The simulation engines are the most speculative — and the most powerful — in the the behavioral AI platform registry. They do not score observed behavior; they project what behavior would look like under conditions that have not yet occurred.

The Engines

  • Digital Twin Engine — Creates a parameterised behavioral model of a specific actor, built from their historical data. The twin can be run through simulations independently of the actor.
  • BSPL Runtime — Executes Behavioral Scripting Language programs: conditional behavioral sequences that model complex multi-party interactions.
  • Reality Simulation Engine — Stress-tests decisions by running them through the digital twin under adverse conditions: what happens to this deal if the counterparty receives a competing offer?
  • Synthetic Data Generation — Generates synthetic behavioral datasets that preserve the statistical properties of real data without containing real personal information.

Code Walkthrough

// Digital twin: create from actor history
function createDigitalTwin(actorId, historicalData) {
  const params = fitBehavioralModel(historicalData);
  return {
    actorId,
    modelVersion: "1.0",
    parameters:   params,
    simulate(scenario) {
      return runSimulation(params, scenario);
    },
    confidence:   params.fitScore, // How well the model fits the historical data
  };
}

// Run a scenario through the twin
const twin = createDigitalTwin("actor-123", actorHistory);
const result = twin.simulate({ scenario: "competitor_offer_received", offerStrength: 0.8 });
// result: { predictedBehavior: "re-engage", confidence: 0.71 }

What to Watch For

  • Digital twins require explicit consent in most jurisdictions. See S2-ADV4.
  • Synthetic data generation that preserves behavioral fingerprints may still re-identify individuals. Run membership inference attacks before releasing synthetic datasets.