ALEA CAPTAAI Deep Ontology & Pattern Calibration

Technic

Alea Capta does not rush to predict — we build the structure first. Ontology lays out the structure of reality, synthetic populations create the missing data, calibration tunes the weights, and the simulation engine rolls the future countless times to find the best path.

What makes us different

TECHNIC IN ACTION

We start by moving reality into a model, load a synthetic population on top, generate the future as distributions, search for the best path, and finally propose the strategy to execute. The method stays the same, but for each domain we redesign the ontology, the population and the strategy space to build a dedicated engine.

  1. 01 — KNOWLEDGE GRAPH · ONTOLOGY

    Move complex reality into a model, and write the rules ourselves.

    It starts with domain modeling and rule-making. With a graph database and an ontology we define entities, relations and rules, mirroring reality as a whole.

  2. 02 — DIGITAL TWIN · SYNTHETIC POPULATION

    Build a digital twin tailored to each domain.

    On top of the model we place a synthetic world (a digital twin) that mirrors the domain one-to-one. Each entity gets hundreds of attributes, and IPF synthesis, which preserves joint distributions, reproduces the structure of reality. Companies, markets, patients, assets — any domain can be moved into a synthetic population.

  3. 03 — MONTE CARLO · SAMPLING DISTRIBUTION

    See how a single choice spreads into outcomes.

    The Monte Carlo engine triggers a specific event at the present moment and rolls its impact tens of thousands to millions of times in the synthetic world. Instead of one answer, it returns the probability distribution of a single event’s outcome with confidence intervals, quantifying the uncertainty of that one move. It can even estimate outcomes of events for which there is no data yet.

  4. 04 — MCTS · GOLDEN PATH SEARCH

    Chain countless choices over time to find the best path.

    MCTS stitches the single-event distributions from Monte Carlo along the timeline and searches the tree of strategies. It calculates which move to make at each moment to reach the goal, finding the optimal route — the golden path. It even finds wildcards (comeback moves) that lie outside the distribution.

  5. 05 — WARGAME · WIN PROBABILITY

    Find the best scenario, and propose the way there.

    A wargame is not just a forecast. It finds the best path to the goal and works backwards to propose which policy or issue to put forward, and when, to get on that path. It proposes the intervention needed at each branch, then redesigns the next move based on what actually happened.

After execution, calibrate again

Every week we match forecasts against outcomes and re-adjust the weights. The model for the next round is one that has learned from this round’s results.

See also

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