Impact Engine — Allocate

Portfolio optimization under uncertainty for initiative selection

Knowing what works is not enough — you must decide where to invest under constraints and uncertainty. Decision theory frames this as a portfolio optimization problem: select the set of initiatives that maximizes returns across scenarios while respecting budget and strategic constraints.

Impact Engine — Allocate solves this with two pluggable decision rules. Minimax regret minimizes the maximum regret across all scenarios. A Bayesian solver maximizes expected return under user-specified scenario weights. Both consume confidence-penalized returns — better evidence enables better bets.

Visit our documentation for details.