{
  "version": 1,
  "schemaVersion": "1.0",
  "updatedAt": "2026-08-22",
  "description": "Worked examples for the Cognitive Biases Decision Review contract. They show calibrated candidate lenses rather than diagnosing people or teams.",
  "examples": [
    {
      "id": "vendor-following-competitors",
      "input": "We should migrate to technology X because several competitors are using it.",
      "review": {
        "schemaVersion": "1.0",
        "decision": "Whether to migrate to technology X.",
        "situation": "vendor-tool-selection",
        "observedFacts": [
          "Several competitors are reported to use technology X."
        ],
        "inferredRisks": [
          {
            "lensId": "cognitive-bias-confirmation-bias",
            "rationale": "Competitor adoption may be used mainly as support for an option that is already preferred.",
            "confidence": "low"
          },
          {
            "lensId": "cognitive-bias-anchoring-effect",
            "rationale": "The competitor choice may become an early reference point before our own criteria are fixed.",
            "confidence": "low"
          }
        ],
        "missingEvidence": [
          "Our required capabilities and constraints.",
          "Evidence that competitor conditions are comparable to ours."
        ],
        "questions": [
          "Which criteria were fixed before looking at competitors?",
          "What evidence would make us reject technology X?"
        ],
        "techniques": [
          {
            "id": "predefined-criteria",
            "why": "Compare options against the same requirements."
          },
          {
            "id": "red-team-review",
            "why": "Build the strongest evidence-based case against the preferred migration."
          }
        ],
        "nextAction": "Define the criteria and run the same evidence check on technology X and one serious alternative.",
        "uncertainty": "Competitor adoption alone does not show whether technology X fits this organisation.",
        "canonicalReferences": [
          {
            "type": "situation",
            "id": "vendor-tool-selection",
            "url": "https://cognitive-biases.github.io/situations/vendor-tool-selection/"
          },
          {
            "type": "bias",
            "id": "cognitive-bias-confirmation-bias",
            "url": "https://cognitive-biases.github.io/biases/cognitive-bias-confirmation-bias/"
          },
          {
            "type": "technique",
            "id": "predefined-criteria",
            "url": "https://cognitive-biases.github.io/techniques/predefined-criteria/"
          }
        ]
      }
    },
    {
      "id": "late-project-continuation",
      "input": "The project is late, but we have already spent too much to stop now.",
      "review": {
        "schemaVersion": "1.0",
        "decision": "Whether to fund the next stage of the late project.",
        "situation": "product-prioritization",
        "observedFacts": [
          "The project is late.",
          "Resources have already been spent."
        ],
        "inferredRisks": [
          {
            "lensId": "logical-fallacy-escalation-of-commitment",
            "rationale": "Past investment is explicitly being used as a reason for future investment.",
            "confidence": "medium"
          }
        ],
        "missingEvidence": [
          "Future cost from today.",
          "Expected future value and probability of achieving it."
        ],
        "questions": [
          "Would we fund the next stage if the project arrived today with no history?",
          "Which criteria should the next stage meet?"
        ],
        "techniques": [
          {
            "id": "incremental-value-check",
            "why": "Separate unrecoverable past spending from future cost and value."
          },
          {
            "id": "predefined-criteria",
            "why": "Judge the next stage against criteria fixed before the continuation decision."
          }
        ],
        "nextAction": "Recalculate the decision using future costs, future value and explicit continuation criteria.",
        "uncertainty": "Past investment can contain information about future capability, but unrecoverable cost is not itself future value.",
        "canonicalReferences": [
          {
            "type": "situation",
            "id": "product-prioritization",
            "url": "https://cognitive-biases.github.io/situations/product-prioritization/"
          },
          {
            "type": "bias",
            "id": "logical-fallacy-escalation-of-commitment",
            "url": "https://cognitive-biases.github.io/biases/logical-fallacy-escalation-of-commitment/"
          },
          {
            "type": "technique",
            "id": "incremental-value-check",
            "url": "https://cognitive-biases.github.io/techniques/incremental-value-check/"
          }
        ]
      }
    },
    {
      "id": "ai-first-estimate",
      "input": "The AI says the migration should take 40 days. Help me refine that estimate.",
      "review": {
        "schemaVersion": "1.0",
        "decision": "Estimate the migration duration.",
        "situation": "ai-assisted-research",
        "observedFacts": [
          "An AI system produced an initial estimate of 40 days."
        ],
        "inferredRisks": [
          {
            "lensId": "cognitive-bias-anchoring-effect",
            "rationale": "The first number may pull later estimates even when its basis is unclear.",
            "confidence": "medium"
          },
          {
            "lensId": "false-priors-automation-bias",
            "rationale": "The generated recommendation may receive more weight than its evidence supports.",
            "confidence": "low"
          }
        ],
        "missingEvidence": [
          "The assumptions behind the 40-day estimate.",
          "Inspectable sources or comparable work supporting the estimate."
        ],
        "questions": [
          "What estimate would we make before seeing 40 days?",
          "What evidence produced the AI number?"
        ],
        "techniques": [
          {
            "id": "independent-estimate",
            "why": "Create an estimate before using the generated number as evidence."
