# LLM Agents Display Human Biases but Exhibit Distinct Learning Patterns

arXiv: https://arxiv.org/abs/2503.10248 · HTML: https://mlsanity.com/paper/2503.10248/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Placed in Social simulation and multi-agent debate, one of 16 directions in LLM agents and tool use.
- Builds on 19 of its 39 references that are in the map.
- First posted on arXiv 13 Mar 2025.

## Placed in

- LLM agents and tool use / [Social simulation and multi-agent debate](https://mlsanity.com/agents/social-simulation-and-multi-agent-debate/)

## Builds on (most cited first, 19 of 19)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Out of One, Many: Using Language Models to Simulate Human Samples](https://mlsanity.com/paper/2209.06899/) (2022)
- [Using Large Language Models to Simulate Multiple Humans and Replicate Human Subject Studies](https://mlsanity.com/paper/2208.10264/) (2022)
- [Using cognitive psychology to understand GPT-3](https://mlsanity.com/paper/2206.14576/) (2022)
- [Thinking Fast and Slow in Large Language Models](https://mlsanity.com/paper/2212.05206/) (2022)
- [Capturing Failures of Large Language Models via Human Cognitive Biases](https://mlsanity.com/paper/2202.12299/) (2022)
- [Turning large language models into cognitive models](https://mlsanity.com/paper/2306.03917/) (2023)
- [CogBench: a large language model walks into a psychology lab](https://mlsanity.com/paper/2402.18225/) (2024)
- [Is this the real life? Is this just fantasy? The Misleading Success of Simulating Social Interactions With LLMs](https://mlsanity.com/paper/2403.05020/) (2024)
- [Large Language Models Assume People are More Rational than We Really are](https://mlsanity.com/paper/2406.17055/) (2024)
- [Incoherent Probability Judgments in Large Language Models](https://mlsanity.com/paper/2401.16646/) (2024)
- [How do Large Language Models Navigate Conflicts between Honesty and Helpfulness?](https://mlsanity.com/paper/2402.07282/) (2024)
- [(Ir)rationality and Cognitive Biases in Large Language Models](https://mlsanity.com/paper/2402.09193/) (2024)
- [Improving Interpersonal Communication by Simulating Audiences with Language Models](https://mlsanity.com/paper/2311.00687/) (2023)
- [Can LLMs Replace Economic Choice Prediction Labs? The Case of Language-based Persuasion Games](https://mlsanity.com/paper/2401.17435/) (2024)
- [LLM-driven Imitation of Subrational Behavior : Illusion or Reality?](https://mlsanity.com/paper/2402.08755/) (2024)
- [Relative Value Biases in Large Language Models](https://mlsanity.com/paper/2401.14530/) (2024)
- [Evaluating and Modeling Social Intelligence: A Comparative Study of Human and AI Capabilities](https://mlsanity.com/paper/2405.11841/) (2024)
- [A Nurse is Blue and Elephant is Rugby: Cross Domain Alignment in Large Language Models Reveal Human-like Patterns](https://mlsanity.com/paper/2405.14863/) (2024)

## Built on by (most cited first, 3 of 3)

- [How Many Instructions Can LLMs Follow at Once?](https://mlsanity.com/paper/2507.11538/) (2025)
- [Human vs. Agent in Task-Oriented Conversations](https://mlsanity.com/paper/2509.17619/) (2025)
- [Mimicry without understanding: the origins of decision bias in large language models](https://mlsanity.com/paper/2608.12339/) (2026)

## Abstract (arXiv)

We investigate the choice patterns of Large Language Models (LLMs) in the context of Decisions from Experience tasks that involve repeated choice and learning from feedback, and compare their behavior to human participants. We find that on the aggregate, LLMs appear to display behavioral biases similar to humans: both exhibit underweighting rare events and correlation effects. However, more nuanced analyses of the choice patterns reveal that this happens for very different reasons. LLMs exhibit strong recency biases, unlike humans, who appear to respond in more sophisticated ways. While these different processes may lead to similar behavior on average, choice patterns contingent on recent events differ vastly between the two groups. Specifically, phenomena such as ``surprise triggers change" and the ``wavy recency effect of rare events" are robustly observed in humans, but entirely absent in LLMs. Our findings provide insights into the limitations of using LLMs to simulate and predict humans in learning environments and highlight the need for refined analyses of their behavior when investigating whether they replicate human decision making tendencies.