# Designing AI-Agents with Personalities: A Psychometric Approach

arXiv: https://arxiv.org/abs/2410.19238 · HTML: https://mlsanity.com/paper/2410.19238/

## Key facts

- Cited by 14 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 9 of its 39 references that are in the map.
- First posted on arXiv 25 Oct 2024.

## 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, 9 of 9)

- [Generative Agents: Interactive Simulacra of Human Behavior](https://mlsanity.com/paper/2304.03442/) (2023)
- [Out of One, Many: Using Language Models to Simulate Human Samples](https://mlsanity.com/paper/2209.06899/) (2022)
- [Want To Reduce Labeling Cost? GPT-3 Can Help](https://mlsanity.com/paper/2108.13487/) (2021)
- [The political ideology of conversational AI: Converging evidence on ChatGPT's pro-environmental, left-libertarian orientation](https://mlsanity.com/paper/2301.01768/) (2023)
- [Thinking Fast and Slow in Large Language Models](https://mlsanity.com/paper/2212.05206/) (2022)
- [Democratizing Large Language Models via Personalized Parameter-Efficient Fine-tuning](https://mlsanity.com/paper/2402.04401/) (2024)
- [Measuring Political Bias in Large Language Models: What Is Said and How It Is Said](https://mlsanity.com/paper/2403.18932/) (2024)
- [The illusion of artificial inclusion](https://mlsanity.com/paper/2401.08572/) (2024)
- [AI for social science and social science of AI: A Survey](https://mlsanity.com/paper/2401.11839/) (2024)

## Built on by (most cited first, 14 of 14)

- [Beyond Static Responses: Multi-Agent LLM Systems as a New Paradigm for Social Science Research](https://mlsanity.com/paper/2506.01839/) (2025)
- [FACET: Teacher-Centred LLM-Based Multi-Agent Systems-Towards Personalized Educational Worksheets](https://mlsanity.com/paper/2508.11401/) (2025)
- [Using Large Language Models to Categorize Strategic Situations and Decipher Motivations Behind Human Behaviors](https://mlsanity.com/paper/2503.15752/) (2025)
- [Evaluating LLM Alignment on Personality Inference from Real-World Interview Data](https://mlsanity.com/paper/2509.13244/) (2025)
- [Evaluating the Simulation of Human Personality-Driven Susceptibility to Misinformation with LLMs](https://mlsanity.com/paper/2506.23610/) (2025)
- [On the Dynamics of Multi-Agent LLM Communities Driven by Value Diversity](https://mlsanity.com/paper/2512.10665/) (2025)
- [Integrating Personality into Digital Humans: A Review of LLM-Driven Approaches for Virtual Reality](https://mlsanity.com/paper/2503.16457/) (2025)
- [Applying Psychometrics to Large Language Model Simulated Populations: Recreating the HEXACO Personality Inventory Experiment with Generative Agents](https://mlsanity.com/paper/2508.00742/) (2025)
- [Persona-based Multi-Agent Collaboration for Brainstorming](https://mlsanity.com/paper/2512.04488/) (2025)
- [From Who They Are to How They Act: Behavioral Traits in Generative Agent-Based Models of Social Media](https://mlsanity.com/paper/2601.15114/) (2026)
- [Nous: An Attempt to Extract and Inject the Cognition Behind Prediction-Market Behavior](https://mlsanity.com/paper/2606.13038/) (2026)
- [AI YOU Town: Make Friends and Money with Your Digital Twin](https://mlsanity.com/paper/2607.10539/) (2026)
- [Inverse Theory of Mind Modeling for Content Recommendation: From Web Browsing to Dynamic Intelligent Interfaces](https://mlsanity.com/paper/2608.11354/) (2026)
- [Modeling Human Behavior with Type Vectors Using AI](https://mlsanity.com/paper/2608.18265/) (2026)

## Abstract (arXiv)

We introduce a methodology for assigning quantifiable and psychometrically validated personalities to AI-Agents using the Big Five framework. Across three studies, we evaluate its feasibility and limitations. In Study 1, we show that large language models (LLMs) capture semantic similarities among Big Five measures, providing a basis for personality assignment. In Study 2, we create AI-Agents using prompts designed based on the Big Five Inventory-2 (BFI-2) in different format, and find that AI-Agents powered by new models align more closely with human responses on the Mini-Markers test, although the finer pattern of results (e.g., factor loading patterns) were sometimes inconsistent. In Study 3, we validate our AI-Agents on risk-taking and moral dilemma vignettes, finding that models prompted with the BFI-2-Expanded format most closely reproduce human personality-decision associations, while safety-aligned models generally inflate 'moral' ratings. Overall, our results show that AI-Agents align with humans in correlations between input Big Five traits and output responses and may serve as useful tools for preliminary research. Nevertheless, discrepancies in finer response patterns indicate that AI-Agents cannot (yet) fully substitute for human participants in precision or high-stakes projects.