Fuzzy Logic

Fuzzy logic is a way of thinking that allows for degrees of truth rather than simply black-and-white answers like TRUE or FALSE when using Boolean logic (a.k.a. Boolean algebra).  Fuzzy logic is an approach to handling the "shades of gray" type situations that exist where information might be incomplete, vague, or have some level of subjectivity.

For example, in my subject matter of accountancy you have the notion of "audit risk".  The notion of audit risk is subjective.  Using fuzzy logic you can get a computer based system to provide useful information when it comes to working with information such as the notion of audit risk.

With fuzzy logic you have a set of tools that enable software to interact appropriately with information in a known, understandable way.  Using fuzzy logic you can have things like:

  • Fuzzy sets: Using normal set theory, membership in a set is black or white.  But with fuzzy sets you can have "degrees of membership" of a set.
  • Linguistic variables: Variables like temperature or speed that use words like "cold", "warm", and "hot" or maybe "fast" and "slow" rather than exact numbers.
  • Membership functions:  Rather than have one specific variable that defines membership in a category of thing; you can have a curve or function that shows how to what degree something belongs to some category.
  • Fuzzification: Turns crisp inputs like "72 degrees" into fuzzy values like "warm".
  • Defuzzification: Turns fuzzy outputs into precise actions like if the input is "warm" then take the precise action to set the fan to 60% power setting.
Again, fuzzy logic is a tool.  It is not appropriate in all situations.  Understanding how this tool works makes it so you can get useful results when using this tool in the right situations.

What I am going to do is create a mechanism to use fuzzy logic in XBRL.


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