1. Horton J. J. Large language models as simulated economic agents: What can we learn from homo silicus? // National Bureau of Economic Research. 2023. DOI: 10.3386/w31122
2. Alekseenko I., Dagaev D., Paklina S., Parshakov P. Strategizing with AI: Insights from a beauty contest experiment // Journal of Economic Behavior & Organization. 2025. Vol. 240. Article 107330. DOI: 10.1016/j.jebo.2025.107330 EDN: LBWXSA
3. Dodivers E., Rafai I. Uncovering the fairness of AI: Exploring focal point, inequality aversion, and altruism in ChatGPT’s dictator game decisions // Economics Bulletin. 2025. Vol. 45, iss. 4. P. 1818-1825.
4. Xie Ch., Chen C., Jia F., Ye Z., Lai Sh., Shu K., Gu J., Bibi A., Hu Z., Jurgens D., Evans J., Torr Ph., Ghanem B., Li G. Can Large Language Model agents simulate human trust behavior? // arXiv. 2024. DOI: 10.48550/arXiv.2402.04559
5. Eckel C. C., Grossman P. J. Are women less selfish than men?: Evidence from dictator experiments // The Economic Journal. 1998. Vol. 108, iss. 448. P. 726-735. DOI: 10.1111/1468-0297.00311
6. Xu R., Sun Y., Guo M., Pan R., Lin H., Sun Le, Han X. AI for social science and social science of AI: A survey // Information Processing & Management. 2024. Vol. 61, iss. 3. Article 103665. DOI: 10.1016/j.ipm.2024.103665 EDN: LFTOKM
7. Akata E., Schulz L., Coda-Forno J. Oh S. J., Bethge M., Schulz E. Playing repeated games with large language models // Nature Human Behaviour. 2025. Vol. 9. P. 1380-1390. DOI: 10.1038/s41562-025-02172-y EDN: YGJBIA
8. Brookins P., DeBacker J. M. Playing games with GPT: What can we learn about a Large Language Model from canonical strategic games? // SSRN. 10 July 2023. 15 p. DOI: 10.2139/ssrn.4493398
9. Fan C., Chen J., Jin Y., He H. Can large language models serve as rational players in game theory? A systematic analysis // Proceedings of the AAAI Conference on Artificial Intelligence. 2024. Vol. 38, no. 16. P. 17960-17967. DOI: 10.1609/aaai.v38i16.29751 EDN: DRCERW
10. Fan L., Li L., Ma Z. Lee S., Yu H., Hemphill L. A bibliometric review of Large Language Models research from 2017 to 2023 // ACM Transactions on Intelligent Systems and Technology. Association for Computing Machinery (ACM). 2024. Vol. 15, iss. 5. Article 91. DOI: 10.1145/3664930
11. Cappelen A. W., Nielsen U. H., Sørensen E. Ø., Tungodden B., Tyran J.-R. Give and take in dictator games // Economics Letters. 2013. Vol. 118, iss. 2. P. 280-283. DOI: 10.1016/j.econlet.2012.10.030
12. Kahneman D., Knetsch J. L., Thaler R. Fairness as a constraint on profit seeking: Entitlements in the market // The American Economic Review. 1986. Vol. 76, no. 4. P. 728-741. URL: https://www.jstor.org/stable/1806070 (дата обращения: 15.04.2025).
13. Ma J. Can machines think like humans? A behavioral evaluation of LLM-agents in dictator games // arXiv. 2024. DOI: 10.31235/osf.io/s37ax
14. Wan Y., Pu G., Sun J., Garimella A., Chang K.-W., Peng N. “Kelly is a warm person, Joseph is a role model”: Gender biases in LLM-generated reference letters // Findings of the Association for Computational Linguistics: EMNLP 2023. Singapore: Association for Computational Linguistics. P. 3730-3748. DOI: 10.18653/v1/2023.findings-emnlp.243
15. Rozado D. Gender and positional biases in LLM-based hiring decisions: Evidence from comparative CV/résumé evaluations // arXiv. 2025. DOI: 10.7717/peerj-cs.3628 EDN: AMQSLK
16. An H., Acquaye C., Wang C., Li Z., Rudinger R. Do Large Language Models discriminate in hiring decisions on the basis of race, ethnicity, and gender? // Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Vol. 2: Short Papers). Bangkok: Association for Computational Linguistics. P. 386-397. DOI: 10.18653/v1/2024.aclshort.37
17. Nomelini G. G., Marcolin C. Gender bias in Large Language Models: A job postings analysis // Revista de Administração Mackenzie. 2024. Vol. 25, no. 6. P. 1-27. DOI: 10.1590/1678-6971/eramd240056 EDN: CWVIDJ
18. Zhong H., Chen S., Liang M. Gender bias of LLM in economics: An existentialism perspective // arXiv. 2024. DOI: 10.48550/arXiv.2410.19775
19. Dong X., Wang Y., Yu Ph. S., Caverlee J. Probing explicit and implicit gender bias through LLM conditional text generation // arXiv. 2023. DOI: 10.48550/arXiv.2311.00306
20. Kotek H., Dockum R., Sun D. Gender bias and stereotypes in Large Language Models // Proceedings of The ACM Collective Intelligence Conference (CI’23). New York: Association for Computing Machinery, 2023. P. 12-24. DOI: 10.1145/3582269.3615599
21. Dong X., Wang Y., Yu Ph. S., Caverlee J. Disclosure and mitigation of gender bias in LLMs // arXiv. 2024. DOI: 10.48550/arXiv.2402.11190
22. Aneja U., Gupta A., Vashistha A. Beyond semantics: Examining gender bias in LLMs deployed within low-resource contexts in India // Proceedings of the 2025 ACM Conference on Fairness, Accountability, and Transparency (FAccT ‘25). New York: Association for Computing Machinery, 2025. P. 2784-2795. DOI: 10.1145/3715275.3732180
23. Jackson S. Elon Musk says he’s planning to create a ‘maximum truth-seeking AI’ that he likes to call ‘TruthGPT’ // Business Insider. URL: https://clck.ru/3SPTeW (дата обращения: 09.09.2025).
24. LLM арена на русском: таблица лидеров // LLM Arena. URL: https://llmarena.ru/ (дата обращения: 09.09.2025).
25. Text Arena // LMarena. URL: https://lmarena.ai/leaderboard/text (дата обращения: 09.09.2025).
26. URL: https://github.com/mrbigant343/LLM_trust_games (дата обращения: 09.09.2025).