# LLM economicus? Mapping the Behavioral Biases of LLMs via Utility Theory

arXiv: https://arxiv.org/abs/2408.02784 · HTML: https://mlsanity.com/paper/2408.02784/

## Key facts

- Cited by 47 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 28 references that are in the map.
- First posted on arXiv 5 Aug 2024.

## Builds on (most cited first, 7 of 7)

- [GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers](https://mlsanity.com/paper/2210.17323/) (2022)
- [Large Language Models](https://mlsanity.com/paper/2307.05782/) (2023)
- [Using Large Language Models to Simulate Multiple Humans and Replicate Human Subject Studies](https://mlsanity.com/paper/2208.10264/) (2022)
- [Reasoning or Reciting? Exploring the Capabilities and Limitations of Language Models Through Counterfactual Tasks](https://mlsanity.com/paper/2307.02477/) (2023)
- [Data Contamination: From Memorization to Exploitation](https://mlsanity.com/paper/2203.08242/) (2022)
- [Embers of Autoregression: Understanding Large Language Models Through the Problem They are Trained to Solve](https://mlsanity.com/paper/2309.13638/) (2023)
- [Political Compass or Spinning Arrow? Towards More Meaningful Evaluations for Values and Opinions in Large Language Models](https://mlsanity.com/paper/2402.16786/) (2024)

## Built on by (most cited first, 30 of 47)

- [LLM Social Simulations Are a Promising Research Method](https://mlsanity.com/paper/2504.02234/) (2025)
- [Anchoring Bias in Large Language Models: An Experimental Study](https://mlsanity.com/paper/2412.06593/) (2024)
- [LLM-based Human Simulations Have Not Yet Been Reliable](https://mlsanity.com/paper/2501.08579/) (2025)
- [A Survey on Large Language Model-Based Social Agents in Game-Theoretic Scenarios](https://mlsanity.com/paper/2412.03920/) (2024)
- [Your AI, Not Your View: The Bias of LLMs in Investment Analysis](https://mlsanity.com/paper/2507.20957/) (2025)
- [Game Theory Meets Large Language Models: A Systematic Survey with Taxonomy and New Frontiers](https://mlsanity.com/paper/2502.09053/) (2025)
- [A Large-Scale Simulation on Large Language Models for Decision-Making in Political Science](https://mlsanity.com/paper/2412.15291/) (2024)
- [EconEvals: Benchmarks and Litmus Tests for Economic Decision-Making by LLM Agents](https://mlsanity.com/paper/2503.18825/) (2025)
- [Silenced Biases: The Dark Side LLMs Learned to Refuse](https://mlsanity.com/paper/2511.03369/) (2025)
- [Distributive Fairness in Large Language Models: Evaluating Alignment with Human Values](https://mlsanity.com/paper/2502.00313/) (2025)
- [How Personality Traits Shape LLM Risk-Taking Behaviour](https://mlsanity.com/paper/2503.04735/) (2025)
- [The Effect of State Representation on LLM Agent Behavior in Dynamic Routing Games](https://mlsanity.com/paper/2506.15624/) (2025)
- [Evaluating and Aligning Human Economic Risk Preferences in LLMs](https://mlsanity.com/paper/2503.06646/) (2025)
- [Are Large Language Models Sensitive to the Motives Behind Communication?](https://mlsanity.com/paper/2510.19687/) (2025)
- [Are LLM Decisions Faithful to Verbal Confidence?](https://mlsanity.com/paper/2601.07767/) (2026)
- [Strategic Algorithmic Monoculture: Experimental Evidence from Coordination Games](https://mlsanity.com/paper/2604.09502/) (2026)
- [LLMs Model Non-WEIRD Populations: Experiments with Synthetic Cultural Agents](https://mlsanity.com/paper/2501.06834/) (2025)
- [Answer, Refuse, or Guess? Investigating Risk-Aware Decision Making in Language Models](https://mlsanity.com/paper/2503.01332/) (2025)
- [Chat Bankman-Fried: an Exploration of LLM Alignment in Finance](https://mlsanity.com/paper/2411.11853/) (2024)
- [Aligning Large Language Model Agents with Rational and Moral Preferences: A Supervised Fine-Tuning Approach](https://mlsanity.com/paper/2507.20796/) (2025)
- [Bridging Language Models and Financial Analysis](https://mlsanity.com/paper/2503.22693/) (2025)
- [Noise, Adaptation, and Strategy: Assessing LLM Fidelity in Decision-Making](https://mlsanity.com/paper/2508.15926/) (2025)
- [Addressing the alignment problem in transportation policy making: an LLM approach](https://mlsanity.com/paper/2510.13139/) (2025)
- [Behavioral Generative Agents for Energy Operations](https://mlsanity.com/paper/2506.12664/) (2025)
- [Pay What LLM Wants: Can LLM Simulate Economics Experiment with 522 Real-human Persona?](https://mlsanity.com/paper/2508.03262/) (2025)
- [Learning Robust Social Strategies with Large Language Models](https://mlsanity.com/paper/2511.19405/) (2025)
- [Cash or Comfort? How LLMs Value Your Inconvenience](https://mlsanity.com/paper/2506.17367/) (2025)
- [CHBench: A Cognitive Hierarchy Benchmark for Evaluating Strategic Reasoning Capability of LLMs](https://mlsanity.com/paper/2508.11944/) (2025)
- [FAIRGAMER: Evaluating Social Biases in LLM-Based Video Game NPCs](https://mlsanity.com/paper/2508.17825/) (2025)
- [Risk Profiling and Modulation for LLMs](https://mlsanity.com/paper/2509.23058/) (2025)

## Abstract (arXiv)

Humans are not homo economicus (i.e., rational economic beings). As humans, we exhibit systematic behavioral biases such as loss aversion, anchoring, framing, etc., which lead us to make suboptimal economic decisions. Insofar as such biases may be embedded in text data on which large language models (LLMs) are trained, to what extent are LLMs prone to the same behavioral biases? Understanding these biases in LLMs is crucial for deploying LLMs to support human decision-making. We propose utility theory-a paradigm at the core of modern economic theory-as an approach to evaluate the economic biases of LLMs. Utility theory enables the quantification and comparison of economic behavior against benchmarks such as perfect rationality or human behavior. To demonstrate our approach, we quantify and compare the economic behavior of a variety of open- and closed-source LLMs. We find that the economic behavior of current LLMs is neither entirely human-like nor entirely economicus-like. We also find that most current LLMs struggle to maintain consistent economic behavior across settings. Finally, we illustrate how our approach can measure the effect of interventions such as prompting on economic biases.