# Using Large Language Models to Categorize Strategic Situations and Decipher Motivations Behind Human Behaviors

arXiv: https://arxiv.org/abs/2503.15752 · HTML: https://mlsanity.com/paper/2503.15752/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 29 references that are in the map.
- First posted on arXiv 20 Mar 2025.

## Builds on (most cited first, 6 of 6)

- [Using Large Language Models to Simulate Multiple Humans and Replicate Human Subject Studies](https://mlsanity.com/paper/2208.10264/) (2022)
- [On the Diversity and Limits of Human Explanations](https://mlsanity.com/paper/2106.11988/) (2021)
- [Modeling Human Subjectivity in LLMs Using Explicit and Implicit Human Factors in Personas](https://mlsanity.com/paper/2406.14462/) (2024)
- [A Study on the Calibration of In-context Learning](https://mlsanity.com/paper/2312.04021/) (2023)
- [Designing AI-Agents with Personalities: A Psychometric Approach](https://mlsanity.com/paper/2410.19238/) (2024)
- [LLMs Model Non-WEIRD Populations: Experiments with Synthetic Cultural Agents](https://mlsanity.com/paper/2501.06834/) (2025)

## Built on by (most cited first, 5 of 5)

- [BehaviorBench: Benchmarking Foundation Models for Behavioral Science Tasks](https://mlsanity.com/paper/2606.24162/) (2026)
- [Do LLM-Driven Agents Exhibit Engagement Mechanisms? Controlled Tests of Information Load, Descriptive Norms, and Popularity Cues](https://mlsanity.com/paper/2603.20911/) (2026)
- [LEVANTE-bench: Multi-Scale Comparison of VLMs to Children Using Cognitive Tasks (or, "Is Your VLM Smarter Than a 5th Grader?")](https://mlsanity.com/paper/2606.05497/) (2026)
- [Measuring Behavior Portability in Large Language Models](https://mlsanity.com/paper/2606.22797/) (2026)
- [Modeling Human Behavior with Type Vectors Using AI](https://mlsanity.com/paper/2608.18265/) (2026)

## Abstract (arXiv)

By varying prompts to a large language model, we can elicit the full range of human behaviors in a variety of different scenarios in classic economic games. By analyzing which prompts elicit which behaviors, we can categorize and compare different strategic situations, which can also help provide insight into what different economic scenarios induce people to think about. We discuss how this provides a first step towards a non-standard method of inferring (deciphering) the motivations behind the human behaviors. We also show how this deciphering process can be used to categorize differences in the behavioral tendencies of different populations.