We live in the era of the Fuzzy Logic Generation.

Throughout our educations in school we learned the mechanics of Boolean Algebra. It is the easiest of algebra to teach because it is linear: That is, with a few simple rules, one can logically see progressions that can easily be spotted and built upon: Two comes after one, positive numbers are greater in value than negative numbers, and odd numbers and even numbers are two completely separate things. Things generally make sense and the world is in order.

While Boolean Algebra is pretty great and wonderful, the real world can often not be described in such linear, or even binary terms. Enter fuzzy logic. In the real world where values don’t necessarily have a binary range of “true” or “false,” or “one” or “zero,” This form of Non-Boolean algebra can help in decision making with imprecise data.

With the background of a computer programmer, I can look at the world and give it a vast series of conditional formulas and craft it into a working model. However, the amount of formulas grows increasingly complex; while this isn’t impossible, the mandate of the wise engineer is to simply things as thoroughly as possible. When such a system hits the “real world,” unnecessary complexity tends to make things, well, more complex than they need to be. Fuzzy logic is great for being able to eliminate much of that complexity, solving problems with expert and realtime systems reacting in an imperfect environment that can be highly variable, unpredictable, and volatile.

In 1964 Lofti Zadeh, a former chairman of the electrical engineering and computer science department over at the University of California at Berkeley, was programming software to solve the handwriting recognition issue. Since traditional set theory didn’t work for him with the binary approach—“off or on—“that it applies, Dr. Zadeh needed something that applied more a “matter of degree” approach rather than the alternative.

From a page that describes Fuzzy Logic:

“** Fuzzy logic manipulates such vague concepts as "warm" or "still dirty" and so helps engineers to build air conditioners, washing machines and other devices that judge how fast they should operate or shift from one setting to another even when the criteria for making those changes are hard to define. When mathematicians lack specific algorithms that dictate how a system should respond to inputs, fuzzy logic can control or describe the system by using "commonsense" rules that refer to indefinite quantities. No known mathematical model can back up a truck-and-trailer rig from a parking lot to a loading dock when the vehicle starts from a random spot. Both humans and fuzzy systems can perform this nonlinear guidance task by using practical but imprecise rules such as "If the trailer turns a little to the left, then turn it a little to the right." Fuzzy systems often glean their rules from experts. When no expert gives the rules, adaptive fuzzy systems learn the rules by observing how people regulate real systems.**”

Short of being a mathematician, what is the point of Fuzzy Logic?

Try having a conversation with anyone under the age of 30; certainly anyone under the age of 20: They have grammars and use sentence structures which are laced with “like,” “kinda,” and “sorta.” How difficult is it to get someone to give you a straight, firm answer about anything?

Fuzzy logic doesn’t dictate a world that belongs or doesn’t belong; in other words, it doesn’t dictate bivalent sets: Cannot belong to both a set and its complement set or to neither of the sets—preserving logic to avoid any contradiction that a number can and cannot simultaneously be a part of multiple sets. Instead, the multivalent sets of fuzzy logic break these laws to some degree. According to fuzzy logic, the number 5 (for instance) can belong to both “odd” and “even” number sets. Imagine an air conditioner: While you may consider the air coming from it to feel “cool,” another person might consider it “just right.” The air coming out of the air conditioner can be measured as belonging to multiple sets. The boundaries of standard sets, those able to be manipulated by classic algebra, are exact while those of fuzzy logic, Non-Boolean Algebra are curved and can taper off, creating partial contradictions: The air coming from that air conditioner can be 25 percent cool and 75 percent not cool at the same time.

I’ve long held to this belief: While many, many things in the world can be described in black and white, it is things like human emotion that add color to our worlds. If you can cut past all of that, you might very well be able to simplify the situation.