Diablo_ClaudeMD_Ricing_example/skills/decision-toolkit/references/framework-deep-dives.md

Decision Frameworks: Deep Dives

Extended explanations and applications for each framework in the decision toolkit.

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First Principles Thinking

Origin

Aristotle defined first principles as "the first basis from which a thing is known." Elon Musk popularized its modern application: breaking problems down to fundamental truths and reasoning up from there.

The Five Questions

1. What problem does this solve?

   • Not "what does it do" but "what pain does it eliminate"
   • If you can't articulate the problem, the solution may be searching for one

2. Can I solve it myself?

   • With current skills, tools, or reasonable investment
   • Not "can someone solve it" but "can I"

3. Is this the best solution?

   • Compare to alternatives, including doing nothing
   • "Best" includes fit for your context, not just theoretical optimality

4. What assumptions am I making?

   • About the problem, the solution, the context
   • Assumptions are where first principles analysis adds most value

5. If starting fresh today, would I choose this?

   • The "clean slate" test
   • Removes sunk cost and path dependency

Application Example

Decision: Join a startup accelerator

1. Problem: Need funding, network, and credibility to grow
2. Solve myself?: Can network independently (slower). Can't easily replicate funding access
3. Best solution?: Other accelerators? Angel investors? Bootstrapping?
4. Assumptions: Accelerator will actually provide value. Equity cost is worth it. My startup fits their focus.
5. Fresh start?: If I hadn't applied yet, would I? Yes — for network. Maybe — for funding.

When to Use

• Evaluating opportunities with significant commitment
• When feeling pressured by external narratives
• When the "obvious" choice feels uncomfortable

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Opportunity Cost Calculator

The Framework

Direct Cost = Hours × Rate
True Cost = Direct Cost + Best Alternative Value + Relationship/Growth Costs

Components

Direct Cost

• Hours/week × weeks × hourly rate
• Use your actual billing rate, or time value equivalent

Best Alternative Value

• What would those hours produce if used differently?
• Not all alternatives — just the best one you'd actually pursue

Relationship/Growth Costs

• What connections might suffer from reduced attention?
• What skills won't develop because you're focused elsewhere?

Application Example

Decision: Take on a 10-hour/week consulting gig for 3 months

Component	Calculation	Value
Direct time	10h × 12 weeks	120 hours
At rate	120 × $200	$24,000
Best alternative	Could build feature worth ~$50K revenue	~$50,000
Relationship cost	Less time for key partnership	Hard to quantify
True opportunity cost		$50,000+

Insight: The $24K consulting gig actually costs ~$50K in forgone product development.

Common Mistakes

1. Ignoring alternatives: Focusing only on time, not what that time could create
2. Undervaluing rest: Recovery time has value; burnout has cost
3. Ignoring compounding: Small weekly investments compound; opportunity cost does too
4. Only counting money: Relationships, learning, and energy have value

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Scenario Matrix

Structure

Scenario	Probability	Outcome	Impact	Expected Value
Worst	X%	Description	-$Y or equivalent	Prob × Impact
Bad	X%
Neutral	X%
Good	X%
Best	X%
Total	100%			Sum

Probability Calibration

Most people are poorly calibrated. Use these anchors:

Probability	Roughly means
5%	"Unlikely but possible"
20%	"Could happen"
50%	"Coin flip"
80%	"Probably"
95%	"Almost certain"

Technique: If you can't assign a probability, estimate "what odds would I accept if betting on this?"

Application Example

Decision: Launch product in January vs April

Scenario	Prob	January Launch	April Launch
Market shifts	20%	-$50K (too early)	-$20K (adapted)
Competitor launches	15%	+$100K (first)	-$30K (second)
Product ready	30%	+$200K	+$200K
Product buggy	35%	-$80K (reputation)	+$180K (polished)

January EV: (0.20×-50K) + (0.15×100K) + (0.30×200K) + (0.35×-80K) = +$27K April EV: (0.20×-20K) + (0.15×-30K) + (0.30×200K) + (0.35×180K) = +$114.5K

Insight: April launch has higher expected value despite missing first-mover scenario.

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Pre-Mortem Analysis

Origin

Gary Klein developed the pre-mortem in 1989. Unlike post-mortems (analyzing what went wrong after failure), pre-mortems imagine failure before it happens.

The Process

1. Assume failure: "It's [future date]. This decision completely failed."
2. Generate causes: Brainstorm every possible reason for failure
3. Categorize: Which causes are within/outside your control?
4. Identify signals: What warning signs would precede each cause?
5. Create triggers: What will you do if you see those signals?

Application Example

Decision: Hire a new senior engineer

Imagine: It's 6 months later. This hire was a disaster.

