decision-trees

# Decision Trees — Structured Decision-Making

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

## When to Use

✅ **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) — your actions
- **Chance nodes** (circles) — random events
- **End nodes** (triangles) — final outcomes

**Process:**
1. **Define options** — all possible actions
2. **Define outcomes** — what can happen after each action
3. **Estimate probabilities** — how likely is each outcome (0-100%)
4. **Estimate values** — utility/reward for each outcome (money, points, utility units)
5. **Calculate EV** — expected value = Σ (probability × value)
6. **Choose** — option with highest EV

## Formula

```
EV = Σ (probability_i × value_i)
```

**Example:**
- Outcome A: 70% probability, +$100 → 0.7 × 100 = $70
- Outcome B: 30% probability, -$50 → 0.3 × (-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
├─ Go to party
│  ├─ Take jacket
│  │  ├─ Cold (70%) → 9 utility (party)
│  │  └─ Warm (30%) → 9 - 2 = 7 utility (carried unnecessarily)
│  │  EV = 0.7 × 9 + 0.3 × 7 = 8.4
│  └─ Don't take jacket
│     ├─ Cold (70%) → 9 - 10 = -1 utility (froze)
│     └─ Warm (30%) → 9 utility (perfect)
│     EV = 0.7 × (-1) + 0.3 × 9 = 2.0
└─ Stay home
   └─ EV = 3.0 (al...

# Decision Trees Skill

Decision tree analysis for complex decision-making across all domains.

## 📊 What It Does

Helps you make structured decisions using decision tree analysis with expected value (EV) calculations. Works for any situation where you need to evaluate multiple options with uncertain outcomes.

## ✨ Features

- **Universal application**: business, investing, personal decisions, operations, career choices
- **Expected value calculation**: quantify risk/reward scenarios
- **Visual tree structure**: see all options and outcomes clearly
- **Python calculator**: automate EV calculations (interactive + JSON)
- **Examples across domains**: trading, business strategy, personal life, operations

## 🎯 Use Cases

### Trading & Investing
- Position sizing (how much capital to allocate)
- Entry timing (buy now or wait)
- Exit strategy (take profit or hold)

### Business Strategy
- Product launch decisions
- Hiring choices
- Capacity planning
- Vendor selection

### Personal Decisions
- Career changes
- Real estate purchases
- Major life decisions

### Operations
- Outsourcing vs in-house
- Expansion timing
- Resource allocation

## 🚀 How It Works

1. **Define options** — all possible actions
2. **Define outcomes** — what can happen after each action
3. **Estimate probabilities** — how likely is each outcome (0-100%)
4. **Estimate values** — utility/reward for each outcome (money, points, utility units)
5. **Calculate EV** — expected value = Σ (probability × value)
6. **Choose** — option with highest EV (with qualitative context)

## 📝 Example

**Decision:** Launch new product?

**Options:**
- Launch (40% success → +$500K, 60% failure → -$200K)
- Don't launch (100% → $0)

**Calculation:**
```
Launch EV = (0.4 × $500K) + (0.6 × -$200K) = $80K
Don't launch EV = $0

✅ Recommendation: Launch (EV: $80K)
```

## 🛠️ Python Calculator

The skill includes a Python script for automated EV calculations:

### Interactive mode:
```bash
python3 scripts/decision_tree.py -...