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Decision Trees

Decision tree analysis for complex decision-making across all domains. Use when user needs to evaluate multiple options with uncertain outcomes, assess risk/reward scenarios, or structure choices systematically. Applicable to business, investment, personal decisions, operations, career choices, product strategy, and any situation requiring structured evaluation. Triggers include decision tree, should I, what if, evaluate options, compare alternatives, risk analysis.

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Decision tree analysis for complex decision-making across all domains. Use when user needs to evaluate multiple options with uncertain outcomes, assess risk/reward scenarios, or structure choices systematically. Applicable to business, investment, personal decisions, operations, career choices, product strategy, and any situation requiring structured evaluation. Triggers include decision tree, should I, what if, evaluate options, compare alternatives, risk analysis.

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Requirements

Target platform
OpenClaw
Install method
Manual import
Extraction
Extract archive
Prerequisites
OpenClaw
Primary doc
SKILL.md

Package facts

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Package format
ZIP package
Source platform
Tencent SkillHub
What's included
README.md, SKILL.md, scripts/decision_tree.py

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Release facts

Source
Tencent SkillHub
Verification
Indexed source record
Version
1.0.1

Documentation

ClawHub primary doc Primary doc: SKILL.md 29 sections Open source page

Decision Trees β€” Structured Decision-Making

Decision tree analysis: a visual tool for making decisions with probabilities and expected value.

When to Use

βœ… Good for: Business decisions (investments, hiring, product launches) Personal choices (career, relocation, purchases) Trading & investing (position sizing, entry/exit) Operational decisions (expansion, outsourcing) Any situation with measurable consequences ❌ Not suitable for: Decisions with true uncertainty (black swans) Fast tactical choices Purely emotional/ethical questions

Method

Decision tree = tree-like structure where: Decision nodes (squares) β€” your actions Chance nodes (circles) β€” random events End nodes (triangles) β€” final outcomes Process: Define options β€” all possible actions Define outcomes β€” what can happen after each action Estimate probabilities β€” how likely is each outcome (0-100%) Estimate values β€” utility/reward for each outcome (money, points, utility units) Calculate EV β€” expected value = Ξ£ (probability Γ— value) Choose β€” option with highest EV

Formula

EV = Ξ£ (probability_i Γ— value_i) Example: Outcome A: 70% probability, +$100 β†’ 0.7 Γ— 100 = $70 Outcome B: 30% probability, -$50 β†’ 0.3 Γ— (-50) = -$15 EV = $70 + (-$15) = $55

Classic Example (from Wikipedia)

Decision: Go to party or stay home?

Estimates:

Party: +9 utility (fun) Home: +3 utility (comfort) Carrying jacket unnecessarily: -2 utility Being cold: -10 utility Probability cold: 70% Probability warm: 30%

Tree:

Decision β”œβ”€ Go to party β”‚ β”œβ”€ Take jacket β”‚ β”‚ β”œβ”€ Cold (70%) β†’ 9 utility (party) β”‚ β”‚ └─ Warm (30%) β†’ 9 - 2 = 7 utility (carried unnecessarily) β”‚ β”‚ EV = 0.7 Γ— 9 + 0.3 Γ— 7 = 8.4 β”‚ └─ Don't take jacket β”‚ β”œβ”€ Cold (70%) β†’ 9 - 10 = -1 utility (froze) β”‚ └─ Warm (30%) β†’ 9 utility (perfect) β”‚ EV = 0.7 Γ— (-1) + 0.3 Γ— 9 = 2.0 └─ Stay home └─ EV = 3.0 (always) Conclusion: Go and take jacket (EV = 8.4) > stay home (EV = 3.0) > go without jacket (EV = 2.0)

Business Example

Decision: Launch new product?

Estimates:

Success probability: 40% Failure probability: 60% Profit if success: $500K Loss if failure: $200K Don't launch: $0

Tree:

Launch product β”œβ”€ Success (40%) β†’ +$500K └─ Failure (60%) β†’ -$200K EV = (0.4 Γ— 500K) + (0.6 Γ— -200K) = 200K - 120K = +$80K Don't launch └─ EV = $0 Conclusion: Launch (EV = +$80K) is better than not launching ($0).

Trading Example

Decision: Enter position or wait?

Estimates:

Probability of rise: 60% Probability of fall: 40% Position size: $1000 Target: +10% ($100 profit) Stop-loss: -5% ($50 loss)

Tree:

Enter position β”œβ”€ Rise (60%) β†’ +$100 └─ Fall (40%) β†’ -$50 EV = (0.6 Γ— 100) + (0.4 Γ— -50) = 60 - 20 = +$40 Wait └─ No position β†’ $0 EV = $0 Conclusion: Entering position has positive EV (+$40), better than waiting ($0).

