PWR-118 · Reasoning, Judgement & Strategy

Expert judgement

Expert judgment is real but conditional: accuracy, calibration, domain, incentives and tools all matter.

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Revision 7T · Full Tutorial Edition · Release manifest · Methodology · Corrections

One source of teaching truth

Canonical Power learning unit · TLU-PWR-118

Under a qualified provider, the learner completes at least 20 resolved domain cases and produces a Brier score (mean squared probability error), calibration bands, discrimination and an error review for the declared case mix.

Canonical Power page: PWR-118 · Expert judgement
Full tutorial: Open full tutorial
Practical authority: The tutorial teaches the complete method; qualified supervision controls the specified practical parts.
Current treatment: Full supervised tutorial
Research depth: deep · 6 bound sources
Risk framing: high
Capability self-practice: Qualified supervision is required for the practical method
Pathway membership: G-CUR-011

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Explanation is not permission.

This dossier explains the evidence and its limits. It is not a diagnosis, personal recommendation, assessment, clearance, performance promise or training programme. Actionable teaching appears only when the canonical Power record explicitly authorises it.

Complete bounded explanation

What this power means.

Capability to develop or express expert judgement in a declared context without inheriting broader claims.

What the current evidence supports

Expert judgment can be assessed and sometimes improved within a defined domain, feedback environment and accountable decision system.

How to observe or measure it without overclaiming

Declare domain, case mix, outcome delay, base rates, calibration, discrimination, conflicts, AI/version, overrides and appeals; credentials and confidence are not outcome accuracy.

Myth

Expert intuition never fails

Metric

Calibration, discrimination and consequential outcomes on a declared case set

Boundary

Credentials, eloquence or one domain score cannot certify universal judgment.

Negative and limiting findings

Confidence and eloquence can increase persuasion independently of forecast accuracy.
Incorrect AI advice reduced human accuracy, and interface protections were incomplete and configuration-specific.

Claims this evidence cannot support

expert intuition is always right
debias the expert
AI plus expert cannot fail
one score identifies the best decision-maker

Individual tutorial · FULL SUPERVISED TUTORIAL

How to learn this Power now.

Get readyProvider verifies scope, confidentiality, case representativeness and legitimate use.
Learn the method7 concrete steps teach the permitted method from start to finish.
See right and wrong4 comparisons show correct or safer execution beside common wrong or riskier choices.
PractiseThe provider schedules fresh resolved blocks until the sample is adequate and feedback remains representative. Live or operational cases are not used for unsupervised practice. Retest after delay and whenever the case mix, model or domain changes.
Check progressFor each resolved case calculate (p−o)² using forecast p on 0–1 and outcome o in {0,1}; average across cases for the Brier score. For each predeclared bin report mean p, occurred/total and their gap. Report mean p for occurred versus non-occurred cases, case count, base rate and missing outcomes.

Authority boundary: Titan teaches the complete method; a qualified person controls the parts that require supervision, clearance, specialist equipment, load, dose or progression. The method is Power-specific; its actionability follows this treatment.

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Related Powers: PWR-113 · PWR-114 · PWR-115 · PWR-116

Direct evidence register

Sources that support—and limit—the claim.

Citation roles are explicit. A source may support existence or trainability while simultaneously limiting transfer, certainty, generalisation or safety.

Primary empirical supportLimiting / contrary
The psychology of intelligence analysis: Drivers of prediction accuracy in world politics
Barbara Mellers; Eric Stone; Pavel Atanasov; Nick Rohrbaugh; S. Emlen Metz; Lyle Ungar; Michael M. Bishop; Michael Horowitz; Ed Merkle; Philip Tetlock · 2015 · Primary research
Primary empirical supportLimiting / contrary
To Trust or to Think: Cognitive Forcing Functions Can Reduce Overreliance on AI in AI-assisted Decision-making
Zana Buçinca; Maja Barbara Malaya; Krzysztof Z. Gajos · 2021 · Primary research
Limiting / contrary
Do People Listen to Cassandra? Persuasion and Accuracy in Geopolitical Forecasts
Yhonatan S. Shemesh; Avi Gamoran; David Leiser; Michael Gilead · 2025 · Primary research
Primary empirical supportLimiting / contrary
Debiasing Decisions
Carey K. Morewedge; Haewon Yoon; Irene Scopelliti; Carl W. Symborski; James H. Korris; Karim S. Kassam · 2015 · Primary research
Primary empirical supportLimiting / contrary
The impact of AI errors in a human-in-the-loop process
Ujué Agudo; Karlos G. Liberal; Miren Arrese; Helena Matute · 2024 · Primary research
Limiting / contraryOfficial boundary context
Artificial Intelligence Risk Management Framework (AI RMF 1.0)
National Institute of Standards and Technology · 2023 · Official authority

Current teaching and evidence boundary

The next gate remains visible.

External confirmation is required before higher-authority use because: a clinical, high-risk or regulated configuration; a restricted safety or legitimacy boundary.

A Power-specific tutorial is available at /tutorials/pwr-118/. It contains a plain-English method, 7 ordered steps, a worked example, right-versus-wrong comparisons, specific mistakes and corrections, practice, measurement, accessibility, stopping rules and evidence. Its supervised mode remains controlling.