Possible causes:

1. Culture mismatch — brilliant but toxic to team
2. Skill mismatch — impressive resume, wrong tech stack
3. Motivation mismatch — wanted different type of work
4. Timing — we couldn't provide promised projects
5. Onboarding failure — we didn't set them up for success
6. External — they got a better offer and left

Within control: 1, 2, 3, 5 (interview better, onboard better) Outside control: 4, 6 (market conditions)

Warning signals:

• Team complaints in first 2 weeks (cause 1)
• Struggles with core tasks (cause 2)
• Asks about different projects repeatedly (cause 3)

Triggers:

• Weekly 1:1s for first month focusing on fit
• 30/60/90 day technical assessments
• Explicit "are you getting what you expected" conversations

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10-10-10 Framework

Origin

Suzy Welch developed this framework to overcome short-term emotional reactions in decision-making.

The Three Windows

10 Minutes: How will I feel immediately after deciding?

• Captures emotional reaction
• Often dominated by fear, excitement, or relief

10 Months: How will I feel after living with this decision?

• Captures medium-term consequences
• Initial emotions usually faded
• Actual outcomes starting to materialize

10 Years: How will I feel looking back on this decision?

• Captures long-term significance
• Most decisions barely remembered
• Regret patterns become clear

Application Example

Decision: Accept job offer at 20% pay cut for better work-life balance

Window	Likely Feeling	What it tells us
10 min	Anxious about money	Normal loss aversion
10 months	Grateful for evenings	Lifestyle has changed
10 years	Barely remember the salary	Money was fungible; time wasn't

Insight: The 10-year view reveals this is primarily a lifestyle decision, not a financial one.

When to Use

• Emotional decisions (relationship changes, big purchases)
• When short-term pain might mask long-term gain
• When caught in analysis paralysis

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Regret Minimization

Origin

Jeff Bezos used this framework to decide whether to leave his finance job to start Amazon.

The Mental Exercise

1. Imagine yourself at 80 years old
2. Looking back at your life
3. Ask: "Will I regret doing this?" and "Will I regret NOT doing this?"

The Asymmetry of Regret

Research shows people regret things they didn't do more than things they did:

• Actions: Initial regret fades as we rationalize, learn, or adapt
• Inactions: Regret compounds as we wonder "what if"

Application Example

Decision: Leave stable corporate job to start a company

At 80, will I regret starting the company?

• If it fails: "I tried. I learned. I'll never wonder."
• If it succeeds: "Life-defining achievement."

At 80, will I regret NOT starting?

• "I always wondered what could have been."
• "I played it safe and got the safe outcome."

Insight: For this type of decision, the regret of inaction typically exceeds the regret of failed action.

Limitations

• Doesn't apply to genuinely irreversible or catastrophic decisions
• Can romanticize risk-taking; consider actual stakes
• Should complement, not replace, rational analysis

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Weighted Decision Matrix

Structure

Criterion	Weight (1-10)	Option A	Option B	Option C
Factor 1	8	Score 1-10	Score 1-10	Score 1-10
Factor 2	6
Factor 3	9
Weighted Total		Σ(weight×score)

Process

1. List criteria: All factors that matter for this decision
2. Weight criteria: How important is each? (1=low, 10=critical)
3. Score options: How well does each option perform? (1=poor, 10=excellent)
4. Calculate: Multiply weights × scores, sum totals
5. Sanity check: Does the winner feel right? If not, examine why.

Application Example

Decision: Choose between three job offers

Criterion	Weight	Company A	Company B	Company C
Compensation	8	9	6	8
Growth opportunity	9	5	9	7
Work-life balance	7	4	7	8
Team quality	8	8	7	6
Mission alignment	6	3	8	5
Weighted Total		225	281	262

Winner: Company B (despite lower compensation)

Caveats

• Weights reveal your values — examine them
• Gut disagreement with result is data (what did you miss?)
• Don't over-engineer; 5-7 criteria usually sufficient

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Integration: When to Use What

Framework	Best For	Key Question
First Principles	Evaluating opportunities	Can I solve this myself?
Opportunity Cost	Resource allocation	What am I giving up?
Scenario Matrix	Risk assessment	What could happen?
Pre-Mortem	Risk mitigation	How could this fail?
10-10-10	Emotional decisions	How will I feel later?
Regret Minimization	Life decisions	What will 80-year-old me think?
Weighted Matrix	Comparing options	Which scores highest?

Combining Frameworks

For major decisions, use multiple frameworks:

1. First Principles: Is this worth considering at all?
2. Opportunity Cost: Can I afford it?
3. Scenario Matrix: What are the likely outcomes?
4. Pre-Mortem: How do I mitigate risks?
5. 10-10-10 / Regret: Does it align with my values?
6. Final check: Does the conclusion feel right?