Simulation engine

The simulation engine uses Monte Carlo to obtain the distribution of outcomes for an event, and MCTS to chain choices over time and find the best path to a goal.

Roll the future countless times to find the best path

Once the structure is complete and the weights are tuned, we can experiment with the future.

Monte Carlo — roll a single event countless times. When a specific event happens, we simulate how its impact spreads, tens of thousands to millions of times in the synthetic world. We get not a single answer but a distribution of outcomes. Instead of “this will happen”, we can say “within this range, with this probability”.

MCTS — chain choices over time to find the best path. We stitch together the single-event distributions from Monte Carlo along the timeline and search the tree of strategies. Like AlphaGo reading moves in Go, it calculates which move to make at which moment to reach the goal.

We don’t stop at predicting the future. We find the best path to the goal, and work backwards to propose what to do, and when, to get on that path. You live through the coming phase in advance, and meet it with the response already in hand.

Not predicting the future, but living it in advance. That is Alea Capta’s simulation.

See also