Araştırma Makalesi

CONTEXT OVER LANGUAGE: PERSONA-DRIVEN INTERTEMPORAL CHOICE IN LARGE LANGUAGE MODELS

Cilt: 17 22 Ekim 2026
Ali Kemal Başbuğ *
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CONTEXT OVER LANGUAGE: PERSONA-DRIVEN INTERTEMPORAL CHOICE IN LARGE LANGUAGE MODELS

Öz

Large language models (LLMs) are increasingly used as experimental subjects in behavioral and economic research. However, it remains unclear whether their decisions reflect stable preferences or context-dependent responses. This study examines intertemporal choice behavior in LLMs within a controlled experimental framework. We implement a fully factorial design across four models (ChatGPT, Gemini, Copilot, and Claude), varying persona (financially stressed vs. disciplined), delay (1 vs. 6 months), reward magnitude (650–900 $), and prompt framing (direct vs. human-like). Each condition is repeated to approximate response distributions. Results show that LLM behavior is structured but not preference-stable. Decisions are primarily driven by persona conditioning: financially disciplined agents choose delayed rewards significantly more often than stressed agents. Delay and reward magnitude affect choices in line with temporal discounting, while prompt framing has negligible effects. We interpret these findings by conceptualizing LLMs as context-dependent behavioral simulators rather than agents with stable preferences. The contribution is not to establish human equivalence, but to identify the internal consistency and structure of LLM responses under controlled variation. Overall, the results highlight the importance of experimental design and suggest that LLM outputs should be understood as conditional simulations rather than stable expressions of preference.

Anahtar Kelimeler

Large language models, Behavioral economics, Intertemporal choice, Experimental design, Context-dependent preferences

Kaynakça

  1. Aher, G. V., Arriaga, R. I., & Kalai, A. T. (2023). Using large language models to simulate multiple humans and replicate human subject studies. In A. Krause, E. Brunskill, K. Cho, B. Engelhardt, S. Sabato, & J. Scarlett (Eds.), Proceedings of the 40th International Conference on Machine Learning (Proceedings of Machine Learning Research, Vol. 202, pp. 337–371). PMLR. https://proceedings.mlr.press/v202/aher23a.html
  2. Anthropic. (2025, May 22). System card: Claude Opus 4 & Claude Sonnet 4. Anthropic. https://www.anthropic.com/claude-4-system-card
  3. 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
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  6. Bowman, S. R. (2023). Eight things to know about large language models. https://doi.org/10.48550/arXiv.2304.00612
  7. Chen, L., Zaharia, M., & Zou, J. (2024). How is ChatGPT's behavior changing over time? Harvard Data Science Review, 6(2). https://doi.org/10.1162/99608f92.5317da47
  8. Dillon, 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
  9. Frederick, S., Loewenstein, G., & O’Donoghue, T. (2002). Time discounting and time preference: A critical review. Journal of Economic Literature, 40(2), 351–401. https://doi.org/10.1257/jel.40.2.351
  10. Gao, C., Lan, X., Li, N., Yuan, Y., Ding, J., Zhou, Z., Xu, F., & Li, Y. (2024). Large language models empowered agent-based modeling and simulation: A survey and perspectives. Humanities and Social Sciences Communications, 11(1), Article 1259. https://doi.org/10.1057/s41599-024-03611-3

Ayrıntılar

Birincil Dil

İngilizce

Konular

Ekonomi Teorisi (Diğer)

Bölüm

Araştırma Makalesi

Yazarlar

Yayımlanma Tarihi

22 Ekim 2026

Gönderilme Tarihi

17 Nisan 2026

Kabul Tarihi

2 Eylül 2026

Yayımlandığı Sayı

Yıl 2026 Cilt: 17

DOI

https://doi.org/10.54688/ayd.1932565

IZ

https://izlik.org/JA74RB43MX

Kaynak Göster

RIS / Bibtex
APA
Başbuğ, A. K. (2026). CONTEXT OVER LANGUAGE: PERSONA-DRIVEN INTERTEMPORAL CHOICE IN LARGE LANGUAGE MODELS. Journal of Academic Approaches, 17, 1-31. https://doi.org/10.54688/ayd.1932565
AMA
1.Başbuğ AK. CONTEXT OVER LANGUAGE: PERSONA-DRIVEN INTERTEMPORAL CHOICE IN LARGE LANGUAGE MODELS. Journal of Academic Approaches. 2026;17:1-31. doi:10.54688/ayd.1932565
Chicago
Başbuğ, Ali Kemal. 2026. “CONTEXT OVER LANGUAGE: PERSONA-DRIVEN INTERTEMPORAL CHOICE IN LARGE LANGUAGE MODELS”. Journal of Academic Approaches 17 (Ekim): 1-31. https://doi.org/10.54688/ayd.1932565.
EndNote
Başbuğ AK (01 Ekim 2026) CONTEXT OVER LANGUAGE: PERSONA-DRIVEN INTERTEMPORAL CHOICE IN LARGE LANGUAGE MODELS. Journal of Academic Approaches 17 1–31.
IEEE
[1]A. K. Başbuğ, “CONTEXT OVER LANGUAGE: PERSONA-DRIVEN INTERTEMPORAL CHOICE IN LARGE LANGUAGE MODELS”, Journal of Academic Approaches, c. 17, ss. 1–31, Eki. 2026, doi: 10.54688/ayd.1932565.
ISNAD
Başbuğ, Ali Kemal. “CONTEXT OVER LANGUAGE: PERSONA-DRIVEN INTERTEMPORAL CHOICE IN LARGE LANGUAGE MODELS”. Journal of Academic Approaches 17 (01 Ekim 2026): 1-31. https://doi.org/10.54688/ayd.1932565.
JAMA
1.Başbuğ AK. CONTEXT OVER LANGUAGE: PERSONA-DRIVEN INTERTEMPORAL CHOICE IN LARGE LANGUAGE MODELS. Journal of Academic Approaches. 2026;17:1–31.
MLA
Başbuğ, Ali Kemal. “CONTEXT OVER LANGUAGE: PERSONA-DRIVEN INTERTEMPORAL CHOICE IN LARGE LANGUAGE MODELS”. Journal of Academic Approaches, c. 17, Ekim 2026, ss. 1-31, doi:10.54688/ayd.1932565.
Vancouver
1.Ali Kemal Başbuğ. CONTEXT OVER LANGUAGE: PERSONA-DRIVEN INTERTEMPORAL CHOICE IN LARGE LANGUAGE MODELS. Journal of Academic Approaches. 01 Ekim 2026;17:1-31. doi:10.54688/ayd.1932565