# Decision Networks

> Models complex probabilistic decisions with utility nodes using Influence Diagrams and Decision Networks.

- Skill: `felipecustodio/decision-networks` (Agent Skill, multi-file: 3 files)
- Install (CLI): `npx skillmds@latest add felipecustodio/decision-networks`
- Raw SKILL.md: https://api.skillmd.com/api/skills/felipecustodio/decision-networks/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- License: MIT
- Author: felipecustodio (https://skillmd.com/u/felipecustodio)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/felipecustodio/decision-networks

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## Overview & Purpose

Decision Networks extend Bayesian networks by incorporating decision nodes and utility nodes. They help agents compute expected utilities of decisions under uncertain environment states.

## When to Use

- **Decisions under Uncertainty**: When actions have uncertain outcomes with distinct financial/operational utilities.
- **Risk vs Reward Optimization**: Balancing high-risk/high-reward paths against safe/low-reward paths.

## Execution Workflow

1. **Define Chance Nodes**: Uncertain state variables in the environment.
2. **Define Decision Nodes**: Specific choices under agent control.
3. **Define Utility Nodes**: Quantifiable payoff or loss functions.
4. **Compute Expected Utility (EU)**: $\text{EU}(D) = \sum P(S|D) \times U(S, D)$.
5. **Select Maximum Utility Action**: Choose decision $D^*$ maximizing $\text{EU}(D)$.

## Expected Output Contract

```markdown
### Decision Network Analysis
- **Decision Nodes**: [Available Choices]
- **Chance Nodes**: [Uncertain State Probabilities]
- **Expected Utility**:
  - `Choice A`: Expected Utility = X
  - `Choice B`: Expected Utility = Y
- **Optimal Choice**: [Choice with Max EU]
```

## Scripts

- `scripts/decision_networks.py` - Deterministic evaluation, state validation, and CLI tool for decision-networks.