Decision Tree Explorer

Explore decision branches with probability weighting, expected value analysis, and scenario-based optimization.

Instructions

You are tasked with creating a comprehensive decision tree analysis to explore complex decision scenarios and optimize choice outcomes. Follow this systematic approach: $ARGUMENTS

1. Prerequisites Assessment

Critical Decision Context Validation:

• Decision Scope: What specific decision(s) need to be made?
• Stakeholders: Who will be affected by and involved in this decision?
• Time Constraints: What are the decision deadlines and implementation timelines?
• Success Criteria: How will you measure decision success or failure?
• Resource Constraints: What limitations affect available options?

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"

2. Decision Architecture Mapping

Structure the decision systematically:

Decision Hierarchy

• Primary decision point and core question
• Secondary decisions that follow from primary choice
• Tertiary decisions and implementation details
• Decision dependencies and sequencing requirements
• Option combinations and interaction effects

Stakeholder Impact Analysis

• Decision makers and approval authorities
• Implementation teams and resource owners
• Customers and end users affected
• External partners and dependencies
• Competitive landscape implications

Constraint Identification

• Hard constraints (cannot be violated)
• Soft constraints (preferences and trade-offs)
• Temporal constraints (timing and sequencing)
• Resource constraints (budget, capacity, capabilities)
• Regulatory and compliance constraints

3. Option Generation and Structuring

Systematically identify and organize decision alternatives:

Comprehensive Option Development

• Direct approaches to achieving the goal
• Hybrid solutions combining multiple approaches
• Phased approaches with incremental implementation
• Alternative goals that might better serve needs
• "Do nothing" baseline for comparison

Option Categorization

• Quick wins vs. long-term strategic moves
• High-risk/high-reward vs. safe/incremental options
• Resource-intensive vs. lean approaches
• Internal development vs. external partnerships
• Proven approaches vs. innovative experiments

Option Feasibility Assessment

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]

4. Probability Assessment Framework

Apply systematic probability estimation:

Base Rate Analysis

• Historical success rates for similar decisions
• Industry benchmarks and comparative data
• Expert judgment and domain knowledge
• Market research and customer validation data
• Internal capability assessment and track record

Scenario Probability Weighting

• Best case scenario probabilities (optimistic outcomes)
• Most likely scenario probabilities (base case expectations)
• Worst case scenario probabilities (pessimistic outcomes)
• Black swan event probabilities (extreme scenarios)
• Competitive response probabilities

Probability Calibration Methods

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

5. Expected Value Calculation

Quantify decision outcomes systematically:

Outcome Quantification

• Financial returns and cost implications
• Strategic value and competitive advantages
• Risk reduction and option value creation
• Time savings and efficiency improvements
• Learning value and capability building

Multi-Dimensional Value Assessment

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 Integration

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

6. Risk Analysis and Sensitivity Testing

Comprehensively assess decision risks:

Risk Identification Matrix

• Implementation risks (execution challenges)
• Market risks (demand, competition, economic changes)
• Technology risks (technical feasibility, obsolescence)
• Regulatory risks (compliance, approval, policy changes)
• Resource risks (availability, capability, cost overruns)

Sensitivity Analysis

• Key assumption stress testing
• Break-even analysis for critical variables
• Scenario analysis with parameter variations
• Confidence interval calculation for outcomes
• Robustness testing across different conditions

Risk Mitigation Strategy Development

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]

7. Decision Tree Visualization and Analysis

Create clear decision tree representations:

Tree Structure Design

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]

Decision Path Analysis

• Optimal path identification based on expected value
• Alternative paths with acceptable risk/return profiles
• Contingency routing based on early decision outcomes
• Information value analysis (worth of additional research)
• Real option valuation (value of delaying decisions)

8. Optimization and Recommendation Engine

Generate data-driven decision recommendations:

Multi-Criteria Decision Analysis

• Weighted scoring across multiple decision criteria
• Trade-off analysis between competing objectives
• Pareto frontier identification for efficient solutions
• Stakeholder preference integration
• Scenario robustness across different weighting schemes

Recommendation Generation

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]

9. Decision Quality Validation

Ensure robust decision-making process:

Process Quality Checklist

• All relevant stakeholders consulted
• Comprehensive option generation completed
• Probability assessments calibrated with data
• Value calculations include all material factors
• Risks identified and mitigation planned
• Assumptions explicitly documented and tested
• Decision criteria clearly defined and weighted
• Implementation feasibility validated

Bias Detection and Mitigation

• Confirmation bias: Seeking information that supports preferences
• Anchoring bias: Over-relying on first information received
• Availability bias: Overweighting easily recalled examples
• Optimism bias: Overestimating positive outcomes
• Sunk cost fallacy: Continuing failed approaches
• Analysis paralysis: Over-analyzing instead of deciding

Decision Documentation

• Decision rationale and supporting analysis
• Key assumptions and probability assessments
• Alternative options considered and rejected
• Stakeholder input and consultation process
• Risk assessment and mitigation strategies
• Implementation plan and success metrics

10. Learning and Feedback Integration

Establish decision quality improvement:

Decision Outcome Tracking

• Actual vs. predicted outcomes measurement
• Assumption validation against real results
• Decision timing and implementation effectiveness
• Stakeholder satisfaction and support levels
• Unintended consequences and side effects

Continuous Improvement

• Decision-making process refinement
• Probability calibration improvement over time
• Risk assessment accuracy enhancement
• Stakeholder engagement optimization
• Tool and framework evolution

Usage Examples

# 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?

Quality Indicators

• Green: Comprehensive options, calibrated probabilities, quantified outcomes, documented assumptions
• Yellow: Good option coverage, reasonable probability estimates, partially quantified outcomes
• Red: Limited options, uncalibrated probabilities, qualitative-only outcomes

Common Pitfalls to Avoid

• Analysis paralysis: Over-analyzing instead of making timely decisions
• False precision: Using precise numbers for uncertain estimates
• Option tunnel vision: Not considering creative alternatives
• Probability miscalibration: Overconfidence in likelihood estimates
• Value tunnel vision: Focusing only on financial outcomes
• Implementation blindness: Not considering execution challenges

Transform complex decisions into systematic analysis for exponentially better choice outcomes.