Classify · Assess & Decide · Analysis & Finance

Classify whether to publish prices using the market 2x2

I give market size and homogeneity (with thresholds) and get the quadrant plus the publish-on-website recommendation, matching the book's transparency 2x2.

You receive: A pure function transparencyDecision(marketSize, homogeneityScore, sizeThreshold, homogeneityThreshold) -> {quadrant, recommendation} graded on hidden input->output cases.

Part of Choose Business Model

What's verified: STUD verifies the quadrant and the publish recommendation are computed deterministically from the size and homogeneity inputs against hidden cases. STUD does NOT decide what the right thresholds are, judge how homogeneous a market really is, or guarantee that publishing (or not) is the better commercial call -- the book itself calls the non-publish quadrants 'subjective'.

Opens soon

Cost20 credits

AcceptanceAutomated check against your inputs

ProtectionHeld until verified delivery

This objective is verified and ready. It opens soon, once sign-in and payments are live.

Deliverable interface

The exact vocabulary the automated check enforces: keys, tokens, entry points, and worked examples. Generated from the verification source.

{
 "constants": {},
 "entryName": "transparencyDecision",
 "expectNote": "expect is the comparison-space value (your return value goes through normalize first when one is published)",
 "hiddenCaseCount": 13,
 "hiddenCaseNames": [
  "boundary_inclusive",
  "gen0",
  "gen1",
  "gen2",
  "gen3",
  "gen4",
  "gen5",
  "gen6",
  "gen7",
  "gen8",
  "gen9",
  "gen10",
  "gen11"
 ],
 "inputKeys": [
  "homogeneityScore",
  "homogeneityThreshold",
  "marketSize",
  "sizeThreshold"
 ],
 "normalizeSource": "def _normalize_transparency_decision_2x2(r): return r if not isinstance(r, dict) else {\"isLarge\": bool(r.get(\"isLarge\")), \"isHomogeneous\": bool(r.get(\"isHomogeneous\")), \"quadrant\": r.get(\"quadrant\"), \"recommendation\": r.get(\"recommendation\")}\n",
 "returnShapes": [
  [
   "isHomogeneous",
   "isLarge",
   "quadrant",
   "recommendation"
  ]
 ],
 "signature": "def transparencyDecision(inp):",
 "submission": "python exposing the entry function; inp is one input object; graded on held-out cases",
 "tier": "calculator",
 "visibleCases": [
  {
   "expect": {
    "isHomogeneous": true,
    "isLarge": true,
    "quadrant": "large-homogeneous",
    "recommendation": "publish"
   },
   "input": {
    "homogeneityScore": 0.9,
    "homogeneityThreshold": 0.7,
    "marketSize": 50000,
    "sizeThreshold": 10000
   },
   "name": "large_hom_publish"
  },
  {
   "expect": {
    "isHomogeneous": true,
    "isLarge": false,
    "quadrant": "limited-homogeneous",
    "recommendation": "do-not-publish-subjective"
   },
   "input": {
    "homogeneityScore": 0.9,
    "homogeneityThreshold": 0.7,
    "marketSize": 100,
    "sizeThreshold": 10000
   },
   "name": "limited_hom"
  }
 ],
 "vocabulary": [
  "-",
  "do-not-publish-subjective",
  "heterogeneous",
  "homogeneityScore",
  "homogeneityThreshold",
  "homogeneous",
  "isHomogeneous",
  "isLarge",
  "large",
  "limited",
  "marketSize",
  "publish",
  "quadrant",
  "recommendation",
  "sizeThreshold"
 ]
}

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