Money Illusion in Large Language Models: An Exploratory Replication Study

Milos Ciganovic, Nicole Macchitella and Luca Panaccione
Additional contact information
Nicole Macchitella: Sapienza University of Rome

Journal of Behavioral Economics for Policy, 2025, vol. 9, issue 1, 75-80

Abstract: As Large Language Models become increasingly integrated in daily life, understanding the extent to which they may reflect behavioural tendencies embedded in the training data becomes a relevant issue. In this paper, we conduct an exploratory replication study on the occurrence of "money illusion" across six Large Language Models: GPT-5, GPT-5.1, DeepSeek-V3.2, Grok 4.1, Sonnet 4.5, and Gemini-3-pro-preview. Specifically, we replicate the survey questions of Shafir, Diamond, and Tversky (1997) and report on the models' responses across twelve scenarios covering earnings, transactions, contracts, and mental accounting. Our exploratory evidence tends to suggest that Large Language Models generally base their responses on real terms rather than nominal ones, particularly in contexts that require objective evaluations, even though there are some exceptions. However, it also tends to suggest that their responses more frequently aligned with those of the human participants in the original study when more subjective evaluations were involved. While a more systematic investigation based on a larger dataset is required to reach more definitive conclusion, we nonetheless hope that our results could be useful for future research analyses.

Keywords: money illusion; large language models; cognitive bias (search for similar items in EconPapers)
Date: 2025
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://sabeconomics.org/journal/RePEc/beh/JBEPv1/articles/JBEP-9-1-6.pdf (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:beh:jbepv1:v:9:y:2025:i:1:p:75-80

Access Statistics for this article

Journal of Behavioral Economics for Policy is currently edited by Giuseppe Attanasi

More articles in Journal of Behavioral Economics for Policy from Society for the Advancement of Behavioral Economics (SABE) Contact information at EDIRC.
Bibliographic data for series maintained by SABE Journal JBEP ().