Soul and machine (learning)
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DOI: 10.1007/s11002-020-09538-4
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- Artem Timoshenko & John R. Hauser, 2019. "Identifying Customer Needs from User-Generated Content," Marketing Science, INFORMS, vol. 38(1), pages 1-20, January.
- Victor Chernozhukov & Denis Chetverikov & Mert Demirer & Esther Duflo & Christian Hansen & Whitney Newey & James Robins, 2018.
"Double/debiased machine learning for treatment and structural parameters,"
Econometrics Journal, Royal Economic Society, vol. 21(1), pages 1-68, February.
- Victor Chernozhukov & Denis Chetverikov & Mert Demirer & Esther Duflo & Christian Hansen & Whitney Newey & James Robins, 2017. "Double/Debiased Machine Learning for Treatment and Structural Parameters," NBER Working Papers 23564, National Bureau of Economic Research, Inc.
- Victor Chernozhukov & Denis Chetverikov & Mert Demirer & Esther Duflo & Christian Hansen & Whitney K. Newey & James Robins, 2017. "Double/debiased machine learning for treatment and structural parameters," CeMMAP working papers CWP28/17, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
- Victor Chernozhukov & Denis Chetverikov & Mert Demirer & Esther Duflo & Christian Hansen & Whitney K. Newey & James Robins, 2017. "Double/debiased machine learning for treatment and structural parameters," CeMMAP working papers 28/17, Institute for Fiscal Studies.
- Eric M. Schwartz & Eric T. Bradlow & Peter S. Fader, 2017. "Customer Acquisition via Display Advertising Using Multi-Armed Bandit Experiments," Marketing Science, INFORMS, vol. 36(4), pages 500-522, July.
- John R. Hauser & Guilherme (Gui) Liberali & Glen L. Urban, 2014. "Website Morphing 2.0: Switching Costs, Partial Exposure, Random Exit, and When to Morph," Management Science, INFORMS, vol. 60(6), pages 1594-1616, June.
- John D. C. Little, 1970. "Models and Managers: The Concept of a Decision Calculus," Management Science, INFORMS, vol. 16(8), pages 466-485, April.
- Hema Yoganarasimhan & Ebrahim Barzegary & Abhishek Pani, 2020. "Design and Evaluation of Personalized Free Trials," Papers 2006.13420, arXiv.org.
- Matt Taddy, 2018. "The Technological Elements of Artificial Intelligence," NBER Working Papers 24301, National Bureau of Economic Research, Inc.
- Emilio Calvano & Giacomo Calzolari & Vincenzo Denicolò & Sergio Pastorello, 2020.
"Artificial Intelligence, Algorithmic Pricing, and Collusion,"
American Economic Review, American Economic Association, vol. 110(10), pages 3267-3297, October.
- Calzolari, Giacomo & Calvano, Emilio & Denicolo, Vincenzo & Pastorello, Sergio, 2018. "Artificial intelligence, algorithmic pricing and collusion," CEPR Discussion Papers 13405, C.E.P.R. Discussion Papers.
- Matt Taddy, 2018. "The Technological Elements of Artificial Intelligence," NBER Chapters, in: The Economics of Artificial Intelligence: An Agenda, pages 61-87, National Bureau of Economic Research, Inc.
- Alex Burnap & John R. Hauser & Artem Timoshenko, 2019. "Product Aesthetic Design: A Machine Learning Augmentation," Papers 1907.07786, arXiv.org, revised Nov 2022.
- Joseph E Harrington, 2018. "Developing Competition Law For Collusion By Autonomous Artificial Agents," Journal of Competition Law and Economics, Oxford University Press, vol. 14(3), pages 331-363.
- Pai, Mallesh & Hansen, Karsten, 2020. "Algorithmic Collusion: Supra-competitive Prices via Independent Algorithms," CEPR Discussion Papers 14372, C.E.P.R. Discussion Papers.
- Joachim Büschken & Greg M. Allenby, 2016. "Sentence-Based Text Analysis for Customer Reviews," Marketing Science, INFORMS, vol. 35(6), pages 953-975, November.
- Kanishka Misra & Eric M. Schwartz & Jacob Abernethy, 2019. "Dynamic Online Pricing with Incomplete Information Using Multiarmed Bandit Experiments," Marketing Science, INFORMS, vol. 38(2), pages 226-252, March.
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Cited by:
- Erik Hermann, 2022. "Leveraging Artificial Intelligence in Marketing for Social Good—An Ethical Perspective," Journal of Business Ethics, Springer, vol. 179(1), pages 43-61, August.
- Miikka Blomster & Timo Koivumäki, 2022. "Exploring the resources, competencies, and capabilities needed for successful machine learning projects in digital marketing," Information Systems and e-Business Management, Springer, vol. 20(1), pages 123-169, March.
- Villarroel Ordenes, Francisco & Silipo, Rosaria, 2021. "Machine learning for marketing on the KNIME Hub: The development of a live repository for marketing applications," Journal of Business Research, Elsevier, vol. 137(C), pages 393-410.
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Keywords
Machine learning; Marketing applications; Knowledge;All these keywords.
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