Rethinking Prospect Theory for LLMs: Revealing the Instability of Decision-Making under Epistemic Uncertainty
Pith reviewed 2026-05-18 23:27 UTC · model grok-4.3
The pith
Modeling LLM decisions with Prospect Theory is not reliable across models and unstable under epistemic uncertainty.
A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.
Core claim
We design a three-stage workflow based on a classic behavioural economics experimental setup. We first estimate PT parameters with economics questions and evaluate PT's fitness with performance metrics. We then derive probability mappings for epistemic markers in the same context, and inject these mappings into the prompt to investigate the stability of PT parameters. Our findings suggest that modelling LLMs' decision-making with PT is not consistently reliable across models, and applying Prospect Theory to LLMs is likely not robust to epistemic uncertainty.
What carries the argument
A three-stage workflow that estimates Prospect Theory parameters from economics questions, derives probability mappings for epistemic markers, and injects those mappings to test parameter stability.
If this is right
- PT-based frameworks should not be deployed where epistemic ambiguity is common.
- The results provide guidance for interpreting LLM behavior under uncertainty.
- Future alignment work for LLM decision-making must account for this lack of robustness.
Where Pith is reading between the lines
- LLMs may process linguistic uncertainty through internal mechanisms that differ from the value and probability weighting functions in Prospect Theory.
- Alternative models of uncertainty, such as those based on fuzzy sets or explicit calibration, could be compared directly against Prospect Theory on the same tasks.
- The instability might be reduced by targeted fine-tuning on examples that contain epistemic markers.
Load-bearing premise
The probability mappings derived for epistemic markers accurately represent how LLMs internally interpret those markers when making choices.
What would settle it
Re-running the three-stage experiment on the same models and finding that Prospect Theory parameters remain unchanged after the epistemic mappings are injected into the prompts.
read the original abstract
Prospect Theory (PT) models human decision-making behaviour under uncertainty, among which linguistic uncertainty is commonly adopted in real-world scenarios. Although recent studies have developed some frameworks to test PT parameters for Large Language Models (LLMs), few have considered the fitness of PT itself on LLMs. Moreover, whether PT is robust under linguistic uncertainty perturbations, especially epistemic markers (e.g. "likely"), remains highly under-explored. To address these gaps, we design a three-stage workflow based on a classic behavioural economics experimental setup. We first estimate PT parameters with economics questions and evaluate PT's fitness with performance metrics. We then derive probability mappings for epistemic markers in the same context, and inject these mappings into the prompt to investigate the stability of PT parameters. Our findings suggest that modelling LLMs' decision-making with PT is not consistently reliable across models, and applying Prospect Theory to LLMs is likely not robust to epistemic uncertainty. The findings caution against the deployment of PT-based frameworks in real-world applications where epistemic ambiguity is prevalent, giving valuable insights in behaviour interpretation and future alignment direction for LLM decision-making.
Editorial analysis
A structured set of objections, weighed in public.
Referee Report
Summary. The paper presents a three-stage experimental workflow to test the applicability of Prospect Theory (PT) to LLMs and its robustness under epistemic uncertainty. Stage 1 estimates standard PT parameters (alpha, beta, lambda, gamma, delta) from economics choice questions and evaluates model fitness via performance metrics. Stage 2 derives probability mappings for epistemic markers such as 'likely'. Stage 3 injects the derived numerical mappings into decision prompts to measure resulting shifts in the fitted PT parameters. The authors conclude that PT-based modeling of LLM decisions is not consistently reliable across models and is not robust to epistemic uncertainty, with implications for real-world deployment and LLM alignment.
Significance. If the results are robust, the work is significant in demonstrating empirical limitations of directly transferring human Prospect Theory to LLMs, particularly when linguistic epistemic markers introduce uncertainty. The structured three-stage design is a clear strength that allows separate assessment of PT fitness and stability. It supplies falsifiable predictions about parameter instability that could inform future alignment research. The significance would be strengthened by confirming that stage-2 mappings are internally equivalent to stage-3 usage.
major comments (2)
- [three-stage workflow] The three-stage workflow (abstract and methods description): the central claim that observed PT parameter shifts after injection demonstrate genuine instability under epistemic uncertainty rests on the untested assumption that the probability mappings derived in stage 2 are the same quantities the LLM uses when the markers appear inside stage-3 choice prompts. No attention, logit, or internal-state probe is reported to rule out the alternative that the numerical substitution acts as a new surface cue or triggers re-interpretation of the original marker.
