Persona Evolve: Implicit Behavioral Alignment of Language Agents in High-Stakes Crowd Simulations

This repository accompanies our research paper titled Implicit Behavioral Alignment of Language Agents in High-Stakes Crowd Simulations, and contains the implementation of Persona–Environment Behavioral Alignment (PEBA) and its optimization algorithm, PersonaEvolve (PEvo) in a Unity3D-based Active Shooter Incident Simulation. The framework reduces the Behavior–Realism Gap by iteratively refining agent personas so their collective behaviors match expert expectations.

For demo videos and more information, please visit our Project Homepage.

Set Up Environment

git clone https://github.com/HATS-ICT/PEBA-ASI

Set up virtual environment and

pip install -r requirements.txt

Download Sample Simulation Data

WIP

Replay Simulation

WIP

Analyze Simulation

### Behavior Classification
python classify_behavior.py --folder "Simulation_Run_Folder_Name"

### Analysis Test
python analyze_optimization.py --runs "Optimization_Run_Folder_Name"

Authors and Citation

Authors: Yunzhe Wang, Gale M. Lucas, Burcin Becerik-Gerber, Volkan Ustun

If you used this codebase, please cite our paper:

@inproceedings{wang2025implicit,
  title={Implicit Behavioral Alignment of Language Agents in High-Stakes Crowd Simulations},
  author={Wang, Yunzhe and Lucas, Gale and Becerik-Gerber, Burcin and Ustun, Volkan},
  booktitle={Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing},
  pages={30669--30686},
  year={2025}
}