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Artificial Intelligence, Economics, and Industrial Organization

In: The Economics of Artificial Intelligence: An Agenda

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Abstract

Machine learning (ML) and artificial intelligence (AI) have been around for many years. However, in the last 5 years, remarkable progress has been made using multilayered neural networks in diverse areas such as image recognition, speech recognition, and machine translation. AI is a general purpose technology that is likely to impact many industries. In this chapter I consider how machine learning availability might affect the industrial organization of both firms that provide AI services and industries that adopt AI technology. My intent is not to provide an extensive overview of this rapidly-evolving area, but instead to provide a short summary of some of the forces at work and to describe some possible areas for future research.
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  • Hal Varian, 2018. "Artificial Intelligence, Economics, and Industrial Organization," NBER Chapters, in: The Economics of Artificial Intelligence: An Agenda, pages 399-419, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberch:14017
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    1. Ilya Segal, 2003. "Optimal Pricing Mechanisms with Unknown Demand," American Economic Review, American Economic Association, vol. 93(3), pages 509-529, June.
    2. Alessandro Acquisti & Hal R. Varian, 2005. "Conditioning Prices on Purchase History," Marketing Science, INFORMS, vol. 24(3), pages 367-381, May.
    3. Varian, Hal R., 1989. "Price discrimination," Handbook of Industrial Organization, in: R. Schmalensee & R. Willig (ed.), Handbook of Industrial Organization, edition 1, volume 1, chapter 10, pages 597-654, Elsevier.
    4. Alessandro Acquisti & Curtis Taylor & Liad Wagman, 2016. "The Economics of Privacy," Journal of Economic Literature, American Economic Association, vol. 54(2), pages 442-492, June.
    5. K. J. Arrow, 1971. "The Economic Implications of Learning by Doing," Palgrave Macmillan Books, in: F. H. Hahn (ed.), Readings in the Theory of Growth, chapter 11, pages 131-149, Palgrave Macmillan.
    6. Igal Hendel & Yossi Spiegel, 2014. "Small Steps for Workers, a Giant Leap for Productivity," American Economic Journal: Applied Economics, American Economic Association, vol. 6(1), pages 73-90, January.
    7. Rubinstein, Ariel, 1986. "Finite automata play the repeated prisoner's dilemma," Journal of Economic Theory, Elsevier, vol. 39(1), pages 83-96, June.
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    • L0 - Industrial Organization - - General

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