Method Limitations

⚠️ Critical points: Subjective estimates β€” probabilities often "finger in the air" Doesn't account for risk appetite β€” ignores psychology (loss aversion) Simplified model β€” reality is more complex Unstable β€” small data changes can drastically alter the tree May be inaccurate β€” other methods exist that are more precise (random forests) But: The method is valuable for structuring thinking, even if numbers are approximate.

1. Structuring

Ask: What are the action options? What are possible outcomes? What are values/utility for each outcome? How do we measure value? (money, utility units, happiness points)

2. Probability Estimation

Help estimate through: Historical data (if available) Comparable situations Expert judgment (user experience) Subjective assessment (if no data)

3. Visualization

Draw tree in markdown: Decision β”œβ”€ Option A β”‚ β”œβ”€ Outcome A1 (X%) β†’ Value Y β”‚ └─ Outcome A2 (Z%) β†’ Value W └─ Option B └─ Outcome B1 (100%) β†’ Value V

4. EV Calculation

For each option: EV_A = (X% Γ— Y) + (Z% Γ— W) EV_B = V

5. Recommendation

Option with highest EV = best choice (rationally). But add context: Risk tolerance (can user handle worst case) Time horizon (when is result needed) Other factors (reputational risk, emotions, ethics)

Trading & Investing

Position Sizing: Options: 5%, 10%, 20% of capital Outcomes: Profit/loss with different probabilities Value: Absolute profit in $ Entry Timing: Options: Enter now, wait for -5%, wait for -10% Outcomes: Price goes up/down Value: Opportunity cost vs better entry price

Business Strategy

Product Launch: Options: Launch / don't launch Outcomes: Success / failure Value: Revenue, market share, costs Hiring Decision: Options: Hire candidate A / candidate B / don't hire Outcomes: Successful onboarding / quit after X months Value: Productivity, costs, opportunity cost

Personal Decisions

Career Change: Options: Stay / change job / start business Outcomes: Success / failure in new role Value: Salary, satisfaction, growth, risk Real Estate: Options: Buy house A / house B / continue renting Outcomes: Price increase / decrease / personal situation changes Value: Net worth, monthly costs, quality of life

Operations

Capacity Planning: Options: Expand production / outsource / status quo Outcomes: Demand increases / decreases Value: Profit, utilization, fixed costs Vendor Selection: Options: Vendor A / Vendor B / in-house Outcomes: Quality, reliability, failures Value: Total cost of ownership

Calculator Script

Use scripts/decision_tree.py for automated EV calculations: python3 scripts/decision_tree.py --interactive Or via JSON: python3 scripts/decision_tree.py --json tree.json JSON format: { "decision": "Launch product?", "options": [ { "name": "Launch", "outcomes": [ {"name": "Success", "probability": 0.4, "value": 500000}, {"name": "Failure", "probability": 0.6, "value": -200000} ] }, { "name": "Don't launch", "outcomes": [ {"name": "Status quo", "probability": 1.0, "value": 0} ] } ] } Output: πŸ“Š Decision Tree Analysis Decision: Launch product? Option 1: Launch └─ EV = $80,000.00 β”œβ”€ Success (40.0%) β†’ +$500,000.00 └─ Failure (60.0%) β†’ -$200,000.00 Option 2: Don't launch └─ EV = $0.00 └─ Status quo (100.0%) β†’ $0.00 βœ… Recommendation: Launch (EV: $80,000.00)

Final Checklist

Before giving recommendation, ensure: βœ… All options covered βœ… Probabilities sum to 100% for each branch βœ… Values are realistic (not fantasies) βœ… Worst case scenario is clear to user βœ… Risk/reward ratio is explicit βœ… Method limitations mentioned βœ… Qualitative context added (not just EV)

Method Advantages

βœ… Simple β€” people understand trees intuitively βœ… Visual β€” clear structure βœ… Works with little data β€” can use expert estimates βœ… White box β€” transparent logic βœ… Worst/best case β€” extreme scenarios visible βœ… Multiple decision-makers β€” can account for different interests

Method Disadvantages

❌ Unstable β€” small data changes β†’ large tree changes ❌ Inaccurate β€” often more precise methods exist ❌ Subjective β€” probability estimates "from the head" ❌ Complex β€” becomes unwieldy with many outcomes ❌ Doesn't account for risk preference β€” assumes risk neutrality

Important

The method is valuable for structuring thinking, but numbers are often taken from thin air. What matters more is the process β€” forcing yourself to think through all branches and explicitly evaluate consequences. Don't sell the decision as "scientifically proven" β€” it's just a framework for conscious choice.

Further Reading

Decision trees in operations research Influence diagrams (more compact for complex decisions) Utility functions (accounting for risk aversion) Monte Carlo simulation (for greater accuracy) Real options analysis (for strategic decisions)

Category context

Agent frameworks, memory systems, reasoning layers, and model-native orchestration.

Source: Tencent SkillHub

Largest current source with strong distribution and engagement signals.

Package contents

Included in package
2 Docs1 Scripts
  • SKILL.md Primary doc
  • README.md Docs
  • scripts/decision_tree.py Scripts

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