Premise

Traditional project management treats work as binary — do it or don't. But real projects have different efficiency curves: some return 80% of their value at 10% effort, others need 60% effort to get to 80% value. The framework asks a more precise question: for each project in your portfolio, where is the effort-to-value ratio maximized, and when does additional work cost more than it returns?

How it evolved

Started as a personal time allocation problem. Grew into a parametric framework when it became clear that single-point heuristics (the Pareto Index alone) couldn't capture the shape differences between project types. Added activation energy as a dead zone before returns begin, separate ramp and decline rates for the rise and fall of efficiency, and peak value as a multiplier. Four named archetypes — Quick Win, Marathon, High Risk, Plateau — make the parameterization accessible without requiring the user to think in six dimensions. A greedy portfolio optimizer with diversity constraints converts the curves into concrete weekly time allocation recommendations.

Technical crux

The key metric is Early Stop Value: what percentage of total project value do you capture at 90% effort? For Quick Win archetypes this is 97%+; for Marathons it's much lower because the curve stays elevated late. This single number reframes the decision from 'is this done?' to 'should I stop now?' The optimizer penalizes late-stage allocation using the same instantaneous efficiency logic — it will redirect hours to a project in its activation zone before it will extend a project past its peak. All computation runs client-side; the entire tool is a single HTML file with Chart.js.

Findings

Six-parameter curve model (activation energy, Pareto index, ramp rate, decline rate, peak value, max hours) generating real-time instantaneous efficiency and cumulative value charts. Four preset archetypes for rapid parameterization. Portfolio optimizer that takes total available hours per week and outputs a prioritized allocation queue. Runs fully offline with no backend and no build step. Live at rivirside.github.io/P-index.

Open questions

The optimizer is a greedy heuristic — no global optimality guarantee. More importantly, the six parameters are subjective inputs, not measured quantities. Historical tracking (predicted efficiency curve vs. actual returns over time) would validate whether the model's shape predictions correspond to how projects actually unfold. The deeper question is whether project efficiency curves are stable properties or depend heavily on context — team size, external deadlines, resource availability.