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 — some inputs we can observe, some are latent variables we can’t observe — circles
- action nodes: what we have control over — squares
- utility nodes: output, what the results would be; we typically sum utilities together if you have multiple of them — 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: