Can Large Language Models Capture Human Risk Preferences? A Cross-Cultural Study

Bing Song; Chenyang Wu; Jianing Liu; Sisi Jian; Vinayak Dixit

REVIEW 5 major objections 9 minor 44 references

Can Large Language Models Capture Human Risk Preferences? A Cross-Cultural Study

T0 review · 5 major / 9 minor · reviewed 2026-08-06 · deepseek-v4-flash

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Pith's one-line read The paper reports that LLM-simulated survey respondents, built from demographic profiles, choose more risk-averse lottery options than the human respondents they mimic, and that prompting in Chinese widens the divergence.

desk verdict Useful benchmark result, but the CRRA estimation is under-specified and the abstract overstates the findings. read the letter →

arxiv 2506.23107 v1 pith:UEHFM4V5 submitted 2025-06-29 cs.AI

Bing Song , Jianing Liu , Sisi Jian , Chenyang Wu , Vinayak Dixit This is my paper

classification cs.AI

keywords largelanguagemodelsriskpreferencesCRRAlotterychoicerole-playingagentscross-culturalstudystatedpreferencesurveyprompt

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper asks whether large language models can stand in for human respondents in risky-choice surveys. Across four cities, simulated respondents built from age, gender, education, income, and city cues chose more cautiously in lottery games than the real participants, and their estimated risk-aversion coefficients were generally higher. ChatGPT o1-mini tracked human choices more closely than ChatGPT 4o did. When the prompt language for Hong Kong and Nanjing was switched from English to Chinese, the distance between simulated and real risk attitudes grew, even though those respondents natively speak Chinese. If these results hold, LLM-based stand-ins for human survey respondents will need calibration before they can be trusted in policy or market research about risk.

What carries the argument

The central object is the CRRA risk-preference coefficient $r$, defined through the utility function $U(x)=x^{1-r}/(1-r)$, with $r>0$ meaning risk aversion, $r=0$ risk neutrality, and $r<0$ risk seeking. The paper adopts the linear specification $r = r_0 + \alpha_1 X_1$ from reference [44] to connect socio-demographic variables $X_1$ to risk preferences, and it builds role-playing agents whose prompts combine an individual profile (age, gender, education, income, city) with chain-of-thought planning instructions and a closed-form action rule that outputs only the chosen lottery option. Each task is simulated three times and the majority choice is kept as the simulated response.

What would settle it

Re-estimate the risk coefficients from the same human and LLM choices with an explicit stochastic choice model, such as a logistic choice rule over CRRA utilities with an error term, and compare the LLM-human gap; if the systematic conservative bias disappears or reverses under that specification, the headline result is an artifact of the unstated mapping from choices to risk scores.

Watch

Extended reading notes

Core claim

Using the Constant Relative Risk Aversion (CRRA) framework, the paper estimates risk-preference coefficients $r$ from real and LLM-simulated lottery choices. It finds that ChatGPT 4o's simulated respondents are systematically more risk-averse than the human respondents in all four city samples, and that ChatGPT o1-mini is generally more risk-averse than humans as well, though its estimates are closer to the observed human values, with Hong Kong as the one reported exception. The paper further finds that for the Hong Kong and Nanjing samples, presenting the same tasks in Chinese rather than English moves the simulated risk attitudes further from the human estimates, a bias amplification the authors attribute to the models' predominantly English-language training.

Load-bearing premise

The argument assumes that a single CRRA risk-score equation taken from an earlier transportation study converts each respondent's lottery answers into a comparable risk-aversion number across four differently designed lotteries, but the paper does not state the choice model that performs this conversion.

Editorial extensions

If this is right

Uncalibrated LLM stand-ins will make populations look more risk-averse than they are, pushing policy recommendations toward overly conservative measures.
Model choice matters: ChatGPT o1-mini is a closer proxy for human risk preferences than ChatGPT 4o in these tasks, so conclusions drawn from one LLM may not transfer to another.
Prompt language is an active variable: even when respondents natively speak Chinese, Chinese prompts can degrade fidelity, so localization alone does not improve simulation accuracy.
Demographically narrow samples, such as the Dhaka taxi-driver data, can erase measurable differences between models, suggesting that LLMs fall back on generic rather than person-specific risk priors.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

