Examples
Examples
Try the starting example
- JSON Risk entries [ID,user likelihood score,user impact score,user exposure weight,treatment cost currency] (USD)
- [["A",3,4,1,200],["B",2,5,1.2,300]]
- Likelihood sensitivity multiplier
- 1
- Risk ranking under supplied scores
- [["A",12],["B",12]]
- Treatment budget (USD)
- $500.00
- Total weighted score
- 24
Try another scenario
- JSON Risk entries [ID,user likelihood score,user impact score,user exposure weight,treatment cost currency] (USD)
- [["A",3,4,1,200],["B",2,5,1.2,300]]
- Likelihood sensitivity multiplier
- 1.2
- Risk ranking under supplied scores
- [["A",14.4],["B",14.4]]
- Treatment budget (USD)
- $500.00
- Total weighted score
- 28.8
Calculation method
Calculation method
Risk ranking under supplied scores
J(sorted([[r[0],r[1]*b*r[2]*r[3]] for r in a],key=lambda r:(-r[1],r[0])))
Treatment budget (USD)
sum(r[4] for r in a)
Total weighted score
sum(r[1]*b*r[2]*r[3] for r in a)
Symbols and units
- a
- JSON Risk entries [ID,user likelihood score,user impact score,user exposure weight,treatment cost currency] (USD)
- b
- Likelihood sensitivity multiplier
Use the units shown. A list keeps the order in which you enter its values. The symbol ^ means “raised to the power of”.
Calculation steps and notation
sum(list) adds all entries in the list.
Assumptions & limits
Assumptions & limits
- Ordinal scores multiplied only as an explicit user convention; no probability, expected loss or security certification implied. Supported limits: All numeric inputs must be finite. Only the explicitly described mathematical scenario is supported.
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.