References
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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.
- Guo, F. (2023). GPT in game theory experiments. https://doi.org/10.48550/arXiv.2305.05516.
- 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.
- Hamill, L., & Gilbert, N. (2015). Agent-based modelling in economics. John Wiley & Sons.
- 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
- HuggingFace. (2022). Illustrating reinforcement learning from human feedback (RLHF). https://huggingface.co/blog/rlhf
- Huijzer, R., & Hill, Y. (2023, January 31). Large language models show human behavior. https://doi.org/10.31234/osf.io/munc9
- 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
- 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
- Keynes, J. M. (1936). The general theory of interest, employment and money. Macmillan.
- 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
- 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
- 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
- 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
- Nagel, R. (1995). Unraveling in guessing games: An experimental study. The American Economic Review, 85(5), 1313–1326. https://www.jstor.org/stable/2950991
- 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
- 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
- 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
- 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
- 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
- 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
- Trality. (2024). Crypto trading bots: The ultimate beginner’s guide. Retrieved January 23, 2024 from https://medium.com/trality/crypto-trading-bots-f46405a7be11
- 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
- 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