1. Weights and scores characterize subjective assessments as discrete values.

2. If multiple criteria are not independent you end up inadvertently overweighting.

3. Because the overall, weighted score is a discrete value a lot of information is lost.

I've discovered that a network of Noisy Adder nodes provides a better alternative. The attached network contains ten

*Criteria*nodes (chance nodes). Their parents are Noisy Adder nodes (

*Assessment*). I split the parents into two nodes (Assessment_1 and Assessment_2); ten parents is too many for a Noisy Adder node with eight states. In fact, seven parents is probably too much. The two parents have a single

*Assessment*parent which aggregates the overall score. The Noisy Adder nodes all have weights for each of the

*Criteria*nodes.

The states are: Negative; NegativeNeutral; NeutralNegative; Neutral; NeutralPositive; PositiveNeutral; Positive; and Disregard. The Disregard state nullifies the influence of the child node; a nice feature.

The overall Assessment node aggregates scores based on weights in the Assessment nodes. Since the top level node has to intermediate parents you have to multiply the weights to get the overall weight. If you set the top level

*Assessment*node as a Target you can use the

*Sensitivity Analysis*feature to see, graphically, the influence of the

*Criteria*nodes.

I added an equation node called

*Distribution*as a parent to the overall

*Assessment*node. (If you want to do a

*Sensitivity Analysis*you have to delete the

*Distribution*node.)

The

*Distribution*equation node will give you the mean and standard deviation of the

*Assessment*score. Rather than having an over-simplified discrete score GeNie gives you a distribution. You might prefer an alternative with a slightly lower score that has a tighter distribution.