          },
          {
            "id": "source-trace",
            "why": "Trace important claims and numbers to inspectable evidence."
          }
        ],
        "nextAction": "Create an independent estimate, then trace the AI estimate to evidence before updating the range.",
        "uncertainty": "The AI estimate may be useful, but its reliability is unknown until its assumptions and evidence are checked.",
        "canonicalReferences": [
          {
            "type": "situation",
            "id": "ai-assisted-research",
            "url": "https://cognitive-biases.github.io/situations/ai-assisted-research/"
          },
          {
            "type": "bias",
            "id": "cognitive-bias-anchoring-effect",
            "url": "https://cognitive-biases.github.io/biases/cognitive-bias-anchoring-effect/"
          },
          {
            "type": "skill",
            "id": "ai-assisted-reasoning",
            "url": "https://cognitive-biases.github.io/skills/ai-assisted-reasoning/"
          }
        ]
      }
    },
    {
      "id": "incident-obvious-cause",
      "input": "After the outage it is obvious that the team should have known this dependency would fail.",
      "review": {
        "schemaVersion": "1.0",
        "decision": "What process lesson should be taken from the outage?",
        "situation": "production-incident",
        "observedFacts": [
          "An outage occurred.",
          "A dependency failed."
        ],
        "inferredRisks": [
          {
            "lensId": "cognitive-bias-hindsight-bias",
            "rationale": "Knowledge of the outcome can make the failure path feel more predictable than it was beforehand.",
            "confidence": "medium"
          },
          {
            "lensId": "cognitive-bias-outcome-bias",
            "rationale": "The bad outcome may replace a review of the information and process available at the decision point.",
            "confidence": "low"
          }
        ],
        "missingEvidence": [
          "Signals available before the outage.",
          "Alternative failure modes that were plausible at the time."
        ],
        "questions": [
          "What was actually known before the dependency failed?",
          "Would the original process look poor if the outage had not happened?"
        ],
        "techniques": [
          {
            "id": "decision-journal",
            "why": "Compare the review with records created before the outcome."
          },
          {
            "id": "red-team-review",
            "why": "Test the preferred causal story against credible alternatives."
          }
        ],
        "nextAction": "Reconstruct the timeline using only information available at each decision point before changing the process.",
        "uncertainty": "A preventable outage can still coexist with hindsight effects; the review should test both possibilities.",
        "canonicalReferences": [
          {
            "type": "situation",
            "id": "production-incident",
            "url": "https://cognitive-biases.github.io/situations/production-incident/"
          },
          {
            "type": "bias",
            "id": "cognitive-bias-hindsight-bias",
            "url": "https://cognitive-biases.github.io/biases/cognitive-bias-hindsight-bias/"
          },
          {
            "type": "technique",
            "id": "decision-journal",
            "url": "https://cognitive-biases.github.io/techniques/decision-journal/"
          }
        ]
      }
    },
    {
      "id": "repeated-claim",
      "input": "I have seen the same claim on six sites, so it is probably well supported.",
      "review": {
        "schemaVersion": "1.0",
        "decision": "How much confidence to place in the repeated claim.",
        "situation": "checking-repeated-claims",
        "observedFacts": [
          "The claim appears on six sites."
        ],
        "inferredRisks": [
          {
            "lensId": "truth-judgment-illusory-truth-effect",
            "rationale": "Repeated exposure can increase familiarity without adding independent evidence.",
            "confidence": "medium"
          }
        ],
        "missingEvidence": [
          "Whether the six sites rely on independent sources.",
          "The earliest inspectable source."
        ],
        "questions": [
          "How many independent sources are there?",
          "Do the sites trace back to one original claim?"
        ],
        "techniques": [
          {
            "id": "source-trace",
            "why": "Separate independent evidence from repeated copies."
          },
          {
            "id": "predefined-update-criteria",
            "why": "State what evidence would increase or reduce confidence."
          }
        ],
        "nextAction": "Trace the six mentions to their original sources and count independent evidence rather than repetitions.",
        "uncertainty": "Repetition does not prove the claim is false; it only fails to establish independence by itself.",
        "canonicalReferences": [
          {
            "type": "situation",
            "id": "checking-repeated-claims",
            "url": "https://cognitive-biases.github.io/situations/checking-repeated-claims/"
          },
          {
            "type": "bias",
            "id": "truth-judgment-illusory-truth-effect",
            "url": "https://cognitive-biases.github.io/biases/truth-judgment-illusory-truth-effect/"
          },
          {
            "type": "technique",
            "id": "source-trace",
            "url": "https://cognitive-biases.github.io/techniques/source-trace/"
          }
        ]
      }
    },
    {
      "id": "career-sunk-time",
      "input": "I have spent twelve years in this field, so changing direction now would waste all that time.",
      "review": {
        "schemaVersion": "1.0",
        "decision": "Whether to stay in the current field or test a different direction.",
        "situation": "career-change",
        "observedFacts": [
          "Twelve years have already been invested in the current field."