A testable extension: vary the stakes or the gain-loss framing while holding the demographic profile fixed; if the conservative bias grows with stakes, the mechanism is closer to loss aversion or safety-oriented calibration than to CRRA curvature alone.
An inference not in the paper: because the mapping from lottery answers to individual $r$ estimates is never spelled out, an explicit stochastic choice model, such as a logit over CRRA utilities, could change the size or even the sign of the reported LLM-human gap.
A testable extension: run the same English-versus-Chinese comparison with models trained primarily on Chinese corpora; the paper's training-bias account predicts the gap would shrink or reverse.
An inference not in the paper: the four lotteries differ in payoff magnitudes and in risky-safe versus risky-risky framing, so a matched-design replication is needed before the cross-city differences in human risk aversion can be read as cultural rather than artifactual.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

Short version: this is a useful empirical benchmark, not a methodological breakthrough. The headline result — two ChatGPT models mostly come out more risk-averse than human respondents on real lottery data, and Chinese prompts widen the gap — is plausible and worth knowing. But the paper needs a major revision before the numbers can be trusted.

What's new: applying role-playing language agents to four real stated-preference lottery datasets across four cities, with demographic profiles, is a legitimate new empirical setting. The prompt-language effect in Chinese is a genuinely new observation, and the majority-voting over three runs is a sensible noise-reduction step.

The biggest problem is the estimation step. Section 4.2 gives the CRRA utility function and cites [44] for r = r0 + α1X1, but never states how an individual's r is recovered from a series of binary choices — no likelihood, no choice rule, no standard errors. Tables 6-9 report means and significance stars only. Without that, I cannot tell whether the differences reflect risk-preference curvature or artifacts of the choice model, the lottery mix, or the majority-voting rule. The comparability of r across four survey designs with different payoff magnitudes and risky-safe vs risky-risky frames is asserted, not defended. The stress-test concern about identification is fair.

There is also a factual overstatement in the abstract and Section 6.1: they say both models exhibit more risk-aversion than humans, but Table 7 shows o1-mini in Hong Kong at 0.509 vs real 0.765 — less risk-averse. That needs correction. Minor: the sample prompt says "nine consecutive lottery questions" but Dhaka and Nanjing have ten tasks. Data and code are not public, so a reader cannot verify any of the central estimates.

Who this is for: researchers using LLM agents to simulate travelers or consumers; it gives a concrete caution. The paper deserves peer review after the estimation procedure is specified, the overclaim fixed, and ideally the data/code released. I'd send it with a request for major revision.

Referee Report

5 major / 9 minor

Summary. This paper asks whether ChatGPT 4o and ChatGPT o1-mini can mimic human risky choices by simulating respondents in four transport stated-preference surveys (Sydney, Hong Kong, Dhaka, Nanjing). The authors feed each LLM a profile built from age, gender, education, and income, have the model answer repeated binary lottery tasks in English (and, for Hong Kong and Nanjing, also in Chinese), take a majority vote over three runs, and estimate CRRA risk-aversion parameters r using the specification r = r0 + α1X1 from reference [44]. They conclude that LLM-simulated respondents are generally more risk-averse than humans, that o1-mini is closer to human estimates than 4o, and that Chinese prompts produce larger deviations. The paper positions this as evidence of an intrinsic conservative bias and warns against using LLMs for risk-sensitive policymaking without calibration.

Significance. If the estimation were fully specified and reproducible, this would be a useful cross-cultural, cross-lingual benchmark for LLM-based behavioral simulation: four diverse datasets, two models, controlled demographic conditioning, and a formal utility framework. The paper also makes concrete, falsifiable claims about systematic overestimation and language-dependent bias, and it draws policy implications that are directly testable. However, the manuscript does not provide the choice model, likelihood, standard errors, or code/data needed to verify the point estimates, and one headline claim is contradicted by the paper's own Table 7. The contribution is potentially valuable but not yet verifiable in this form.

major comments (5)

