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Traditionally, AI has been modeled within economics as a technology that impacts payoffs by reducing costs or refining information for human agents.
Bayesian Models in Economic Theory
Bengt Holmstrom · 1984
Earlier work this paper cites.
Microeconomic theory
Andreu Mas-Colell, Michael Dennis Whinston, Jerry R Green, et al · 1995
Earlier work this paper cites.
Collaborative systems (aaai-94 presidential address)
Barbara J Grosz · 1996
Earlier work this paper cites.
Theories of delegation
Jonathan Bendor, Amihai Glazer, and Thomas Hammond · 2001
Earlier work this paper cites.
The Theory of Incentives: The Principal-Agent Model
Jean-Jacques Laffont and David Martimort · 2002
Earlier work this paper cites.
A model of delegated project choice
Mark Armstrong and John Vickers · 2010
Earlier work this paper cites.
Bayesian persuasion
Emir Kamenica and Matthew Gentzkow · 2011
Earlier work this paper cites.
The sample complexity of revenue maximization
Richard Cole and Tim Roughgarden · 2014
Earlier work this paper cites.
The limits of price discrimination
Dirk Bergemann, Benjamin Brooks, and Stephen Morris · 2015
Earlier work this paper cites.
Commonsense reasoning and commonsense knowledge in artificial intelligence
Ernest Davis and Gary Marcus · 2015
Earlier work this paper cites.
Dynamic pricing and learning: historical origins, current research, and new directions
Arnoud V Den Boer · 2015
Earlier work this paper cites.
Trust in automation: Integrating empirical evidence on factors that influence trust
Kevin Anthony Hoff and Masooda Bashir · 2015
Earlier work this paper cites.
High frequency market microstructure
Maureen O’hara · 2015
Earlier work this paper cites.
Information design, bayesian persuasion, and bayes correlated equilibrium
Dirk Bergemann and Stephen Morris · 2016
Earlier work this paper cites.
Learning simple auctions
Jamie Morgenstern and Tim Roughgarden · 2016
Earlier work this paper cites.
Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin · 2017
Earlier work this paper cites.
Explaining explanations: An overview of interpretability of machine learning
Leilani H Gilpin, David Bau, Ben Z Yuan, Ayesha Bajwa, Michael Specter, and Lalana Kagal · 2018
Earlier work this paper cites.
Guidelines for human-ai interaction
Saleema Amershi, Dan Weld, Mihaela Vorvoreanu, Adam Fourney, Besmira Nushi, Penny Collisson, Jina Suh, Shamsi Iqbal, Paul N Bennett, Kori Inkpen, et al · 2019
Earlier work this paper cites.
Updates in human-ai teams: Understanding and addressing the performance/compatibility tradeoff
Gagan Bansal, Besmira Nushi, Ece Kamar, Daniel S Weld, Walter S Lasecki, and Eric Horvitz · 2019
Cited alongside, same era.
Information design: A unified perspective
Dirk Bergemann and Stephen Morris · 2019
Cited alongside, same era.
Bayesian persuasion and information design
Emir Kamenica · 2019
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Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel Ziegler, Jeffrey Wu, Clemens Winter, Chris Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei · 2020
Cited alongside, same era.
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Ninareh Mehrabi, Fred Morstatter, Nripsuta Saxena, Kristina Lerman, and Aram Galstyan · 2021
Cited alongside, same era.
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Luyu Gao, Aman Madaan, Shuyan Zhou, Uri Alon, Pengfei Liu, Yiming Yang, Jamie Callan, and Graham Neubig · 2023
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Google · 2023
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Suriya Gunasekar, Yi Zhang, Jyoti Aneja, Caio César Teodoro Mendes, Allie Del Giorno, Sivakanth Gopi, Mojan Javaheripi, Piero Kauffmann, Gustavo de Rosa, Olli Saarikivi, Adil Salim, Shital Shah, Harkirat Singh Behl, Xin Wang, Sébastien Bubeck, Ronen Eldan, Adam Tauman Kalai, Yin Tat Lee, and Yuanzhi Li · 2023
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Large language models as simulated economic agents: What can we learn from homo silicus?
John J Horton · 2023
Later among the works it cites.
Textbooks are all you need ii: phi-1.5 technical report
Yuanzhi Li, Sébastien Bubeck, Ronen Eldan, Allie Del Giorno, Suriya Gunasekar, and Yin Tat Lee · 2023
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Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll L. Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, and et al · 2022
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Chain-of-thought prompting elicits reasoning in large language models
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoc V Le, Denny Zhou, et al · 2022
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OpenAI · 2023
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Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, et al · 2023
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