Most behavioral scoring treats each interaction as independent. Temporal engines treat them as a sequence — a trajectory through behavioral state space with momentum, phase transitions, and predictable future states.

The Engines

  • State Machine Runtime — Maintains a behavioral state for each actor (e.g., exploration → commitment → negotiation → close) and detects phase transitions.
  • Causal Graph Engine — Builds a causal model of behavioral patterns: what actions cause what responses. Distinguishes correlation from causation using do-calculus.
  • Counterfactual Reasoning Engine — Answers "what would this actor have done if the context had been different?" Used for stress-testing decisions.
  • Long-Horizon Prediction Engine — Projects behavioral trajectories 30, 60, and 90 days forward. Confidence degrades sharply beyond 30 days; the uncertainty band expands accordingly.

Code Walkthrough

// Behavioral state machine: track phase transitions
class BehavioralStateMachine {
  constructor(actorId, initialState) {
    this.actorId = actorId;
    this.state   = initialState;
    this.history = [{ state: initialState, ts: Date.now() }];
  }

  transition(newState, evidence) {
    if (!VALID_TRANSITIONS[this.state]?.includes(newState)) {
      return false; // Invalid transition — log as anomalous
    }
    this.state = newState;
    this.history.push({ state: newState, ts: Date.now(), evidence });
    eventBus.emit("state.transition.detected", {
      actorId:  this.actorId,
      from:     this.history.at(-2).state,
      to:       newState,
      evidence,
    });
    return true;
  }
}

What to Watch For

  • Long-horizon predictions should always display their confidence decay curve, not just the point estimate.
  • Causal graphs built on observational data contain confounders. Use instrumental variables or randomisation where possible before trusting causal claims.