Global match rate
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Matches ÷ pairs evaluated
Match → relationship
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% of matches with aligned goals
Men with ≥1 match
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Market participation
Women with ≥1 match
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Market participation
Match distribution by tier and sex
Each bar is an attractiveness decile. The system empirically reproduces the Pareto curve — adjust parameters to shift the equilibrium.
Men
Women
Perfect equality line
Reference: Rudder, C. (2014). Dataclysm. 25M OKCupid users. · Galloway, S. (2023). Adrift. · Tyson et al. (2016). A First Look at User Activity on Tinder.
Match matrix · tier × tier
% of women from tier (row) who matched with men from tier (column). Reveals who pairs with whom at equilibrium.
Prior: Hypergamy documented in revealed preferences — women prefer partners of status ≥ their own (Buss; Hitsch et al. 2010).
Zero-match rate by tier
% of people in each tier who ended the session with no match at all. The male market floor lives here.
Men with no match
Women with no match
Reference: Twenge et al. (2017) · GSS — male 18–24 sexual inactivity doubled (19% → 31%).
Lorenz curve · match concentration
X-axis: % of population (from least to most successful). Y-axis: cumulative % of matches received. The more convex the curve, the more unequal the distribution.
Men · Gini —
Women · Gini —
Perfect equality
Reference: Tyson et al. (2016) · The bottom 50% of male users receive less than 5% of likes on Tinder. Galloway (2023) — 80/20 Pareto curve.
Goal alignment across matches
Of the matches formed, in how many do both want the same thing. A serious × casual match is noise: it disappears from both sides later.
Reference: Lafortune & Low (2023) — Tinder ↑ sex, no ↑ in bond formation. Perel (2017) — paradox of abundance.
What the simulation shows
Synthesis of the results from the latest run.