CupidLeaks
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Methodology & reading guide

This page is the long-form reading guide for the simulator. It explains — in calm, descriptive language — what the simulation does, why it does it, and where its limits are. If something is unclear in the lab, the answer is most likely here.

1. What the simulation is

A model. More precisely: an agent-based model with around 2 000 synthetic profiles meeting on a virtual dating marketplace. You take the architect perspective — you are not on the app yourself, you set the rules. Sliders on the left control selectivity, gender ratio, and population size. One click swaps the recommendation algorithm. You can toggle dark patterns taken from real platform practice. The right pane shows in real time what your changes do.

Everything runs in your browser. No server, no telemetry of your slider moves, no storage beyond theme and language preference. Closing the tab wipes the simulation. Shared seeds in URLs reproduce the same run on another visit.

2. Why it exists

Dating platforms are socio-technical experiments running on millions of people. Their internal rules are not public. What we know comes from aggregated industry reports, whistleblower data, court records, and a few research papers. A simulation can turn this outside view into a traceable mechanism model. You are not seeing a perfect copy of Tinder or Hinge — you are seeing which effects certain architectures structurally enforce.

Goal: after an hour with this lab you no longer think "algorithms are only for mathematicians", but rather "I understand why Hinge's SMR looks the way it does, and which business decision sits behind it."

3. The population

2 000 agents by default (slider 500–10 000). Each agent carries a vector:

  • Gender — binary 0/1. This is a simplification; the underlying studies are collected binary. Real user populations are more diverse.
  • Attractiveness — latent, normally distributed around 0.5. Not a real value, but an abstract construct for "how often will this profile be marked positive when seen".
  • Selectivity — how often the agent right-swipes others. Male agents start at 46 %, female at 5.5 % (with noise).
  • Interests — a 32-bit bitmask, 4–8 random bits set. Used for Jaccard similarity in FAIR-MATCH.
  • Trait — abstract feature that in reality often represents ethnicity. Only takes effect when the "trait bias" dark-pattern toggle is on.
  • Counters — likes sent/received, matches, messages, days active, frustration.

Gender split 67 % male / 33 % female reflects aggregated platform data 2024/2025. This asymmetry is the precondition for every effect that follows.

4. The four engines — what they do, why they differ

Engine A — ELO

Idea: borrowed from chess. Every agent has a hidden score (start 1500). After a match, both scores shift by an amount that depends on the pre-match difference: being liked by someone with a much higher score earns you more points; being liked by someone much lower earns you almost nothing.

Effect: hierarchies form fast. An elite circulates among itself, the majority loses visibility. Tinder used a system like this in its early phase and has officially moved away — residues still shape behavior.

Engine B — Gale-Shapley

Idea: Nobel-prize-winning algorithm for stable matchings (the classic "Stable Marriage Problem"). Each iteration: all available men propose, all women decide — and no matching is stable if two people would prefer each other over their assigned partner.

Quirk in this sim: agents engage in "aspirational pursuit" — they reject proposals whose attractiveness is well below their own aspiration tier. Result: many matches fail because both sides reject profiles outside their league, even though the algorithm identified the pairing as "stable".

Hinge uses this logic for its "Most Compatible" feature.

Engine C — Collaborative filtering

Idea: "people who click like you have similar taste" — taken from e-commerce ("customers who bought this also bought…").

Problem: amplifies majority preferences exponentially. Profiles that already have many likes get shown to even more people; profiles with few likes disappear. This is the documented "popularity bias". Marginalized or non-mainstream profiles get systematically under-recommended.

Engine D — FAIR-MATCH

Idea: reciprocal recommender. Instead of one-sided "does X fit Y?", it takes the harmonic mean of two directed probabilities — does X fit Y and does Y fit X. The harmonic mean has a key property: if either value is small, the overall result drops sharply. Asymmetric interest is automatically devalued.

Effect: popularity bias is reduced. Gini drops. But: fewer total matches, because the engine ranks more conservatively.

