The Economics of Artificial Intelligence
An Agenda
9780226613338
9780226613475
The Economics of Artificial Intelligence
An Agenda
Advances in artificial intelligence (AI) highlight the potential of this technology to affect productivity, growth, inequality, market power, innovation, and employment. This volume seeks to set the agenda for economic research on the impact of AI. It covers four broad themes: AI as a general purpose technology; the relationships between AI, growth, jobs, and inequality; regulatory responses to changes brought on by AI; and the effects of AI on the way economic research is conducted. It explores the economic influence of machine learning, the branch of computational statistics that has driven much of the recent excitement around AI, as well as the economic impact of robotics and automation and the potential economic consequences of a still-hypothetical artificial general intelligence. The volume provides frameworks for understanding the economic impact of AI and identifies a number of open research questions.
Contributors:
Daron Acemoglu, Massachusetts Institute of Technology
Philippe Aghion, Collège de France
Ajay Agrawal, University of Toronto
Susan Athey, Stanford University
James Bessen, Boston University School of Law
Erik Brynjolfsson, MIT Sloan School of Management
Colin F. Camerer, California Institute of Technology
Judith Chevalier, Yale School of Management
Iain M. Cockburn, Boston University
Tyler Cowen, George Mason University
Jason Furman, Harvard Kennedy School
Patrick Francois, University of British Columbia
Alberto Galasso, University of Toronto
Joshua Gans, University of Toronto
Avi Goldfarb, University of Toronto
Austan Goolsbee, University of Chicago Booth School of Business
Rebecca Henderson, Harvard Business School
Ginger Zhe Jin, University of Maryland
Benjamin F. Jones, Northwestern University
Charles I. Jones, Stanford University
Daniel Kahneman, Princeton University
Anton Korinek, Johns Hopkins University
Mara Lederman, University of Toronto
Hong Luo, Harvard Business School
John McHale, National University of Ireland
Paul R. Milgrom, Stanford University
Matthew Mitchell, University of Toronto
Alexander Oettl, Georgia Institute of Technology
Andrea Prat, Columbia Business School
Manav Raj, New York University
Pascual Restrepo, Boston University
Daniel Rock, MIT Sloan School of Management
Jeffrey D. Sachs, Columbia University
Robert Seamans, New York University
Scott Stern, MIT Sloan School of Management
Betsey Stevenson, University of Michigan
Joseph E. Stiglitz. Columbia University
Chad Syverson, University of Chicago Booth School of Business
Matt Taddy, University of Chicago Booth School of Business
Steven Tadelis, University of California, Berkeley
Manuel Trajtenberg, Tel Aviv University
Daniel Trefler, University of Toronto
Catherine Tucker, MIT Sloan School of Management
Hal Varian, University of California, Berkeley
Contributors:
Daron Acemoglu, Massachusetts Institute of Technology
Philippe Aghion, Collège de France
Ajay Agrawal, University of Toronto
Susan Athey, Stanford University
James Bessen, Boston University School of Law
Erik Brynjolfsson, MIT Sloan School of Management
Colin F. Camerer, California Institute of Technology
Judith Chevalier, Yale School of Management
Iain M. Cockburn, Boston University
Tyler Cowen, George Mason University
Jason Furman, Harvard Kennedy School
Patrick Francois, University of British Columbia
Alberto Galasso, University of Toronto
Joshua Gans, University of Toronto
Avi Goldfarb, University of Toronto
Austan Goolsbee, University of Chicago Booth School of Business
Rebecca Henderson, Harvard Business School
Ginger Zhe Jin, University of Maryland
Benjamin F. Jones, Northwestern University
Charles I. Jones, Stanford University
Daniel Kahneman, Princeton University
Anton Korinek, Johns Hopkins University
Mara Lederman, University of Toronto
Hong Luo, Harvard Business School
John McHale, National University of Ireland
Paul R. Milgrom, Stanford University
Matthew Mitchell, University of Toronto
Alexander Oettl, Georgia Institute of Technology
Andrea Prat, Columbia Business School
Manav Raj, New York University
Pascual Restrepo, Boston University
Daniel Rock, MIT Sloan School of Management
Jeffrey D. Sachs, Columbia University
Robert Seamans, New York University
Scott Stern, MIT Sloan School of Management
Betsey Stevenson, University of Michigan
Joseph E. Stiglitz. Columbia University
Chad Syverson, University of Chicago Booth School of Business
Matt Taddy, University of Chicago Booth School of Business
Steven Tadelis, University of California, Berkeley
Manuel Trajtenberg, Tel Aviv University
Daniel Trefler, University of Toronto
Catherine Tucker, MIT Sloan School of Management
Hal Varian, University of California, Berkeley
648 pages | 74 line drawings, 21 tables | 6 x 9 | © 2019
National Bureau of Economic Research Conference Report
Economics and Business: Business--Industry and Labor, Economics--Development, Growth, Planning
