PWR-119 · Reasoning, Judgement & Strategy

Decision under uncertainty

Good decisions under uncertainty are built by a configured system—not by confidence, a single formula or an AI answer.

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

One source of teaching truth

Canonical Power learning unit · TLU-PWR-119

The learner produces a traceable fictional decision record with options, probability ranges, consequences, protected constraints, sensitivity analysis, threshold and update rule.

Canonical Power page: PWR-119 · Decision under uncertainty
Full tutorial: Open full tutorial
Practical authority: The tutorial teaches a low-risk method that may be practised inside its stated limits.
Current treatment: Full low-stakes tutorial
Research depth: deep · 6 bound sources
Risk framing: high
Capability self-practice: Permitted inside the tutorial's stated low-risk limits
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 decision under uncertainty in a declared context without inheriting broader claims.

What the current evidence supports

Selected components of decision-making under uncertainty can improve, but outcomes depend on the person, information, values, interface, institution and stakes.

How to observe or measure it without overclaiming

Declare options, values, probabilities, data, AI/version, timing, incentives, affected groups, process, outcome and appeal; accuracy is not the only legitimate value.

Myth

Always choose the optimal answer

Metric

Decision process, calibration, value alignment, harms and outcomes under declared uncertainty

Boundary

No laboratory task or model output can determine universally correct high-stakes choices.

Negative and limiting findings

Incorrect AI advice reduced accuracy even in human-in-the-loop designs.
A human-first interface reduced but did not eliminate AI influence, and one forcing design reduced usability.

Claims this evidence cannot support

make the optimal decision every time
remove uncertainty
trust the algorithm
decision score overrides lived experience

Individual tutorial · FULL SELF GUIDED TUTORIAL

How to learn this Power now.

The learner produces a traceable fictional decision record with options, probability ranges, consequences, protected constraints, sensitivity analysis, threshold and update rule.

Get readyWrite FICTIONAL PRACTICE at the top.
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.
PractiseUse one new fictional case weekly for four weeks, alternating cost, time and non-numeric consequences. On the fourth case, have another learner audit the authority, constraints and sensitivity rather than agree with the choice.
Check progressCheck owner, options, mutually exclusive states, probability range and source, consequences, affected roles, constraints, reversibility, threshold and update rule. Reproduce each expected-cost row as Σ probability(state) × cost(option,state) at both range ends and verify probabilities sum to 1.

Authority boundary: Titan teaches a concrete low-stakes method step by step while keeping evidence and transfer claims bounded. The method is Power-specific; its actionability follows this treatment.

Open the full tutorialOpen My Power Path

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 effect of problem-solving and decision-making education on problem-solving and decision-making skills of nurse managers: A randomized controlled trial
Berra Yilmaz Kusakli; Betül Sönmez · 2024 · Primary research
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
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-119/. 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 self guided mode remains controlling.