The Declarative Shift
Doxa was developed as a Python-native simulation environment, designed from the ground up for modern researchers. At its core lies a simple philosophy: separating the “what” from the “how.”
The complexity of an agent's world—initial resources, market mechanics, price shocks, or resource shortages—is defined declaratively via YAML. This ensures simulations are not just complex, but readable and manageable.
AI-Native Brains
Integrate Gemini, OpenAI, or local Ollama instances. Assign goals and personalities to agents to generate non-deterministic strategic decisions.
Scientific Verifiability
Built with FastAPI and Pydantic. Execution is strictly determined by the scenario file and the engine version, ensuring reproducibility.
Case Study
The Hormuz Crisis Model
In our experimental models, we witnessed AI agents utilizing Propaganda as a Resource to mitigate political decay. Hard resource constraints eventually forced a ceasefire where traditional diplomatic prompts had failed.
Explore the Research Colab →Scenario Definition
actors: - id: player_farmer provider: google model_name: gemini-1.5-pro persona: | You are a farmer and a trader. Your goal is to maximize gold reserves while maintaining enough corn to survive. initial_portfolio: credits: 45 corn: 12 gold: 5
Doxa is currently in active development. We view this not as a finished product, but as the inception of a community-driven effort for complex systems research.