Game-theory behaviour of large language models: The case of Keynesian beauty contests

Aher, G. V., Arriaga, R. I., & Kalai, A. T. (2023). Using large language models to simulate multiple humans and replicate human subject studies. Proceedings of Machine Learning, 202, 337–371. https://proceedings.mlr.press/v202/aher23a.html Search in Google Scholar

Akata, E., Schulz, L., Coda-Forno, J., Oh, S. J., Bethge, M., & Schulz, E. (2023). Playing repeated games with large language models. https://doi.org/10.48550/arXiv.2305.16867 Search in Google Scholar

Argyle, L. P., Busby, E. C., Fulda, N., Gubler, J. R., Rytting, C., & Wingate, D. (2023). Out of one, many: Using language models to simulate human samples. Political Analysis, 31(3), 337–351. https://doi.org/10.1017/pan.2023.2 Search in Google Scholar

Bauer, K., Liebich, L., Hinz, O., & Kosfeld, M. (2023). Decoding GPT’s hidden ‘rationality’ of cooperation. SAFE Working Paper, 401. https://doi.org/10.2139/ssrn.4576036 Search in Google Scholar

Bosch-Domenech, A., Montalvo, J. G., Nagel, R., & Satorra, A. (2002). One, two,(three), infinity,…: Newspaper and lab beauty-contest experiments. American Economic Review, 92(5), 1687–1701. https://doi.org/10.1257/000282802762024737 Search in Google Scholar

Brown, Z. Y., & MacKay, A. (2023). Competition in pricing algorithms. American Economic Journal: Microeconomics, 15(2), 109–156. https://doi.org/10.1257/mic.20210158 Search in Google Scholar

Camerer, C. F., Ho, T. H., & Chong, J. K. (2004). A cognitive hierarchy model of games. The Quarterly Journal of Economics, 119(3), 861–898. https://doi.org/10.1162/0033553041502225 Search in Google Scholar

Chen, L., Mislove, A., & Wilson, C. (2016). An empirical analysis of algorithmic pricing on Amazon marketplace. Proceedings of the 25th International Conference on World Wide Web, 1339–1349. https://doi.org/10.1145/2872427.2883089 Search in Google Scholar

Chen, Y., Liu, T. X., Shan, Y., & Zhong, S. (2023). The emergence of economic rationality of GPT. Proceedings of the National Academy of Sciences, 120(51), e2316205120. https://doi.org/10.1073/pnas.2316205120 Search in Google Scholar

Coricelli, G., & Nagel, R. (2009). Neural correlates of depth of strategic reasoning in medial prefrontal cortex. Proceedings of the National Academy of Sciences, 106(23), 9163–9168. https://doi.org/10.1073/pnas.0807721106 Search in Google Scholar

Costa-Gomes, M. A., & Weizsäcker, G. (2008). Stated beliefs and play in normal-form games. The Review of Economic Studies, 75(3), 729–762. https://doi.org/10.1111/j.1467-937X.2008.00498.x Search in Google Scholar

Devetag, G., Di Guida, S., & Polonio, L. (2016). An eye-tracking study of feature-based choice in one-shot games. Experimental Economics, 19(1), 177–201. https://doi.org/10.1007/s10683-015-9432-5 Search in Google Scholar

Dillion, D., Tandon, N., Gu, Y., & Gray, K. (2023). Can ai language models replace human participants? Trends in Cognitive Sciences, 27(7), 597–600. https://doi.org/10.1016/j.tics.2023.04.008 Search in Google Scholar

Fan, C., Chen, J., Jin, Y., & He, H. (2023). Can large language models serve as rational players in game theory? A systematic analysis. https://doi.org/10.48550/arXiv.2312.05488. Search in Google Scholar

Guo, F. (2023). GPT in game theory experiments. https://doi.org/10.48550/arXiv.2305.05516. Search in Google Scholar

Guo, S., Bu, H., Wang, H., Ren, Y., Sui, D., Shang, Y., & Lu, S. (2024). Economics arena for large language models. https://doi.org/10.48550/arXiv.2401.01735. Search in Google Scholar

Hamill, L., & Gilbert, N. (2015). Agent-based modelling in economics. John Wiley & Sons. Search in Google Scholar

Horton, J. J. (2023). Large language models as simulated economic agents: What can we learn from homo silicus? NBER Working Paper, 31122.. https://doi.org/10.3386/w31122 Search in Google Scholar

HuggingFace. (2022). Illustrating reinforcement learning from human feedback (RLHF). https://huggingface.co/blog/rlhf Search in Google Scholar

