Explore decision branches with probability weighting, expected value analysis, and scenario-based optimization.
Analyzes complex decisions using probability weighting, expected value calculations, and scenario-based optimization.
/plugin marketplace add davepoon/buildwithclaude/plugin install all-commands@buildwithclaudeSpecify decision tree parametersExplore decision branches with probability weighting, expected value analysis, and scenario-based optimization.
You are tasked with creating a comprehensive decision tree analysis to explore complex decision scenarios and optimize choice outcomes. Follow this systematic approach: $ARGUMENTS
Critical Decision Context Validation:
If any context is unclear, guide systematically:
Missing Decision Scope:
"I need clarity on the decision you're analyzing. Please specify:
- Primary Decision: The main choice you need to make
- Decision Level: Strategic, tactical, or operational
- Decision Type: Go/no-go, resource allocation, priority ranking, or option selection
- Alternative Options: What choices are you considering?
Examples:
- Strategic: 'Should we enter the European market next year?'
- Investment: 'Which of 3 product features should we build first?'
- Operational: 'Should we migrate to microservices or improve the monolith?'
- Crisis: 'How should we respond to the new competitor launch?'"
Missing Success Criteria:
"How will you evaluate if this decision was successful?
- Financial Metrics: Revenue impact, cost savings, ROI targets
- Strategic Metrics: Market share, competitive position, capability building
- Operational Metrics: Efficiency gains, quality improvements, risk reduction
- Timeline Metrics: Speed to market, implementation time, payback period"
Missing Resource Context:
"What constraints limit your decision options?
- Budget: Available investment capital and operating funds
- Time: Implementation deadlines and resource availability windows
- Capabilities: Team skills, technology infrastructure, operational capacity
- Regulatory: Compliance requirements and approval processes"
Structure the decision systematically:
Systematically identify and organize decision alternatives:
For each option, evaluate:
- Technical Feasibility: Can this actually be implemented?
- Economic Feasibility: Do benefits justify costs?
- Operational Feasibility: Do we have capability to execute?
- Timeline Feasibility: Can this be done in available time?
- Political Feasibility: Will stakeholders support this?
Feasibility Scoring (1-10 scale):
Option: [name]
- Technical: [score] - [reasoning]
- Economic: [score] - [reasoning]
- Operational: [score] - [reasoning]
- Timeline: [score] - [reasoning]
- Political: [score] - [reasoning]
Overall Feasibility: [average score]
Apply systematic probability estimation:
Use multiple estimation approaches:
1. Historical Data Analysis:
- Similar past decisions and outcomes
- Success/failure rates in comparable situations
- Market adoption patterns for similar offerings
2. Expert Consultation:
- Domain expert probability estimates
- Cross-functional team input and perspectives
- External advisor and consultant insights
3. Market Validation:
- Customer research and feedback
- Competitive analysis and market dynamics
- Regulatory and environmental factor assessment
4. Monte Carlo Simulation:
- Run multiple probability scenarios
- Test sensitivity to assumption changes
- Generate confidence intervals for estimates
Quantify decision outcomes systematically:
Value Calculation Framework:
Financial Value:
- Direct Revenue Impact: $[amount] ± [uncertainty range]
- Cost Savings: $[amount] ± [uncertainty range]
- Investment Required: $[amount] and timeline
- NPV Calculation: $[net present value] over [timeframe]
Strategic Value:
- Market Position Improvement: [qualitative + quantitative]
- Competitive Advantage Creation: [sustainable differentiation]
- Capability Building: [new skills and infrastructure]
- Option Value: [future opportunities enabled]
Risk Value:
- Risk Reduction: [quantified risk mitigation]
- Downside Protection: [worst-case scenario costs]
- Opportunity Cost: [alternative options foregone]
- Reversibility: [cost and difficulty of changing course]
Expected Value Formula Application:
EV = Σ(Probability × Outcome Value) for all scenarios
Example Calculation:
Option A: New Product Launch
- Best Case (20% probability): $10M revenue, 80% margin = $8M profit
- Base Case (60% probability): $5M revenue, 70% margin = $3.5M profit
- Worst Case (20% probability): $1M revenue, 50% margin = $0.5M profit
Expected Value = (0.20 × $8M) + (0.60 × $3.5M) + (0.20 × $0.5M)
= $1.6M + $2.1M + $0.1M = $3.8M
Investment Required: $2M
Net Expected Value: $1.8M
Comprehensively assess decision risks:
Risk Mitigation Framework:
For each significant risk:
1. Risk Description: [specific risk scenario]
2. Probability Assessment: [likelihood of occurrence]
3. Impact Assessment: [severity if it occurs]
4. Early Warning Indicators: [signals to watch for]
5. Prevention Strategies: [actions to reduce probability]
6. Mitigation Strategies: [actions to reduce impact]
7. Contingency Plans: [responses if risk materializes]
8. Risk Ownership: [who monitors and responds]
Create clear decision tree representations:
Decision Tree Format:
[Decision Point]
├── Option A [probability: X%]
│ ├── Scenario A1 [probability: Y%] → Outcome: $Z
│ ├── Scenario A2 [probability: Y%] → Outcome: $Z
│ └── Scenario A3 [probability: Y%] → Outcome: $Z
├── Option B [probability: X%]
│ ├── Scenario B1 [probability: Y%] → Outcome: $Z
│ └── Scenario B2 [probability: Y%] → Outcome: $Z
└── Option C [probability: X%]
└── Scenario C1 [probability: Y%] → Outcome: $Z
Expected Values:
- Option A: $[calculated EV]
- Option B: $[calculated EV]
- Option C: $[calculated EV]
Generate data-driven decision recommendations:
Decision Recommendation Format:
## Primary Recommendation: [Selected Option]
### Executive Summary
- Recommended Decision: [specific choice and rationale]
- Expected Value: $[amount] with [confidence level]%
- Key Success Factors: [critical requirements for success]
- Major Risks: [primary concerns and mitigation approaches]
- Implementation Timeline: [key milestones and dependencies]
### Supporting Analysis
- Expected Value Calculation: [detailed breakdown]
- Probability Assessments: [key assumptions and sources]
- Risk Assessment: [major risks and mitigation strategies]
- Sensitivity Analysis: [critical variables and break-even points]
- Alternative Options: [other viable choices and trade-offs]
### Implementation Guidance
- Immediate Next Steps: [specific actions required]
- Success Metrics: [measurable indicators of progress]
- Decision Points: [future choice points and triggers]
- Resource Requirements: [budget, team, timeline needs]
- Stakeholder Communication: [alignment and buy-in strategies]
### Contingency Planning
- Plan B Options: [alternative approaches if primary fails]
- Early Warning Systems: [risk monitoring and triggers]
- Decision Reversal: [exit strategies and switching costs]
- Adaptive Strategies: [adjustment mechanisms for changing conditions]
Ensure robust decision-making process:
Establish decision quality improvement:
# Strategic business decision
/simulation:decision-tree-explorer Should we acquire competitor X for $50M or build competing product internally?
# Product development prioritization
/simulation:decision-tree-explorer Which of 5 product features should we build first given limited engineering resources?
# Technology architecture choice
/simulation:decision-tree-explorer Microservices vs monolith architecture for our new platform?
# Market expansion decision
/simulation:decision-tree-explorer European market entry strategy: direct expansion vs partnership vs acquisition?
Transform complex decisions into systematic analysis for exponentially better choice outcomes.