Fuzzy logic, an antidote
to mental myopia


Things are rarely fully black or right and fully clear, as we live in
uncertain, unstable, always moving "complex dynamical systems"

Fuzzy logic is a practical form of reasoning that is non binary but gradual,
used to tackle such situations in which the cursor cannot be positioned
too precisely and definitively between two extremes.

It is also a system regulation tool.

Moving the reasoning cursor tentatively
Fuzzy logic is a practical form of reasoning that is gradual gradual
and
masks non binary.
It avoids the trap of seeing things as just true or false.
It is an antidote to
exclusive / unbalanced / myopic /
caricatural
representations (
stereotypes), definitions,
categorizations and judgements
(see representativeness
heuristic)

In real life, most statements have to be considered to represent a
variable degree / shade of truth and untruth
, let us say
somewhere between 1% and 99%.
Fuzzy logic helps to discern- or at least estimate - to what extent
something is true (or to what extent it is not) and to adapt one's
reasoning and behavior to complex and unstable, not
clear-cut situations
.

In a more mundane way, it is also a practical regulation tool.
We use it more or less consciously when handling
our shower
lever between hot and cold. Fuzzy robotics !
More explicitly, FL is quite helpful for analysis and decision making
in complexcomplex, dynamicalevolving, fog uncertain and imprecise cases 
in which probabilities (and often the current reality) cannot be well
known

This applies to many human, social and even physical situations
in which we have to make anticipations so as to take
decisions
.

To sum it up, to most real life situations.
Yes, the world is fuzzy, complex and evolving (as a "complex
dynamical system"), we live in fuzziness, better understand it
!

Non binary reasoning

Fuzzy logic:
  • Avoids the binary logic / false dilemma / thinking myopia
by which things are supposed to:
    • be either black or white, right or wrong. Sorry, Aristotle!
    • obey the "one cause / one effect" dogma. Sorry, Descartes!
  • Takes into account that such clear cut statements do not fit

what is often found in our complex and evolving world in which
many things are largely:

    • Uncertain (lack of reliable mathematical diceprobabilities),
    • Muddled, mixed up
(no single cause but a convergence of circumstances)
    • Imprecise, approximative
(a range of numbers more than a precise number)
    • And ambiguous (appearances do not tell all what is behind)
Let us look at an apple from which a piece has been bitten off.
Can it still be defined as:

An apple? A non apple?
A near-apple? A near non-apple?

But two-faced?

FL has also some similarity with the yinyang Yin-Yang philosophical
principles.
Those two principles, in real life:
  • Oppose each other.
And at he same time
  • Coexist, complement, combine each other to make a whole.
Also quantic mathematics do not work in a binary 0 - 1 logic that computer use but
admits a superposition of opposite cases (like in the Shrödinger metaphor of a cat that
is at the same timeis present or vanished..

Gradual

Little by little

FL tries to find gradually and iteratively (= via trials and errors) an
approximative  dialmetercursor position between the two extremes.

Those extremes can be, for example:
  • 99% true / 99% false,
  • Ultra-cheap / ultra-expensive,
  • Ultra-safe / ultra-risky
  • And so on...
Fuzzy logic might be therefore labeled "the logic of vague probabilities".

A classic example
- already mentioned, I know - is when trying too find
the right
water tap position between scalding and freezing with intermediate
ones such as
rather hot, rather cold and nicely warm.
Here FL is used as a system regulation tool.

Adapted to evolutive situations

Fuzzy logic was devised first to handle situations in which absolute
precision or certainty is hard to find, and even illusory,
as things
are always moving in a more or less unpredictable way.

This is often the case in dynamical (= evolving) systems,
and the World is a place where we meet them everyday.

Its tentative / iterative aspect has some similarity, when decision-
making is at stake, with "Bayesian  probabilities"
by which
hypothetical odds are adjusted every time an event (or lack of
event) :
  • Changes the situation, or at least complement its knowledge.
  • Or meets or fails some conditions (conditional probabilities)
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M.a.j. / updated : 17 July  2015
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