# Decision-Making Behavior Evaluation Framework for LLMs under Uncertain Context

arXiv: https://arxiv.org/abs/2406.05972 · HTML: https://mlsanity.com/paper/2406.05972/

## Key facts

- Cited by 41 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 46 references that are in the map.
- First posted on arXiv 10 Jun 2024.

## Builds on (most cited first, 14 of 14)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [Large Language Models Are Human-Level Prompt Engineers](https://mlsanity.com/paper/2211.01910/) (2022)
- [BloombergGPT: A Large Language Model for Finance](https://mlsanity.com/paper/2303.17564/) (2023)
- [GLaM: Efficient Scaling of Language Models with Mixture-of-Experts](https://mlsanity.com/paper/2112.06905/) (2021)
- [SnapKV: LLM Knows What You are Looking for Before Generation](https://mlsanity.com/paper/2404.14469/) (2024)
- [Large Language Models as Optimizers](https://mlsanity.com/paper/2309.03409/) (2023)
- [Can Large Language Models Transform Computational Social Science?](https://mlsanity.com/paper/2305.03514/) (2023)
- [Playing repeated games with Large Language Models](https://mlsanity.com/paper/2305.16867/) (2023)
- [Bias Runs Deep: Implicit Reasoning Biases in Persona-Assigned LLMs](https://mlsanity.com/paper/2311.04892/) (2023)
- [Can Large Language Models Serve as Rational Players in Game Theory? A Systematic Analysis](https://mlsanity.com/paper/2312.05488/) (2023)
- [From LLM to NMT: Advancing Low-Resource Machine Translation with Claude](https://mlsanity.com/paper/2404.13813/) (2024)
- [Prompting Techniques for Reducing Social Bias in LLMs through System 1 and System 2 Cognitive Processes](https://mlsanity.com/paper/2404.17218/) (2024)