- [Methods and results] Methods and results sections: the manuscript reports performance metrics and parameter changes but provides no details on the specific LLMs tested, number of trials per condition, exact prompt templates, or statistical procedures used to fit and compare PT parameters. These omissions are load-bearing because they prevent verification that the reported instability is robust rather than driven by post-hoc choices or low statistical power.
minor comments (2)
- [abstract and introduction] The abstract and introduction would benefit from explicit comparison to prior PT-LLM studies to better situate the novelty of the three-stage design.
- [results] Figure or table captions should clarify whether the reported metrics are averaged across models or shown per model, as this affects the 'not consistently reliable across models' claim.
Simulated Author's Rebuttal
We thank the referee for their constructive comments, which have helped us clarify the scope and limitations of our three-stage workflow. We address each major comment below and have revised the manuscript accordingly to improve transparency and acknowledge methodological boundaries.
read point-by-point responses
-
Referee: [three-stage workflow] The three-stage workflow (abstract and methods description): the central claim that observed PT parameter shifts after injection demonstrate genuine instability under epistemic uncertainty rests on the untested assumption that the probability mappings derived in stage 2 are the same quantities the LLM uses when the markers appear inside stage-3 choice prompts. No attention, logit, or internal-state probe is reported to rule out the alternative that the numerical substitution acts as a new surface cue or triggers re-interpretation of the original marker.
Authors: We appreciate this observation on the interpretive assumptions underlying our design. The workflow measures the behavioral impact on fitted PT parameters when derived numerical equivalents replace epistemic markers, which directly tests robustness at the level of observable choices—the primary output relevant to decision-making applications. We did not perform internal probes (attention, logit, or state analysis) as these fall outside the paper's focus on external validity and parameter stability. We have added an explicit limitations paragraph in the Discussion section acknowledging that the observed shifts could partly reflect surface-level cue effects or re-interpretation, and we note that future work could incorporate mechanistic interpretability to test internal equivalence. revision: partial
-
Referee: [Methods and results] Methods and results sections: the manuscript reports performance metrics and parameter changes but provides no details on the specific LLMs tested, number of trials per condition, exact prompt templates, or statistical procedures used to fit and compare PT parameters. These omissions are load-bearing because they prevent verification that the reported instability is robust rather than driven by post-hoc choices or low statistical power.
Authors: We agree that these details are essential for reproducibility and verification. In the revised manuscript we have substantially expanded the Methods section to specify: the exact LLMs evaluated (including model versions and access methods), the number of trials per condition (with ranges and justification for sample sizes), the full prompt templates for each stage (now included verbatim in the main text or as supplementary material), and the statistical procedures (including the optimization routine for PT parameter fitting, goodness-of-fit metrics, and tests for parameter differences such as bootstrap confidence intervals and paired comparisons with correction for multiple testing). We have also added a supplementary table summarizing raw trial counts and variance estimates to address concerns about statistical power. revision: yes
Circularity Check
No significant circularity; experimental workflow is self-contained
full rationale
The paper describes a three-stage empirical workflow: estimating PT parameters on economics questions, deriving probability mappings for epistemic markers, and injecting mappings to observe parameter stability. Central claims rest on observed changes in fitted parameters and performance metrics across models, without any mathematical derivation, self-definitional equations, or fitted inputs presented as independent predictions. No load-bearing self-citations or uniqueness theorems reduce the results to prior author work by construction. The analysis is externally falsifiable via replication of the prompt-based experiments and does not rely on internal loops.
Axiom & Free-Parameter Ledger
free parameters (1)
- Prospect Theory parameters (alpha, beta, lambda, gamma, delta)
axioms (1)
- domain assumption LLMs possess stable internal decision weights that can be captured by the Prospect Theory functional form
Lean theorems connected to this paper
-
IndisputableMonolith/Cost/FunctionalEquation.leanwashburn_uniqueness_aczel unclear?
unclearRelation between the paper passage and the cited Recognition theorem.
We design a three-stage workflow... estimate PT parameters with economics questions... derive probability mappings for epistemic markers... inject these mappings into the prompt
-
IndisputableMonolith/Foundation/ArithmeticFromLogic.leanembed_strictMono_of_one_lt unclear?
unclearRelation between the paper passage and the cited Recognition theorem.
Linguistic uncertainty causes moderate shifts in risk preference but affects other decision parameters more profoundly
What do these tags mean?
- matches
- The paper's claim is directly supported by a theorem in the formal canon.
- supports
- The theorem supports part of the paper's argument, but the paper may add assumptions or extra steps.
- extends
- The paper goes beyond the formal theorem; the theorem is a base layer rather than the whole result.
- uses
- The paper appears to rely on the theorem as machinery.
- contradicts
- The paper's claim conflicts with a theorem or certificate in the canon.
- unclear
- Pith found a possible connection, but the passage is too broad, indirect, or ambiguous to say the theorem truly supports the claim.
discussion (0)
Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.