MUSE can model anything — cells, neurons, amoebae, flocks of birds. But we specialize in modeling people. When you model people with real internal states in real environments, you get data that matters.
Research is hitting a wall that more experiments won't solve.
Across biology, behavioral science, and biomedicine, the dominant methodology is unchanged from fifty years ago: pose a narrow hypothesis, design an experiment, collect data, run statistics, publish. Then repeat with a minor variation. Each cycle takes months to years. Each cycle produces a small increment of knowledge tied to a specific experimental paradigm. Replication crises, variable results, and slow progress aren't accidents — they're what happens when you try to map a vast hypothesis space one experiment at a time.
The problem isn't experimental rigor. The problem is that the hypothesis space is too large to test exhaustively, and the methodology has no efficient way to narrow it before running experiments. So researchers test what's familiar — the same EEG bands, the same behavioral paradigms, the same statistical contrasts — because exploring beyond the familiar is too expensive to attempt.
The fields making the fastest progress today have already changed how they work. Computational biology, climate science, and parts of physics now follow a different loop:
The result is faster discovery, broader exploration, and experiments that matter more because each one is sharper. The fields that have adopted this loop are outcompeting the fields that haven't, on rate of progress per dollar and per year.
MUSE is infrastructure for this methodology — not a faster version of the existing experimental pipeline, but the substrate for a different one.
Run parameter sweeps that would take months on traditional infrastructure in hours. Explore hypothesis spaces that are currently inaccessible.
Identify which mechanisms are biologically plausible, which combinations produce the patterns observed in real data, which interventions are worth testing in the lab.
Combine mechanistic models of biological systems with agent-based dynamics and neural surrogates in a single runtime. Most real questions span scales and domains that existing tools cannot bridge.
Run thousands of simulated experiments in the time it takes to run one real one. Identify which experimental designs are sensitive to the effects you care about before committing to human subjects research.
MUSE has been applied to or is actively being developed for:
Gothic Grandma is actively pursuing federal grant funding across three research domains.
Using emergent simulation to build reading engagement through agency. Children read more when they have meaningful control over a world that responds.
Simulation environments for social situation practice and neurodivergence training — worlds that accommodate different processing styles naturally.
Agent-based epidemiological modeling with real behavioral systems. Not network models — full entity simulation with internal immune, social, and behavioral states.
Research access is provided through a restricted GLYPH license. Researchers can configure starting parameters, define experimental conditions, and run FONT headless at scale. Results are exported as curated datasets.
The simulation architecture stays proprietary — researchers work with the engine, not its internals. This keeps the platform consistent across research groups and protects the investment that makes the platform possible.
As the platform matures, we are building toward a funded research fellowship program — direct awards to graduate students and early-career researchers doing work with MUSE in exchange for co-publication and citation. That program is aspirational for now, but it reflects where we intend to go.
Tools like NetLogo, Mesa, MASON, and FLAME GPU 2 are valuable and have long track records. MUSE offers different trade-offs.
Millions of entities with full internal states. No CUDA required.
Behaviors are modular, mix-and-match across entity types.
GLYPH provides a full R&D environment — configure, run, analyze, export.
Systems derived from neuroscience, behavioral science, and cognitive science literature.