The forms of risk.
Vektor puts a self-assured risk number back under scrutiny: how well calibrated it is, what happens in the tails, and who pays if it is wrong.
From forecasting the future to mapping vulnerability
Vektor generates thousands of plausible scenarios and shows where a system risks breaking. Alongside historical data it uses world models and counterfactual simulations, so decisions can be tested in situations never seen before.
For every decision it also models how other actors react: competitors, markets, environmental dynamics. If reality leaves known bounds, the platform says so instead of offering a single reassuring number.
Vektor OS keeps a dual transparency ledger. The Frequency Ledger measures how well estimates land under ordinary conditions; the Payout Ledger tracks real-world impact, and harm, in the risk tails.
Methods in composition
A real system will not sit still for a single formalism. Vektor OS combines several within the same reasoning.
Uncertainty & explicit bounds
With a Bayesian approach, priors and updates stay readable. The model signals when it enters uncharted territory.
Monte Carlo & tail stress
The Monte Carlo method samples many scenarios to see where extremes and regime shifts concentrate.
Causality & counterfactual intervention
Asks how the system’s response changes when decision levers move, separating correlation from intervention.
Damage curve & fragility map
Measures whether harm grows linearly or disproportionately with shock, whatever probability is assigned to that shock.
Reactive actors & network dynamics
Models interactions and feedback: how competitors, markets, or counterparties react to your choices over time.
World models & regime shifts
Learns the system’s dynamics and updates the map when reality contradicts starting assumptions.
Dual-ledger transparency
One ledger measures accuracy on ordinary noise (Frequency); the other tracks gain–loss asymmetry in extreme events (Payout).
Vektor OS
Under the product sits the platform: different methods calibrate against data, contest their hypotheses, and measure robustness against observed reality.