        ],
        "inferredRisks": [
          {
            "lensId": "logical-fallacy-escalation-of-commitment",
            "rationale": "Past investment is being treated as a reason to continue regardless of the next step's value.",
            "confidence": "medium"
          }
        ],
        "missingEvidence": [
          "Future value of staying.",
          "Future value and cost of alternatives."
        ],
        "questions": [
          "Which next year would be best if the past could not be changed?",
          "What small test can reduce uncertainty about the alternative?"
        ],
        "techniques": [
          {
            "id": "incremental-value-check",
            "why": "Judge the next period by future value rather than unrecoverable time."
          },
          {
            "id": "reversible-test",
            "why": "Gather evidence about another path before making a large commitment."
          }
        ],
        "nextAction": "Compare the next twelve months of each option and design one small test of the strongest alternative.",
        "uncertainty": "Past experience can create skills and switching costs, so it can still matter as current evidence.",
        "canonicalReferences": [
          {
            "type": "situation",
            "id": "career-change",
            "url": "https://cognitive-biases.github.io/situations/career-change/"
          },
          {
            "type": "bias",
            "id": "logical-fallacy-escalation-of-commitment",
            "url": "https://cognitive-biases.github.io/biases/logical-fallacy-escalation-of-commitment/"
          },
          {
            "type": "technique",
            "id": "reversible-test",
            "url": "https://cognitive-biases.github.io/techniques/reversible-test/"
          }
        ]
      }
    },
    {
      "id": "performance-last-event",
      "input": "The employee missed the last delivery, so the annual performance rating should be low.",
      "review": {
        "schemaVersion": "1.0",
        "decision": "Determine the annual performance rating.",
        "situation": "performance-review",
        "observedFacts": [
          "The employee missed the most recent delivery."
        ],
        "inferredRisks": [
          {
            "lensId": "heuristic-bias-availability-bias",
            "rationale": "The most recent and vivid event may receive more weight than the full review period.",
            "confidence": "medium"
          },
          {
            "lensId": "cognitive-bias-outcome-bias",
            "rationale": "The delivery outcome may stand in for a review of the underlying work and process.",
            "confidence": "low"
          }
        ],
        "missingEvidence": [
          "Performance evidence across the full period.",
          "Predefined role criteria."
        ],
        "questions": [
          "What evidence represents the full review period?",
          "What evidence supports a materially different rating?"
        ],
        "techniques": [
          {
            "id": "predefined-criteria",
            "why": "Use the same role criteria across the full period."
          },
          {
            "id": "consider-the-opposite",
            "why": "Look for evidence that supports a different conclusion."
          }
        ],
        "nextAction": "Review the full period against predefined criteria before assigning the rating.",
        "uncertainty": "The missed delivery may be important; the review should test its weight rather than automatically discount it.",
        "canonicalReferences": [
          {
            "type": "situation",
            "id": "performance-review",
            "url": "https://cognitive-biases.github.io/situations/performance-review/"
          },
          {
            "type": "bias",
            "id": "heuristic-bias-availability-bias",
            "url": "https://cognitive-biases.github.io/biases/heuristic-bias-availability-bias/"
          },
          {
            "type": "technique",
            "id": "predefined-criteria",
            "url": "https://cognitive-biases.github.io/techniques/predefined-criteria/"
          }
        ]
      }
    },
    {
      "id": "forecast-single-number",
      "input": "Give me one exact launch date. We need a number for the plan.",
      "review": {
        "schemaVersion": "1.0",
        "decision": "Choose how to represent the launch forecast.",
        "situation": "forecasting",
        "observedFacts": [
          "The plan currently requests one exact launch date."
        ],
        "inferredRisks": [
          {
            "lensId": "probability-bias-subadditivity-effect",
            "rationale": "Breaking uncertainty into parts may reveal uncertainty hidden by one headline estimate.",
            "confidence": "low"
          },
          {
            "lensId": "heuristic-bias-availability-bias",
            "rationale": "A recent delivery may dominate the estimate if a broader reference class is not used.",
            "confidence": "low"
          }
        ],
        "missingEvidence": [
          "Comparable delivery history.",
          "A range for major dependencies."
        ],
        "questions": [
          "What range is supported by comparable launches?",
          "Which dependencies push the date toward either end?"
        ],
        "techniques": [
          {
            "id": "reference-class-forecasting",
            "why": "Ground the forecast in completed comparable cases."
          },
          {
            "id": "predefined-update-criteria",
            "why": "Make later changes traceable to new evidence."
          }
        ],
        "nextAction": "Publish a forecast range with assumptions and an update trigger before choosing a planning date inside it.",
        "uncertainty": "Planning may still require one date, but that date should not erase the underlying forecast uncertainty.",
        "canonicalReferences": [
          {
            "type": "situation",
            "id": "forecasting",
            "url": "https://cognitive-biases.github.io/situations/forecasting/"
          },
          {
            "type": "skill",
            "id": "forecasting",
            "url": "https://cognitive-biases.github.io/skills/forecasting/"
          },
          {
            "type": "technique",
            "id": "reference-class-forecasting",
            "url": "https://cognitive-biases.github.io/techniques/reference-class-forecasting/"
          }
        ]
      }
    }
  ]
}