Decision Synthesis

Core principle: Analysis produces options and criteria. Synthesis produces a decision. Most frameworks are good at divergence — generating possibilities, mapping complexity. This skill is about convergence: taking what you know and making a defensible, traceable choice.

A good decision process doesn't guarantee the right outcome. It maximizes the quality of reasoning given available information, and it's transparent enough to learn from when outcomes arrive.

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When to Use This Skill

Use after other reasoning frameworks have done their work:

• Systems Thinking mapped the structure
• 5 Whys found the root causes
• Scenario Planning produced multiple futures
• Red Teaming attacked the options
• Stakeholder Mapping identified who needs to align

Now you have a rich picture and multiple options. Decision Synthesis is how you land.

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The Core Process

Step 1: Clarify What's Actually Being Decided

Before evaluating options, make the decision crisp:

• What is the exact choice being made?
• What's the decision horizon? (reversible in 3 months? irreversible?)
• Who has final authority?
• What's the cost of delaying the decision?

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

Step 2: Surface All Options

List every viable option explicitly — including:

• The status quo (doing nothing is always an option)
• Hybrid approaches
• Sequenced approaches (do A now, revisit B in 6 months)
• Options that were dismissed early but deserve a formal look

Step 3: Define Criteria

What matters in this decision? List the criteria explicitly:

• Must-haves (binary — options failing these are eliminated)
• Want-to-haves (graded — options are compared on these)

Good criteria are:

• Specific enough to score (not "good quality" but "error rate < 1%")
• Independent of each other (avoid double-counting)
• Tied to the actual goal, not proxies

Step 4: Weight the Criteria

Not all criteria are equally important. Assign weights explicitly — this forces clarity about what actually matters most and exposes hidden disagreements between stakeholders.

Simple approach: distribute 100 points across criteria. The allocation is the conversation.

Step 5: Score the Options

For each option against each criterion, score 1–5 (or 1–10). Be explicit about the reasoning for each score — scores without reasoning can't be challenged or improved.

Step 6: Compute and Challenge

Weighted scores give a quantitative signal — not a verdict. Use the result to:

• Check: does the top scorer match intuition? If not, why not?
• Interrogate: which criteria are driving the result? Are they the right ones?
• Stress-test: if the top two criteria swap weights, does the answer change?
• Sanity-check: would you be comfortable explaining this choice to a critic?

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Output Format

🎯 Decision Statement

• Decision: [The exact choice being made]
• Horizon: [Reversible / Partially reversible / Irreversible]
• Decider: [Who has authority]
• Deadline: [When this must be resolved]

📋 Options

#	Option	Brief description
1	[Name]	[One line]
2	...

⚖️ Criteria & Weights

Criterion	Type	Weight	Rationale
[Criterion 1]	Must-have	—	[Why it's binary]
[Criterion 2]	Want-to-have	35	[Why this weight]
[Criterion 3]	Want-to-have	25
...		100

📊 Scoring Matrix

Option	Criterion 1	Criterion 2 (×35)	Criterion 3 (×25)	...	Weighted Total
Option A	✅ Pass	4 → 140	3 → 75		X
Option B	✅ Pass	2 → 70	5 → 125		Y
Option C	❌ Fail	—	—		Eliminated

🏆 Recommendation

• Recommended option: [Name]
• Primary reason: [The 1–2 criteria that drove the result]
• Main trade-off: [What this option sacrifices]
• Confidence: [High / Medium / Low — based on quality of information, not strength of preference]

⚠️ Sensitivity Check

• If [top-weighted criterion] changes in importance, does the answer change?
• What assumption, if wrong, most undermines this recommendation?
• What new information would cause a re-evaluation?

🔁 Reversibility & Regret

• Can this be undone? At what cost?
• Regret minimization: Which choice produces the least regret if the situation changes significantly?
• If confidence is low and the decision is irreversible — flag this explicitly before committing.

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Decision Traps to Avoid

False consensus: Everyone nods but the criteria weights were never made explicit — different people were solving for different things.

Analysis paralysis: More analysis rarely resolves genuine value disagreements. Name the disagreement and make the call.

Criteria inflation: Adding more criteria to feel thorough — but irrelevant criteria add noise, not signal. Keep the list short and honest.

Anchoring on the first option: The option framed first gets disproportionate attention. Evaluate all options in parallel, not sequentially.

Score laundering: Working backwards from a preferred conclusion to assign scores that justify it. The matrix is a thinking tool, not a legitimacy machine.

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Thinking Triggers

• "If I had to make this decision alone, with no politics involved, what would I choose?"
• "Which criteria are we weighting based on what actually matters vs. what's easy to measure?"
• "Is there a hybrid option we haven't named?"
• "What would a regret minimizer choose? A risk minimizer? A maximizer?"
• "Are we delaying because we need more information, or because we don't want to own the decision?"