{
  "example_kind": "original synthetic manual planning case",
  "slug": "forecast-asymmetric-error-cost-rule",
  "title": "Inspect a forecast decision with asymmetric mistake costs",
  "scenario": "Synthetic planning case: A toy decision can commit or wait. Its supplied readiness probability is 0.6. A false commitment costs four declared loss points; waiting when ready costs one. Correct decisions cost zero.",
  "layout": "evaluation",
  "dataset": {
    "heading": "Inspect the invented case records",
    "headers": [
      "Action",
      "Mistake probability under supplied p",
      "Loss if mistaken",
      "Expected loss"
    ],
    "rows": [
      [
        "Commit",
        "1−0.6=0.4",
        "4 points",
        "1.6"
      ],
      [
        "Wait",
        "0.6",
        "1 point",
        "0.6"
      ]
    ]
  },
  "derivation": "Commit loss=4(1−p); wait loss=p. They tie when 4−4p=p, so p=0.8. This threshold follows the toy losses and supplied probability, not a universal readiness policy.",
  "result": "Under these hypothetical inputs, committing has expected loss 1.6 points and waiting 0.6, so the declared loss rule favors waiting. A majority probability alone does not choose the action.",
  "boundary": "This links a binary decision threshold to asymmetric declared mistake costs, distinct from accuracy or probability-error scoring.",
  "limitations": "Loss points are local toy preferences, not money, measured harm or a legal/safety assurance. No live task probability is validated.",
  "sources": [
    "forecast"
  ],
  "tasks": [
    [
      "Define actual action consequences",
      "Decision owner",
      "The four-to-one loss convention and zero correct-decision loss are declared."
    ],
    [
      "Inspect the conditional comparison",
      "Reviewer",
      "At supplied p0.6,1.6 exceeds 0.6 expected loss points."
    ],
    [
      "Check evidence before real use",
      "Planning lead",
      "Actual probabilities, authority and consequences need independent justification before any commitment."
    ]
  ],
  "faqs": [
    [
      "Is 0.8 a recommended business threshold?",
      "No. It is the algebraic result of this invented four-to-one loss rule."
    ],
    [
      "Does the worksheet validate the supplied 0.6 probability?",
      "No. It is a hypothetical input; a real decision needs justified probability and consequence evidence."
    ]
  ],
  "method_references": [
    {
      "id": "forecast",
      "title": "Forecasting: Principles and Practice — accuracy",
      "url": "https://otexts.com/fpp3/accuracy.html",
      "scope": "Genuine held-out forecast and point-error evaluation context. Binary scoring examples use their own explicit toy definitions; no real task model is validated."
    }
  ]
}