[§4.2, Eq. (1)] The paper never specifies the probabilistic choice model that maps each binary lottery answer to an individual r estimate. Eq. (1) defines CRRA utility and the text invokes r = r0 + α1X1 from [44], but there is no likelihood, no statement of whether r is estimated per person or from all choices pooled, and no parameter uncertainty. Tables 6–9 therefore report means of an unstated estimator, and the paired t-tests in Section 5.1 are presented only through significance stars, with no standard errors, p-values, or test statistics. The central quantitative claims that LLMs are more risk-averse than humans and that o1-mini is closer cannot be assessed without this identification step. Please state the choice model (e.g., a logit over CRRA expected utilities with a specific noise term), the estimation method, and report standard errors or confidence intervals for each mean.
[Abstract; §6.1] The headline claim that “both ChatGPT 4o and ChatGPT o1-mini systematically exhibited higher levels of risk aversion compared to actual human respondents” is contradicted by Table 7. For Hong Kong, the o1-mini mean is 0.509 versus the real mean of 0.765, so the model is less risk-averse than the human sample. The text in Section 5.1 carefully avoids saying “higher” for that cell, but the abstract and Section 6.1 overstate the result. Please qualify the claim to the datasets/cells where the direction holds, or add a statistical test that supports a general “systematic” direction.
[§3.2, Table 4] The Dhaka lottery description conflicts with Table 4. The text states that in the left lottery the lower payoff 6 occurs with probability p and the higher payoff 8 with probability 1−p, and symmetrically for the right lottery (1 with p and 20 with 1−p). However, the table's expected values (e.g., 6.2 = 0.1×8 + 0.9×6 and 2.9 = 0.1×20 + 0.9×1) show that p is instead the probability of the first-listed payoff, i.e., the higher payoff in both lotteries. Because every Dhaka CRRA estimate, and hence the Dhaka rows of Tables 6–8 and Figure 1(c), depends on the correct probability mapping, this discrepancy must be resolved and corrected.
[§5.2, Table 9] Table 9 does not test the English-versus-Chinese difference that Section 5.2 claims. The reported significance stars are for each simulated mean relative to the real mean (the column header “Ha: mean(diff) ≠ 0”), not for the difference between the English and Chinese simulated means. To support the statement that switching to Chinese prompts consistently amplifies the bias, the authors need a paired test of English versus Chinese simulated risk attitudes, with standard errors, and ideally a test on absolute deviations from the real values. As it stands, the linguistic effect is not statistically established.
[§3.2, Tables 2–5] The four lottery designs are not directly comparable for the cross-city claim. Sydney and Hong Kong pair a risky option against a fixed payoff; Dhaka and Nanjing pair two risky options; and the number of tasks (9 versus 10), payoff magnitudes, and probability grids differ. Under CRRA expected utility, nominal r comparisons are only meaningful if the same choice rule is assumed across designs. The manuscript should include a robustness analysis using a single structural model, or an alternative model, to show that the cross-city and cross-language comparisons are not artifacts of design differences, option-order effects, or probability-weighting deviations in LLM choices.

minor comments (9)

[§4.1] The heading “Planing” is a typo and should read “Planning”.
[Table 4] The header row “Prob. payoff 1 2” is unclear; define the columns (e.g., Prob(payoff 1), Prob(payoff 2)) and label which payoff is lower and which is higher, consistent with the text.
[§4.1] The sample prompt tells the agent that there will be “nine consecutive lottery questions,” but the Dhaka and Nanjing surveys have ten tasks; the prompt should be dataset-specific.
[§4.1] The prompt text “a age-year-old” should be “an age-year-old”.
[§4.2, Eq. (1)] Eq. (1) is undefined at r = 1; adding the standard limiting form would avoid a gap for estimates near risk neutrality.
[Tables 6–9] The significance-star notation is used without t-statistics or p-values; even after the estimation is clarified, the tables should include measures of uncertainty.
[Figure 1] The kernel density plots should state the bandwidth and the sample sizes; with n = 64 in Sydney, density estimates are noisy and may be misleading.
[Data availability] For a simulation study with bespoke prompts and a proprietary LLM, releasing at least the prompts and the estimation code would be necessary for independent verification; the current statement limits access to aggregated data only.
[§3.2] The text says “the primary focus should be on the differences in expected values between the left and right options,” but for CRRA estimation the full payoff distributions, not just EV differences, are needed; the text should not suggest otherwise.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: risk attitudes are refit from separately simulated LLM choices, not defined by the comparison.

