Examples
Examples
Try the starting example
- Positive reciprocal comparison matrix
- 1,2 0.5,1
- Row-geometric priority weights
- 0.666666666667, 0.333333333333
- Approximate consistency index
- 0
- Largest log-ratio disagreement
- 0
- Average implied eigenvalue
- 2
Try another scenario
- Positive reciprocal comparison matrix
- 1,3 0.3333333333333333,1
- Row-geometric priority weights
- 0.75, 0.25
- Approximate consistency index
- 0
- Largest log-ratio disagreement
- 0
- Average implied eigenvalue
- 2
Calculation method
Calculation method
Row-geometric priority weights
Take exp(mean(log(row entries))) and normalise across rows, scaling the exponentials for stability.
Approximate consistency index
For n>1, max(0,(average((A×w)i/wi)−n)/(n−1)); this uses geometric weights and is an approximation.
Largest log-ratio disagreement
Maximum |log(Aij)−log(wi/wj)|.
Average implied eigenvalue
Average component ratio (A×w)i/wi.
Symbols and units
- a
- Positive reciprocal comparison matrix
Use the units shown. A list keeps the order in which you enter its values. The symbol ^ means “raised to the power of”.
Assumptions & limits
Assumptions & limits
- A geometric-mean priority method for 1–10 reciprocal criteria. Diagnostics expose inconsistency; this is not an exact principal-eigenvector AHP calculation and no universal consistency-ratio threshold is assumed.
Are these inputs private?
These calculations run in your browser. A shared link contains the values you choose to share. Only questions submitted to the separate AI finder are sent to OpenAI.
Further reading
UK Government Analysis Function: multi-criteria analysis ↗
Background reading; this does not imply endorsement or independent review of SumFizz.