Huijzer, R., & Hill, Y. (2023, January 31). Large language models show human behavior. https://doi.org/10.31234/osf.io/munc9 Search in Google Scholar

Ireson, J., & Hallam, S. (1999). Raising standards: Is ability grouping the answer? Oxford Review of Education, 25(3), 343–358. https://doi.org/10.1080/030549899104026 Search in Google Scholar

Kalton, G., & Schuman, H. (1982). The effect of the question on survey responses: A review. Journal of the Royal Statistical Society Series A, 145(1), 42–73. https://doi.org/10.2307/2981421 Search in Google Scholar

Keynes, J. M. (1936). The general theory of interest, employment and money. Macmillan. Search in Google Scholar

Kosinski, M. (2023). Theory of mind may have spontaneously emerged in large language models. https://www.gsb.stanford.edu/faculty-research/working-papers/theory-mind-may-have-spontaneously-emerged-large-language-models Search in Google Scholar

Liem, G. A. D., Marsh, H. W., Martin, A. J., McInerney, D. M., & Yeung, A. S. (2013). The big-fish-little-pond effect and a national policy of within-school ability streaming: Alternative frames of reference. American Educational Research Journal, 50(2), 326–370. https://doi.org/10.3102/0002831212464511 Search in Google Scholar

Mauersberger, F., & Nagel, R. (2018). Levels of reasoning in Keynesian beauty contests: A generative framework. In C. Hommes & B. LeBaron (Eds.), Handbook of computational economics (vol. 4, pp. 541–634). Elsevier. https://doi.org/10.1016/bs.hescom.2018.05.002 Search in Google Scholar

Mei, Q., Xie, Y., Yuan, W., & Jackson, M. O. (2024). A Turing test of whether ai chat-bots are behaviorally similar to humans. Proceedings of the National Academy of Sciences, 121(9), e2313925121. https://doi.org/10.1073/pnas.2313925121 Search in Google Scholar

Nagel, R. (1995). Unraveling in guessing games: An experimental study. The American Economic Review, 85(5), 1313–1326. https://www.jstor.org/stable/2950991 Search in Google Scholar

Nagel, R., Bühren, C., & Frank, B. (2017). Inspired and inspiring: Hervé moulin and the discovery of the beauty contest game. Mathematical Social Sciences, 90, 191–207. https://doi.org/10.1016/j.mathsocsci.2016.09.001 Search in Google Scholar

OpenAI. (2024). How ChatGPT and our language models are developed. https://help.openai.com/en/articles/7842364-how-chatgpt-and-our-language-models-are-developed Search in Google Scholar

Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., Schulman, J., Hilton, J., Kelton, F., Miller, L., Simens, M., Askell, A., Welinder, P., Christiano, P., Leike, J., & Lowe, R. (2022). Training language models to follow instructions with human feedback. https://proceedings.neu-rips.cc/paper_files/paper/2022/file/b1efde53be364a73914f58805a001731-Paper-Conference.pdf Search in Google Scholar

Phelps, S., & Russell, Y. I. (2023). Investigating emergent goal-like behaviour in large language models using experimental economics. https://doi.org/10.48550/arXiv.2305.07970 Search in Google Scholar

Sclar, M., Choi, Y., Tsvetkov, Y., & Suhr, A. (2023). Quantifying language models’ sensitivity to spurious features in prompt design or: How i learned to start worrying about prompt formatting. https://doi.org/10.48550/arXiv.2310.11324 Search in Google Scholar

Strachan, J. W. A,. Albergo, D., Borghini, G., Pansardi, O., Scaliti, E., Gupta, S., Saxena, K., Rufo, A., Panzeri, S., Manzi, G., Graziano, M. S. A., & Becchio, C. (2024). Testing theory of mind in large language models and humans. Nature Human Behaviour, 8(7), 1285–1295. https://doi.org/10.1038/s41562-024-01882-z Search in Google Scholar

Trality. (2024). Crypto trading bots: The ultimate beginner’s guide. Retrieved January 23, 2024 from https://medium.com/trality/crypto-trading-bots-f46405a7be11 Search in Google Scholar

Tversky, A., & Kahneman, D. (1981). The framing of decisions and the psychology of choice. Science, 211(4481), 453–458. https://www.jstor.org/stable/1685855 Search in Google Scholar

Webb, T., Holyoak, K. J., & Lu, H. (2023). Emergent analogical reasoning in large language models. Nature Human Behaviour, 7(9), 1526–1541. https://doi.org/10.1038/s41562-023-01659-w Search in Google Scholar