CoBRA

Toward Precise and Consistent Agent Behaviors across Models Anchored by Validated Social Science Knowledge

🌐 Project Page: cobra.clawder.ai  |  📄 Paper: arXiv 2509.13588

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💡 What is Cognitive Bias?

Systematic deviations from rational judgment in human cognition and decision-making. For example, Framing Effect: "90% survival rate" vs. "10% mortality rate" — logically identical, yet people make different choices based on how information is framed.

Reproducibility and controllability are fundamental to scientific research. Yet implicit natural language descriptions — the dominant approach for specifying social agent behaviors in nearly all LLM-based social simulations — often fail to yield consistent behavior across models or capture the nuances of the descriptions.

CoBRA (Cognitive Bias Regulator for Social Agents) is a novel toolkit that lets researchers explicitly specify desired nuances in LLM-based agents and obtain consistent behavior across models.

Through CoBRA, we show how to operationalize validated social science knowledge as reusable "gym" environments for AI — an approach that generalizes to richer social and affective simulations.

The problem and our solution: from inconsistent agent behaviors under implicit specifications to explicit, quantitative control.

At the heart of CoBRA is a novel closed-loop system with two core components:

• Cognitive Bias Index — measures the cognitive bias of a social agent by quantifying its reactions in validated classic social science experiments
• Behavioral Regulation Engine — aligns the agent's behavior to exhibit controlled cognitive bias, via three control methods:
  • Prompt Engineering (input space control)
  • Representation Engineering (activation space control)
  • Fine-tuning (parameter space control)

Example: A researcher specifies a target bias level → CoBRA measures it via classic experiments → iteratively adjusts the agent until it reliably exhibits the desired bias.

Quick Start (3 Steps)

# 1. Install dependencies
pip install -r requirements.txt

# 2. Navigate to the unified bias control module
cd examples/unified_bias

# 3. Run a bias experiment
python pipelines.py --bias authority --method repe-linear --model Mistral-7B

That's it. The system will measure and control the agent's Authority Effect bias.

Repository Structure

CoBRA/
├── control/                    # Core bias control engine
├── examples/
│   ├── unified_bias/           # Main entry point (START HERE)
│   │   ├── pipelines.py        # Unified experiment runner
│   │   ├── run_pipelines.py    # CLI interface
│   │   ├── ablation/           # Ablation studies
│   │   └── README.md           # Full usage guide
│   ├── authority/              # Authority Effect utils
│   ├── bandwagon/              # Bandwagon Effect utils
│   ├── confirmation/           # Confirmation Bias utils
│   └── framing/                # Framing Effect utils
├── generator/                  # Data generation utilities
├── data_generated/             # Generated experimental data
├── webdemo/                    # Web demonstration interface
└── requirements.txt            # Python dependencies

Key Components

Supported Biases & Experiments

Citation

If you use CoBRA in your research, please cite our paper:

@article{liu2025cobra,
  title={CoBRA: Programming Cognitive Bias in Social Agents Using Classic Social Science Experiments},
  author={Liu, Xuan and Shang, Haoyang and Jin, Haojian},
  journal={arXiv preprint arXiv:2509.13588},
  year={2025}
}

Paper Link: https://arxiv.org/abs/2509.13588

License

MIT License - see LICENSE for details

Contact

For questions, please contact the corresponding author Xuan Liu at xul049@ucsd.edu, or file a GitHub Issue to report bugs and request features.

Need help? Check examples/unified_bias/README.md for detailed walkthroughs. The finetuning code is in the finetuning branch.