Reviews
Table of Contents
The Economics of Artificial Intelligence: An Agenda
Ajay Agrawal, Joshua Gans, and Avi Goldfarb, editors
Acknowledgements
Introduction
Ajay Agrawal, Joshua Gans, and Avi Goldfarb
I. AI as a GPT
1. Artificial Intelligence and the Modern Productivity Paradox: A Clash of Expectations and Statistics
Erik Brynjolfsson, Daniel Rock, and Chad Syverson
Comment: Rebecca Henderson
2. The Technological Elements of Artificial Intelligence
Matt Taddy
3. Prediction, Judgment, and Complexity: A Theory of Decision-Making and Artificial Intelligence
Ajay Agrawal, Joshua Gans, and Avi Goldfarb
Comment: Andrea Prat
4. The Impact of Artificial Intelligence on Innovation: An Exploratory Analysis
Iain M. Cockburn, Rebecca Henderson, and Scott Stern
Comment: Matthew Mitchell
5. Finding Needles in Haystacks: Artificial Intelligence and Recombinant Growth
Ajay Agrawal, John McHale, and Alexander Oettl
6. Artificial Intelligence as the Next GPT: A Political-Economy Perspective
Manuel Trajtenberg
II. Growth, Jobs, and Inequality
7. Artificial Intelligence, Income, Employment, and Meaning
Betsey Stevenson
8. Artificial Intelligence, Automation, and Work
Daron Acemoglu and Pascual Restrepo
9. Artificial Intelligence and Economic Growth
Philippe Aghion, Benjamin F. Jones, and Charles I. Jones
Comment: Patrick Francois
10. Artificial Intelligence and Jobs: The Role of Demand
James Bessen
11. Public Policy in an AI Economy
Austan Goolsbee
12. Should We Be Reassured If Automation in the Future Looks Like Automation in the Past?
Jason Furman
13. R&D, Structural Transformation, and the Distribution of Income
Jeffrey D. Sachs
14. Artificial Intelligence and Its Implications for Income Distribution and Unemployment
Anton Korinek and Joseph E. Stiglitz
15. Neglected Open Questions in the Economics of Artificial Intelligence
Tyler Cowen
III. Machine Learning and Regulation
16. Artificial Intelligence, Economics, and Industrial Organization
Hal Varian
Comment: Judith Chevalier
17. Privacy, Algorithms, and Artificial Intelligence
Catherine Tucker
18. Artificial Intelligence and Consumer Privacy
Ginger Zhe Jin
19. Artificial Intelligence and International Trade
Avi Goldfarb and Daniel Trefler
20. Punishing Robots: Issues in the Economics of Tort Liability and Innovation in Artificial Intelligence
Alberto Galasso and Hong Luo
IV. Machine Learning and Economics
21. The Impact of Machine Learning on Economics
Susan Athey
Comment: Mara Lederman
22. Artificial Intelligence, Labor, Productivity, and the Need for Firm-Level Data
Manav Raj and Robert Seamans
23. How Artificial Intelligence and Machine Learning Can Impact Market Design
Paul R. Milgrom and Steven Tadelis
24. Artificial Intelligence and Behavioral Economics
Colin F. Camerer
Comment: Daniel Kahneman
Author Index
Subject Index
Ajay Agrawal, Joshua Gans, and Avi Goldfarb, editors
Acknowledgements
Introduction
Ajay Agrawal, Joshua Gans, and Avi Goldfarb
I. AI as a GPT
1. Artificial Intelligence and the Modern Productivity Paradox: A Clash of Expectations and Statistics
Erik Brynjolfsson, Daniel Rock, and Chad Syverson
Comment: Rebecca Henderson
2. The Technological Elements of Artificial Intelligence
Matt Taddy
3. Prediction, Judgment, and Complexity: A Theory of Decision-Making and Artificial Intelligence
Ajay Agrawal, Joshua Gans, and Avi Goldfarb
Comment: Andrea Prat
4. The Impact of Artificial Intelligence on Innovation: An Exploratory Analysis
Iain M. Cockburn, Rebecca Henderson, and Scott Stern
Comment: Matthew Mitchell
5. Finding Needles in Haystacks: Artificial Intelligence and Recombinant Growth
Ajay Agrawal, John McHale, and Alexander Oettl
6. Artificial Intelligence as the Next GPT: A Political-Economy Perspective
Manuel Trajtenberg
II. Growth, Jobs, and Inequality
7. Artificial Intelligence, Income, Employment, and Meaning
Betsey Stevenson
8. Artificial Intelligence, Automation, and Work
Daron Acemoglu and Pascual Restrepo
9. Artificial Intelligence and Economic Growth
Philippe Aghion, Benjamin F. Jones, and Charles I. Jones
Comment: Patrick Francois
10. Artificial Intelligence and Jobs: The Role of Demand
James Bessen
11. Public Policy in an AI Economy
Austan Goolsbee
12. Should We Be Reassured If Automation in the Future Looks Like Automation in the Past?
Jason Furman
13. R&D, Structural Transformation, and the Distribution of Income
Jeffrey D. Sachs
14. Artificial Intelligence and Its Implications for Income Distribution and Unemployment
Anton Korinek and Joseph E. Stiglitz
15. Neglected Open Questions in the Economics of Artificial Intelligence
Tyler Cowen
III. Machine Learning and Regulation
16. Artificial Intelligence, Economics, and Industrial Organization
Hal Varian
Comment: Judith Chevalier
17. Privacy, Algorithms, and Artificial Intelligence
Catherine Tucker
18. Artificial Intelligence and Consumer Privacy
Ginger Zhe Jin
19. Artificial Intelligence and International Trade
Avi Goldfarb and Daniel Trefler
20. Punishing Robots: Issues in the Economics of Tort Liability and Innovation in Artificial Intelligence
Alberto Galasso and Hong Luo
IV. Machine Learning and Economics
21. The Impact of Machine Learning on Economics
Susan Athey
Comment: Mara Lederman
22. Artificial Intelligence, Labor, Productivity, and the Need for Firm-Level Data
Manav Raj and Robert Seamans
23. How Artificial Intelligence and Machine Learning Can Impact Market Design
Paul R. Milgrom and Steven Tadelis
24. Artificial Intelligence and Behavioral Economics
Colin F. Camerer
Comment: Daniel Kahneman
Author Index
Subject Index
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