Decision synthesis

Decision Synthesis

Core principle: Analysis produces options and criteria. Synthesis produces a decision. Most frameworks diverge — generate possibilities, map complexity. This skill converges: makes a defensible, traceable choice. A good decision process maximizes reasoning quality given available information and is transparent enough to learn from.

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When to Use

After other frameworks have done their work — Systems Thinking mapped structure, 5 Whys found root causes, Scenario Planning produced futures, Red Teaming attacked options, Stakeholder Mapping identified alignment needs. Now: a rich picture, multiple options, time to land.

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The Core Process

Step 1: Clarify What's Being Decided

• Exact choice?
• Decision horizon (reversible 3 months? irreversible?)?
• Final authority?
• Cost of delay?

Many processes fail because people are evaluating different questions without realizing it.

Step 2: Surface All Options

List every viable option, including:

• Status quo (always an option)
• Hybrid approaches
• Sequenced approaches (do A now, revisit B in 6 months)
• Options dismissed early but worth a formal look

Step 3: Define Criteria

• Must-haves (binary — failing = eliminated)
• Want-to-haves (graded — options compared)

Good criteria: specific enough to score ("error rate < 1%"), independent (no double-counting), tied to actual goal not proxies.

Step 4: Weight the Criteria

Distribute 100 points. The allocation is the conversation — exposes hidden disagreements between stakeholders.

Step 5: Score the Options

Score each option × criterion 1–5 (or 1–10) with explicit reasoning. Scores without reasoning can't be challenged.

Step 6: Compute and Challenge

Weighted scores = signal, not verdict:

• Top scorer match intuition? If not, why?
• Which criteria drive the result? Right ones?
• Swap top-two weights — does the answer change?
• Comfortable defending this to a critic?

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Output Format

Decision Statement

• Decision: [Exact choice]
• Horizon: [Reversible / Partially / Irreversible]
• Decider: [Authority]
• Deadline: [When resolved]

Options

#	Option	Brief description
1	[Name]	[One line]

Criteria & Weights

Criterion	Type	Weight	Rationale
[C1]	Must-have	—	[Why binary]
[C2]	Want-to-have	35	[Why this weight]
[C3]	Want-to-have	25
		100

Scoring Matrix

Option	C1	C2 (×35)	C3 (×25)	Weighted Total
A	Pass	4 → 140	3 → 75	X
B	Pass	2 → 70	5 → 125	Y
C	Fail	—	—	Eliminated

Recommendation

• Recommended: [Name]
• Primary reason: [1–2 criteria driving result]
• Main trade-off: [What it sacrifices]
• Confidence: [High/Med/Low — based on info quality, not preference strength]

Sensitivity Check

• If [top criterion] changes weight, does answer change?
• Which assumption, if wrong, most undermines this?
• What new info would re-open the decision?

Reversibility & Regret

• Can it be undone? At what cost?
• Regret minimization: which choice produces least regret if situation shifts?
• Low confidence + irreversible → flag explicitly before committing.

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Decision Traps

• False consensus: Everyone nods but criteria weights were never explicit — different people solving different things.
• Analysis paralysis: More analysis rarely resolves value disagreements. Name the disagreement and call it.
• Criteria inflation: More criteria adds noise, not signal. Keep the list short and honest.
• Anchoring on first option: Evaluate all options in parallel, not sequentially.
• Score laundering: Working backwards from a preferred conclusion. The matrix is a thinking tool, not a legitimacy machine.

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Thinking Triggers

• "If I decided alone, no politics, what would I choose?"
• "Are we weighting what matters or what's easy to measure?"
• "Is there a hybrid we haven't named?"
• "What would a regret minimizer choose? A risk minimizer? A maximizer?"
• "Are we delaying because we need information, or because we don't want to own the decision?"