# Personality-Driven Decision-Making in LLM-Based Autonomous Agents

arXiv: https://arxiv.org/abs/2504.00727 · HTML: https://mlsanity.com/paper/2504.00727/

## Key facts

- Cited by 13 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 11 of its 48 references that are in the map.
- First posted on arXiv 1 Apr 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, 11 of 11)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [Generative Agents: Interactive Simulacra of Human Behavior](https://mlsanity.com/paper/2304.03442/) (2023)
- [Continuous control with deep reinforcement learning](https://mlsanity.com/paper/1509.02971/) (2015)
- [A Survey on Large Language Model based Autonomous Agents](https://mlsanity.com/paper/2308.11432/) (2023)
- [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)
- [Social Simulacra: Creating Populated Prototypes for Social Computing Systems](https://mlsanity.com/paper/2208.04024/) (2022)
- [Language models show human-like content effects on reasoning tasks](https://mlsanity.com/paper/2207.07051/) (2022)
- [A Game-Theoretic Taxonomy and Survey of Defensive Deception for Cybersecurity and Privacy](https://mlsanity.com/paper/1712.05441/) (2017)

## Built on by (most cited first, 13 of 13)

- [AgentMisalignment: Measuring the Propensity for Misaligned Behaviour in LLM-Based Agents](https://mlsanity.com/paper/2506.04018/) (2025)
- [Enabling Personalized Long-term Interactions in LLM-based Agents through Persistent Memory and User Profiles](https://mlsanity.com/paper/2510.07925/) (2025)
- [FAIRGAME: a Framework for AI Agents Bias Recognition using Game Theory](https://mlsanity.com/paper/2504.14325/) (2025)
- [Toward Personalized LLM-Powered Agents: Foundations, Evaluation, and Future Directions](https://mlsanity.com/paper/2602.22680/) (2026)
- [PTCBENCH: Benchmarking Contextual Stability of Personality Traits in LLM Systems](https://mlsanity.com/paper/2602.00016/) (2026)
- [Temperature and Persona Shape LLM Agent Consensus With Minimal Accuracy Gains in Qualitative Coding](https://mlsanity.com/paper/2507.11198/) (2025)
- [The Judgment-Consequence Gap: LLM Moral Reasoning in Healthcare Decisions](https://mlsanity.com/paper/2608.05583/) (2026)
- [Who Gets the Kidney? Human-AI Alignment, Indecision, and Moral Values](https://mlsanity.com/paper/2506.00079/) (2025)
- [Effects of personality steering on cooperative behavior in Large Language Model agents](https://mlsanity.com/paper/2601.05302/) (2026)
- [The AI in the Mirror: LLM Self-Recognition in an Iterated Public Goods Game](https://mlsanity.com/paper/2508.18467/) (2025)
- [Structured Personality Control and Adaptation for LLM Agents](https://mlsanity.com/paper/2601.10025/) (2026)
- [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)
- [Beyond Partner Diversity: An Influence-Based Team Steering Framework for Zero-Shot Human-Machine Teaming](https://mlsanity.com/paper/2605.15400/) (2026)

## Abstract (arXiv)

The embedding of Large Language Models (LLMs) into autonomous agents is a rapidly developing field which enables dynamic, configurable behaviours without the need for extensive domain-specific training. In our previous work, we introduced SANDMAN, a Deceptive Agent architecture leveraging the Five-Factor OCEAN personality model, demonstrating that personality induction significantly influences agent task planning. Building on these findings, this study presents a novel method for measuring and evaluating how induced personality traits affect task selection processes - specifically planning, scheduling, and decision-making - in LLM-based agents. Our results reveal distinct task-selection patterns aligned with induced OCEAN attributes, underscoring the feasibility of designing highly plausible Deceptive Agents for proactive cyber defense strategies.