## Built on by (most cited first, 30 of 41)

- [The Prompt Makes the Person(a): A Systematic Evaluation of Sociodemographic Persona Prompting for Large Language Models](https://mlsanity.com/paper/2507.16076/) (2025)
- [A Survey on Large Language Model-Based Social Agents in Game-Theoretic Scenarios](https://mlsanity.com/paper/2412.03920/) (2024)
- [PDE-Controller: LLMs for Autoformalization and Reasoning of PDEs](https://mlsanity.com/paper/2502.00963/) (2025)
- [Constrained Discrete Diffusion](https://mlsanity.com/paper/2503.09790/) (2025)
- [OEP: Poisoning Self-Evolving LLM Agents via Locally Correct but Non-Transferable Experiences](https://mlsanity.com/paper/2605.18930/) (2026)
- [Do Large Language Models Know What They Are Capable Of?](https://mlsanity.com/paper/2512.24661/) (2025)
- [Reasoning Under Uncertainty: Exploring Probabilistic Reasoning Capabilities of LLMs](https://mlsanity.com/paper/2509.10739/) (2025)
- [Are LLM Decisions Faithful to Verbal Confidence?](https://mlsanity.com/paper/2601.07767/) (2026)
- [Answer, Refuse, or Guess? Investigating Risk-Aware Decision Making in Language Models](https://mlsanity.com/paper/2503.01332/) (2025)
- [AIM-Bench: Evaluating Decision-making Biases of Agentic LLM as Inventory Manager](https://mlsanity.com/paper/2508.11416/) (2025)
- [Large Language Newsvendor: Decision Biases and Cognitive Mechanisms](https://mlsanity.com/paper/2512.12552/) (2025)
- [Can Revealed Preferences Clarify LLM Alignment and Steering?](https://mlsanity.com/paper/2605.08556/) (2026)
- [Teaching AI to Handle Exceptions: Supervised Fine-Tuning with Human-Aligned Judgment](https://mlsanity.com/paper/2503.02976/) (2025)
- [Comparing Exploration-Exploitation Strategies of LLMs and Humans: Insights from Standard Multi-armed Bandit Experiments](https://mlsanity.com/paper/2505.09901/) (2025)
- [Rethinking Prospect Theory for LLMs: Revealing the Instability of Decision-Making under Epistemic Uncertainty](https://mlsanity.com/paper/2508.08992/) (2025)
- [Noise, Adaptation, and Strategy: Assessing LLM Fidelity in Decision-Making](https://mlsanity.com/paper/2508.15926/) (2025)
- [An Experimental Study of Competitive Market Behavior Through LLMs](https://mlsanity.com/paper/2409.08357/) (2024)
- [Would a Large Language Model Pay Extra for a View? Inferring Willingness to Pay from Subjective Choices](https://mlsanity.com/paper/2602.09802/) (2026)
- [Discovering Differences in Strategic Behavior Between Humans and LLMs](https://mlsanity.com/paper/2602.10324/) (2026)
- [Causality Is Key to Understand and Balance Multiple Goals in Trustworthy ML and Foundation Models](https://mlsanity.com/paper/2502.21123/) (2025)
- [Who Gets the Kidney? Human-AI Alignment, Indecision, and Moral Values](https://mlsanity.com/paper/2506.00079/) (2025)
- [Adaptive Preference Optimization with Uncertainty-aware Utility Anchor](https://mlsanity.com/paper/2509.10515/) (2025)
- [Can Large Language Models Develop Gambling Addiction?](https://mlsanity.com/paper/2509.22818/) (2025)
- [Apparent Psychological Profiles of Large Language Models are Largely a Measurement Artifact](https://mlsanity.com/paper/2606.20205/) (2026)
- [Cash or Comfort? How LLMs Value Your Inconvenience](https://mlsanity.com/paper/2506.17367/) (2025)
- [GG-BBQ: German Gender Bias Benchmark for Question Answering](https://mlsanity.com/paper/2507.16410/) (2025)
- [Getting out of the Big-Muddy: Escalation of Commitment in LLMs](https://mlsanity.com/paper/2508.01545/) (2025)
- [Risk Profiling and Modulation for LLMs](https://mlsanity.com/paper/2509.23058/) (2025)
- [Identity-Robust Language Model Generation via Content Integrity Preservation](https://mlsanity.com/paper/2601.09141/) (2026)
- [Funny or Persuasive, but Not Both: Evaluating Fine-Grained Multi-Concept Control in LLMs](https://mlsanity.com/paper/2601.18483/) (2026)

## Abstract (arXiv)

When making decisions under uncertainty, individuals often deviate from rational behavior, which can be evaluated across three dimensions: risk preference, probability weighting, and loss aversion. Given the widespread use of large language models (LLMs) in decision-making processes, it is crucial to assess whether their behavior aligns with human norms and ethical expectations or exhibits potential biases. Several empirical studies have investigated the rationality and social behavior performance of LLMs, yet their internal decision-making tendencies and capabilities remain inadequately understood. This paper proposes a framework, grounded in behavioral economics, to evaluate the decision-making behaviors of LLMs. Through a multiple-choice-list experiment, we estimate the degree of risk preference, probability weighting, and loss aversion in a context-free setting for three commercial LLMs: ChatGPT-4.0-Turbo, Claude-3-Opus, and Gemini-1.0-pro. Our results reveal that LLMs generally exhibit patterns similar to humans, such as risk aversion and loss aversion, with a tendency to overweight small probabilities. However, there are significant variations in the degree to which these behaviors are expressed across different LLMs. We also explore their behavior when embedded with socio-demographic features, uncovering significant disparities. For instance, when modeled with attributes of sexual minority groups or physical disabilities, Claude-3-Opus displays increased risk aversion, leading to more conservative choices. These findings underscore the need for careful consideration of the ethical implications and potential biases in deploying LLMs in decision-making scenarios. Therefore, this study advocates for developing standards and guidelines to ensure that LLMs operate within ethical boundaries while enhancing their utility in complex decision-making environments.