Applied Worlds publishes original, empirical work on how environments evolve, how to simulate them faithfully, and how agents learn and decide inside complex systems.
Financial markets, multi-agent environments, and physical systems under adaptive control share a common structure: they are non-stationary, partially observable, and endogenously shaped by the agents acting inside them. Building computational representations that are good enough to learn from, plan with, and decide inside is the problem all three research threads address.
Each artifact follows the same structure: a hypothesis, an experiment, an implementation, and a published record of results — including failures. Negative results are published alongside positive ones.
Applied Worlds is the Systems leg of a three-site research ecosystem. It connects to Applied Markets — how agents behave in financial environments — and Applied Models — how neural representations form internally. The three questions are complementary: Systems × Behavior × Intelligence.
Learning a latent representation of how a system evolves — then planning in that space without acting in the real environment. Covers learned dynamics models, physics-informed hybrid architectures that combine known structure with learned components, and model-based planning inside simulated environments.
When an agent acts, the environment changes — which changes the agent's next observation, which shapes the agent's next action. In markets this is reflexivity. In multi-agent RL this is non-stationarity — every agent's policy is part of every other agent's environment. In macroeconomics this is the Lucas critique. Experiments involve building simulated environments with multiple adaptive agents and studying what emerges from the feedback loop.
Simulation is only useful if you can intervene inside it, not just run it forward. Structural causal models provide the formal language for "what if?" — and when encoded as simulable environments, they allow measurement of whether counterfactual reasoning improves decisions compared to purely observational baselines.
Implementation, training run, evaluation, results. Reproducible where possible, honest about limitations always.
Longer-form writing where an experiment alone does not tell the full story. Always grounded in results.
Executable walkthroughs of methods and implementations. Not tutorials — structured explorations.
Negative results ship alongside positive ones. The record of what was tried is as valuable as what worked.
Applied Worlds is a personal initiative. Other projects from the same desk: Prachalabs.com — products and tools, including Rewire — and Pracha.me — personal site and broader writing.