Can LLMs Replace Economic Choice Prediction Labs? The Case of Language-based Persuasion Games

[2401.17435] Can LLMs Replace Economic Choice Prediction Labs? The Case of Language-based Persuasion Games Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Machine Learning arXiv:2401.17435 (cs) [Submitted on 30 Jan 2024 ( v1 ), last revised 20 Nov 2025 (this version, v5)] Title: Can LLMs Replace Economic Choice Prediction Labs? The Case of Language-based Persuasion Games Authors: Eilam Shapira , Omer Madmon , Roi Reichart , Moshe Tennenholtz View a PDF of the paper titled Can LLMs Replace Economic Choice Prediction Labs? The Case of Language-based Persuasion Games, by Eilam Shapira and 3 other authors View PDF HTML (experimental) Abstract: Human choice prediction in economic contexts is crucial for applications in marketing, finance, public policy, and more. This task, however, is often constrained by the difficulties in acquiring human choice data. With most experimental economics studies focusing on simple choice settings, the AI community has explored whether LLMs can substitute for humans in these predictions and examined more complex experimental economics settings. However, a key question remains: can LLMs generate training data for human choice prediction? We explore this in language-based persuasion games, a complex economic setting involving natural language in strategic interactions. Our experiments show that models trained on LLM-generated data can effectively predict human behavior in these games and even outperform models trained on actual human data. Beyond data generation, we investigate the dual role of LLMs as both data generators and predictors, introducing a comprehensive empirical study on the effectiveness of utilizing LLMs for data generation, human choice prediction, or both. We then utilize our choice prediction framework to analyze how strategic factors shape decision-making, showing that interaction history (rather than linguistic sentiment alone) plays a key role in predicting human decision-making in repeated interactions. Particularly, when LLMs capture history-dependent decision patterns similarly to humans, their predictive success improves substantially. Finally, we demonstrate the robustness of our findings across alternative persuasion-game settings, highlighting the broader potential of using LLM-generated data to model human decision-making. Subjects: Machine Learning (cs.LG) ; Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Computer Science and Game Theory (cs.GT); Human-Computer Interaction (cs.HC) Cite as: arXiv:2401.17435 [cs.LG] (or arXiv:2401.17435v5 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2401.17435 Focus to learn more arXiv-issued DOI via DataCite Related DOI : https://doi.org/10.1613/jair.1.21102 Focus to learn more DOI(s) linking to related resources Submission history From: Eilam Shapira [ view email ] [v1] Tue, 30 Jan 2024 20:49:47 UTC (587 KB) [v2] Thu, 1 Feb 2024 18:57:18 UTC (601 KB) [v3] Thu, 7 Mar 2024 16:47:00 UTC (600 KB) [v4] Wed, 14 Aug 2024 19:23:43 UTC (2,063 KB) [v5] Thu, 20 Nov 2025 00:08:29 UTC (1,819 KB) Full-text links: Access Paper: View a PDF of the paper titled Can LLMs Replace Economic Choice Prediction Labs? The Case of Language-based Persuasion Games, by Eilam Shapira and 3 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.LG < prev | next > new | recent | 2024-01 Change to browse by: cs cs.AI cs.CL cs.GT cs.HC References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... 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