5. Reading the KPIs

SMR — Swipe-to-Match-Ratio

SMR = matches ÷ likes sent. The hard indicator of "market value" on the app. Empirical Tinder/Hinge:

  • Men: average ~5.3 %, median 2 %. Top 10 % > 12.5 %.
  • Women: average ~44 %, median 41 %. Bottom 10 % around 15.8 %.

If your sim shows male SMR of 30 %, you've either shoved female selectivity to atypical highs, or you're running a tiny population on lucky rolls. Realistic are the defaults — which is why they are defaults.

MRR — Message Response Rate

Share of your opening messages that get a reply. Measures whether your conversation even starts. We split MRR "you initiated" vs. "they initiated" — the asymmetry is huge (men initiate 79 % of conversations with 12-char openers; women initiate 21 % with 120+ chars).

MDC — Match-to-Date Conversion

Share of matches that turn into a simulated real-world date. Empirically 3–10 %. Lower than that: you likely have dark patterns active or a very uneven distribution.

Gini coefficient

Borrowed from economics. Describes how unequal a distribution is. 0 = everyone gets the same, 1 = one person has it all. Hinge sits empirically around ~0.58 — that is more inequality than most national economies show. Our default sim reproduces this value approximately.

Top 10 % share

Share of all female likes that goes to the top 10 % of male profiles. Empirically ~58 %. If this climbs to 80 %, Engine C (collaborative) is probably active — or you've enabled trait bias.

Burnout index

Aggregate "frustration" across active agents. Frustration grows when likes go unmatched, when conversations break, when dark patterns degrade UX. Above 75 % the end state triggers: a modal noting simulated mass attrition. Real comparison: 79 % of Gen Z, 80 % of millennials report app burnout.

6. Dark patterns

Dark patterns are design choices that steer user behavior against user interest. They are explicitly named in this sim, off by default, and one click away. You should see what they do — not because they are cool, but because they exist.

Surveillance pricing

Premium prices vary based on captured behavior and demographic data. Longer active, fewer matches, or swiping at typical "desperation" hours — you pay more. Real example: 2018 class action against Tinder (California court), over-30s charged twice what under-30s paid for the same premium features. Settlement: USD 60.5 m.

Artificial match scarcity

Highly compatible pairs are withheld and revealed only after a premium purchase. The algorithm knows that X and Y would likely match — and only surfaces the suggestion when someone pays. Documented in industry reporting (Groundwork Collaborative 2024).

Trait bias

An abstract profile trait is algorithmically devalued. In collaborative- filtering engines the flagged group's visibility drops sharply. We model this as an abstract trait, not ethnicity — but real research (Cornell 2018, OkCupid data) shows exactly this pattern is measurable on ~65 % of studied platforms.

7. Formulas

Gini coefficient

G = 1 − 2 · (Σ cumulative values) / (n · Σ values) + 1/n

Jaccard coefficient

J(A, B) = popcount(A ∧ B) / popcount(A ∨ B)

Harmonic mean (FAIR-MATCH)

r(x, y) = 2 / (1/s(x,y) + 1/s(y,x))

with s(x, y) = 0.6 · J(interests) + 0.4 · J(network) + 0.001

ELO update

e(A) = 1 / (1 + 10^((r_B − r_A) / 400)), then r_A ← r_A + K · (1 − e(A)) on a match.

8. What it cannot do

  • It says nothing about specific individuals. The agents are synthetic.
  • It is not an empirical model of Tinder/Hinge/Bumble. Internal algorithms are not public.
  • It does not reproduce rare events (romance scams, harassment, violence risks) — these belong in the Safety & Trust chapter.
  • It is not therapy, counseling, or self-help.

9. Reproducibility

Every run is reproducible via URL query. Example: ?seed=42&pop=2000&ms=0.67&ml=0.46&fl=0.055&engine=fair-match&dp=sp,as. Same URL → bit-identical trajectory. Share it to discuss concrete findings.

10. Sources

Defaults and benchmark values come from the lexicon sources (see the bibliographies in articles 1–10) and the AlgoMatch source document. Where lexicon and simulator disagree, the lexicon wins on substantive claims and the simulator wins on its own technical reality (e.g. default parameters).