---
title: "decision network"
type: concept
related: [Decision Networks, Baysian Network, Factor, Utility Theory, Probability]
source: https://www.jemoka.com/posts/kbhdecision_networks/
confidence: high
status: active
---

A decision network is a Baysian Network which is used to make decisions based on optimizing utility.
To solve a problem, we iterate through all possible decision parameters to find the one that maximizes utility.
Nodes chance nodes: random variables &mdash; some inputs we can observe, some are latent variables we can&rsquo;t observe &mdash; circles action nodes: what we have control over &mdash; squares utility nodes: output, what the results would be; we typically sum utilities together if you have multiple of them &mdash; diamonds Edges conditional edge - arrows to chance nodes: conditional probability edges informational edge - arrows to action nodes: this information is used to inform choice of action functional edge - arrows to utility nodes: computes how the action affects the world Example For \(U\), for instance, you can have a factor that loks ilke:
