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Report NEP-AIN-2024-10-14
This is the archive for NEP-AIN, a report on new working papers in the area of Artificial Intelligence. Ben Greiner issued this report. It is usually issued weekly.Subscribe to this report: email, RSS, or Mastodon, or Bluesky.
Other reports in NEP-AIN
The following items were announced in this report:
- Bohren, Noah & Hakimov, Rustamdjan & Lalive, Rafael, 2024, "Creative and Strategic Capabilities of Generative AI: Evidence from Large-Scale Experiments," IZA Discussion Papers, IZA Network @ LISER, number 17302, Sep.
- Ziyan Cui & Ning Li & Huaikang Zhou, 2024, "Can Large Language Models Replace Human Subjects? A Large-Scale Replication of Scenario-Based Experiments in Psychology and Management," Papers, arXiv.org, number 2409.00128, Aug, revised Jun 2025.
- Jingru Jia & Zehua Yuan, 2024, "An Experimental Study of Competitive Market Behavior Through LLMs," Papers, arXiv.org, number 2409.08357, Sep, revised Oct 2024.
- Vikram Krishnaveti & Saannidhya Rawat, 2024, "GPT takes the SAT: Tracing changes in Test Difficulty and Math Performance of Students," Papers, arXiv.org, number 2409.10750, Sep.
- Leonardo Gambacorta & Han Qiu & Shuo Shan & Daniel M Rees, 2024, "Generative AI and labour productivity: a field experiment on coding," BIS Working Papers, Bank for International Settlements, number 1208, Sep.
- Lee, Jaehyun & Lim, Jihye & Hwang, Junseok & Lee, Junmin, 2024, "How workers let artificial intelligence recruit and dismiss?," 24th ITS Biennial Conference, Seoul 2024. New bottles for new wine: digital transformation demands new policies and strategies, International Telecommunications Society (ITS), number 302513.
- Ali Merali, 2024, "Scaling Laws for Economic Productivity: Experimental Evidence in LLM-Assisted Translation," Papers, arXiv.org, number 2409.02391, Sep, revised Dec 2024.
- Yueling Huang, 2024, "The Labor Market Impact of Artificial Intelligence: Evidence from US Regions," IMF Working Papers, International Monetary Fund, number 2024/199, Sep.
- Alexander Bick & Adam Blandin & David Deming, 2024, "The Rapid Adoption of Generative AI," Working Papers, Federal Reserve Bank of St. Louis, number 2024-027, Sep, revised 27 Oct 2025, DOI: 10.20955/wp.2024.027.
- Doron Yeverechyahu & Raveesh Mayya & Gal Oestreicher-Singer, 2024, "The Impact of Large Language Models on Open-source Innovation: Evidence from GitHub Copilot," Papers, arXiv.org, number 2409.08379, Sep, revised Jun 2025.
- Francesco D'Alessandro & Enrico Santarelli & Marco Vivarelli, 2024, "The KSTE+I approach and the AI technologies," DISCE - Working Papers del Dipartimento di Politica Economica, Università Cattolica del Sacro Cuore, Dipartimenti e Istituti di Scienze Economiche (DISCE), number dipe0039, Sep.
- Martin Obschonka & Moren Levesque, 2024, "A Market for Lemons? Strategic Directions for a Vigilant Application of Artificial Intelligence in Entrepreneurship Research," Papers, arXiv.org, number 2409.08890, Sep.
- Anthony Harding & Juan Moreno-Cruz, 2024, "Watts and Bots: The Energy Implications of AI Adoption," Papers, arXiv.org, number 2409.06626, Sep.
- Cristian Trout, 2024, "Insuring Uninsurable Risks from AI: Government as Insurer of Last Resort," Papers, arXiv.org, number 2409.06672, Sep, revised Jul 2025.
- Oecd, 2024, "Detecting cartels for ex officio investigations," OECD Roundtables on Competition Policy Papers, OECD Publishing, number 311, Sep, DOI: 10.1787/1ea7cdba-en.
- Sandy Chen & Leqi Zeng & Abhinav Raghunathan & Flora Huang & Terrence C. Kim, 2024, "MoA is All You Need: Building LLM Research Team using Mixture of Agents," Papers, arXiv.org, number 2409.07487, Sep, revised Sep 2024.
- Shengkun Wang & Taoran Ji & Linhan Wang & Yanshen Sun & Shang-Ching Liu & Amit Kumar & Chang-Tien Lu, 2024, "StockTime: A Time Series Specialized Large Language Model Architecture for Stock Price Prediction," Papers, arXiv.org, number 2409.08281, Aug.
- Junjie Li & Yang Liu & Weiqing Liu & Shikai Fang & Lewen Wang & Chang Xu & Jiang Bian, 2024, "MarS: a Financial Market Simulation Engine Powered by Generative Foundation Model," Papers, arXiv.org, number 2409.07486, Sep, revised Mar 2025.
- Samuel Chang & Andrew Kennedy & Aaron Leonard & John List, 2024, "12 Best Practices for Leveraging Generative AI in Experimental Research," Artefactual Field Experiments, The Field Experiments Website, number 00796.