cial intelligence and,
human- enhancing innovations (HEI), 184– 85
262– 70; prospects for technology-
human- replacing innovations (HRI), 184– 85
driven, 149– 53; Zeira model of auto-
mation and, 239– 41. See also growth
idea production function, artifi cial intelli-
economics, impact of machine learning on
gence in, 250– 52
practice of, 15– 16
implementation/ restructuring lags, as expla-
education, factory model of, 180
nation for Solow paradox, 31– 36
Electronic Communications Privacy Act of
incentive auctions, machine learning and,
1986: (ECPA), 456
569– 76
employment: automation and, 190– 91;
income, artifi cial intelligence and, 189– 90
levels of, and new technologies, 220– 21;
income distribution: artifi cial intelligence
long- run vs. short run, 192– 94; studies
and, 351; impact of AI on, 11
on automation and, 555– 57; work out-
income inequality: artifi cial intelligence and,
side of, 194– 95
320– 23; impact of AI on, 7– 8, 11– 12;
evolution of cooperation, 414
speed of technological adoption and,
310– 12
factory model of education, 180
income redistribution, political e
conomy of,
Federal Trade Commission (FTC), 454– 55
394– 95
fi rm boundaries (make/ buy decisions),
indirect network eff ects, 412
107– 8
industrial regulation, trade policies and, 487
fi rm- level data: need for, 558– 59; strategies
inequality. See income inequality
for collecting, 561– 62
information technology (IT), 24
fi rm- level research questions, 560– 61
innovation, 115– 18; competition policy and,
fi rms: artifi cial intelligence and, 262– 70;
141– 43; early stage, 121– 22; impact
impact of machine learning on, 12
of artifi cial intelligence on, 125– 28;
institutions and, 141– 43; management
general purpose machine learning (GPML),
and organization of, 140– 41; product
67– 71
liability and, 494
general purpose technology (GPT), 2, 65,
institutions, innovations and, 141– 43
119– 20, 169– 70; artifi cial intelligence
intelligence- assisting innovation (IA),
as, 4– 7, 39– 41; deep learning as, 133–
350– 51
39; viewing today’s Solow paradox
International Federation of Robotics
through previous history of, 44– 45
(IFR), 16
generative adversarial networks (GANs),
international macroeconomics, artifi cial
66– 67
intelligence and, 488
GPML (general purpose machine learning),
international trade, economics of data
67– 71
and, 14
628 Subject Index
invention of a method of inventing (IMI),
machine learning– provision industries,
120– 21, 124
414– 15
inverted- U pattern, 293; simple model of,
machine learning– using industries, 408f;
297– 301
boundaries and, 409– 10; fi rm size and,
409– 10; price diff erentiation and, 410–
JDM (judgment and decision- making) re-
11; pricing and, 410; returns to scale
search, 596– 98
and, 411– 14; structure of, 406– 8
job displacement, 310– 12
macroeconomics, international, artifi cial
job losses, 291
intelligence and, 488
job markets, speed of technological adop-
make/ buy decisions (fi rm boundaries),
tion and, 310– 12
107– 8
jobs, impact of artifi cial intelligence on, 7– 8,
Maluuba, 62– 63
9– 11
market design, introduction to, 567– 69
judgment: in absence of prediction, 96– 101;
massive open online courses (MOOCS),
as complements/ substitutes to predic-
181
tion, 102– 3; creating role for, 91; predic-
matrix completion problem, 531– 32
tion and, 91– 92