full rationale

The paper's derivation chain is: (i) take human lottery choices from four prior surveys; (ii) prompt LLMs with demographic profiles and the same lottery tasks; (iii) estimate CRRA risk coefficients separately from human choices and from LLM-simulated choices; (iv) compare the resulting mean r values across cities, models, and prompt languages. None of these steps defines the reported difference in terms of the inputs by construction. The CRRA utility function U(x) = x^(1-r)/(1-r) and the linear specification r = r0 + α1X1 are standard modeling assumptions cited from [44], but the paper does not use [44]'s fitted coefficients to generate Tables 6-9; the r values are presented as estimates from the observed and simulated choices. The finding that LLM-simulated choices imply higher r than human choices is an empirical outcome of the LLMs' outputs, not an algebraic consequence of the prompts or the estimation equation. Likewise, the Chinese-vs-English difference is a measured comparison of two sets of simulated choices, not a pre-imposed relation. The self-citations to [31]-[34] supply the underlying survey datasets, which are external empirical inputs, and [44] supplies a model form; these are not cited as proof of the paper's central claim. The manuscript does under-specify the probabilistic choice/estimation model and the comparability of r across the four lottery designs, but that is an identification and transparency limitation, not circular reasoning. No equation is shown to be equivalent to its own output, and no fitted parameter is renamed as a prediction. Therefore no circular step is exhibited.

Assumptions & free parameters 2 free parameters · 4 assumptions · 0 invented entities

The paper's quantitative conclusions rest on a fitted CRRA model whose estimation procedure is not fully specified in the text. r0 and α1 are fitted to the survey choices; the choice model itself is an assumption about how people and LLMs resolve lotteries. No new physical or conceptual entities are introduced.

free parameters (2)

r0 (constant CRRA risk parameter) = not reported in paper
Intercept in r = r0 + α1 X1 fitted to each dataset's choices; all reported risk attitudes (Tables 6 to 9) are outputs of this fit.
α1 (coefficient vector for age, gender, education, income) = not reported in paper
Socio-demographic coefficients in the same equation; their values determine individual-level risk estimates.

assumptions (4)

domain assumption Choices are generated by CRRA utility U(x) = x^(1-r)/(1-r) with a single risk parameter r
Invoked in Section 4.2; if subjects or LLMs use probability weighting or non-expected utility, the estimated r conflates risk attitude with other biases.
domain assumption Risk parameter shifts linearly with demographics: r = r0 + α1 X1
Equation in Section 4.2 adopted from reference [44]; linearity and variable set are assumed, not derived.
domain assumption CRRA estimates from four different lottery designs are comparable within and across cities
Assumed when pooling Tables 6 to 9 and Figure 1; different payoff magnitudes, task counts, and both-risky versus risky-safe frames may affect elicited r.
domain assumption LLM choices can be treated as realizations of the same CRRA decision process as human choices
Core to interpreting simulated choices as risk attitudes; the paper provides no validity check that LLM choice patterns satisfy CRRA or transitivity.

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Cite this review

Pith. "Pith review of Can Large Language Models Capture Human Risk Preferences? A Cross-Cultural Study." pith.science (2026). https://pith.science/paper/UEHFM4V5

@misc{pith2026250623107,
  author       = {Pith},
  title        = {Pith review of: Can Large Language Models Capture Human Risk Preferences? A Cross-Cultural Study},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/UEHFM4V5}},
  note         = {Machine review of arXiv:2506.23107}
}

read the original abstract

Large language models (LLMs) have made significant strides, extending their applications to dialogue systems, automated content creation, and domain-specific advisory tasks. However, as their use grows, concerns have emerged regarding their reliability in simulating complex decision-making behavior, such as risky decision-making, where a single choice can lead to multiple outcomes. This study investigates the ability of LLMs to simulate risky decision-making scenarios. We compare model-generated decisions with actual human responses in a series of lottery-based tasks, using transportation stated preference survey data from participants in Sydney, Dhaka, Hong Kong, and Nanjing. Demographic inputs were provided to two LLMs -- ChatGPT 4o and ChatGPT o1-mini -- which were tasked with predicting individual choices. Risk preferences were analyzed using the Constant Relative Risk Aversion (CRRA) framework. Results show that both models exhibit more risk-averse behavior than human participants, with o1-mini aligning more closely with observed human decisions. Further analysis of multilingual data from Nanjing and Hong Kong indicates that model predictions in Chinese deviate more from actual responses compared to English, suggesting that prompt language may influence simulation performance. These findings highlight both the promise and the current limitations of LLMs in replicating human-like risk behavior, particularly in linguistic and cultural settings.

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Reviewed August 6, 2026 · model on record in the stance chip above.

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