This model compares rules for allocating a fixed budget of opportunity (places, training, funding) among people who differ in potential and will. Each agent’s effective potential, its denatotita, is potential multiplied by will. Realisation is denatotita multiplied by the opportunity received, and an agent succeeds when realisation reaches a threshold. Nine allocation regimes are compared: equal shares; shares proportional to denatotita; three threshold rules that reclaim and redistribute the allocations of those who would fail; an efficient benchmark that maximises successes; and three forward-looking rules (foresight, will-aware foresight and hindsight). A mixed regime blends targeting with equal shares. Over time, opportunity develops potential through a skill-formation technology, and will rises with success and falls when an agent is passed over. The model reproduces the article’s results: a closed-form crossover between equal and proportional allocation; a reversal in favour of broad allocation when development is productive; a hollowing-out of potential under success-maximising allocation; and the failure of mixtures of targeting and equal shares. Written in Python (NumPy, pandas, Matplotlib), with fixed random seeds so results are exactly reproducible.