Reach decisive team consensus
without meeting gridlock

You define alternatives (the options being compared) and factors (the criteria that matter).

Each factor carries a priority (0–10, 0 = “doesn’t apply to me”) and, for every alternative, a performance range (1–10, min–max, optionally with a triangular peak) that represents uncertainty.

A priority is a relative weight. In every trial the engine combines factors into an outcome, and the weight scales how much a factor steers that outcome. Only the ratios between priorities matter: with {5, 7, 9}, the 9 outweighs the 7 by 9/7 ≈ 1.29× and the 5 by 1.8×. Rescaling all priorities together (e.g. to percentages that sum to 100) does not change the winner.

In team runs, every member’s importance ratings are first made relative to their own top factor: their #1 becomes the reference (1.0) and their other ratings become fractions of it. So a member who tops out at 7 and one who tops out at 10 contribute equal top-weight — nobody’s personal number scale silently dominates the average.

A 0 means “does not apply to me”: it is excluded for that member and never drags the group weight down. If more than half the members mark a factor 0, that factor is dropped from the run entirely (the strongest factor is always kept so the run stays meaningful).

The members’ normalized weights are averaged and mapped to a 0–10 priority for the engine. Dispersion is measured on the same normalized values, so disagreement reflects real differences in preference — not differences in how big a number each person happens to write.

Each option accumulates a weighted score per simulation trial. Then, within every trial, options are ordered by score, and the winner of each head-to-head pair gets a tally. After thousands of trials the pairs become a probability: “A beats B in 94% of runs.” The option that beats every other one over 50% is the Condorcet winner; if none (a score-based cycle), the option with the most pairwise wins wins. Uses performance intensity, uncertainty ranges, and the optional downside-risk penalty.

This reconstructs classic Condorcet voting. A priority distribution like {19%, 19%, 27%, 35%} is treated the way one treats voter blocs: 19% of the decision weight prefers one ordering, 35% prefers another. Criteria genuinely disagree (cost loves C, quality loves B), so real majority conflict — and genuine cycles — can emerge, and a majority of weight resolves them. This is the fairest “whose priorities win” reading, but it ignores performance intensity and near-ties.

Method A = richer decision model: uses magnitude, uncertainty, and risk — but is not majority voting, and its priorities are compressed.

Method B = faithful voting model: proportional to priority spread, surfaces true disagreement — but discards intensity and near-tie detail.

They answer different questions. Method A asks “what is the best weighted recommendation?”, Method B asks “which option wins a fair contest of the team’s preferences?”

Performance ranges are sampled thousands of times; each sample set is one trial. This turns confidence into probabilities and makes results reproducible with a fixed seed.

Method A samples a shared weighted score; Method B re-ranks each criterion’s bloc per trial, so wide ranges weaken conviction without inventing certainty.