Occam's Razor
one should not increase, beyond what is necessary, the number of
entities required to explain anything
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Occam's razor is a logical principle attributed to the mediaeval philosopher
William of Occam (or Ockham). The principle states that one should not make
more assumptions than the minimum needed. This principle is often called the
principle of parsimony. It underlies all scientific modelling and theory
building. It admonishes us to choose from a set of otherwise equivalent
models of a given phenomenon the simplest one. In any given model, Occam's
razor helps us to "shave off" those concepts, variables or constructs that
are not really needed to explain the phenomenon. By doing that, developing
the model will become much easier, and there is less chance of introducing
inconsistencies, ambiguities and redundancies.
Though the principle may seem rather trivial, it is essential for model
building because of what is known as the "underdetermination of theories by
data". For a given set of observations or data, there is always an infinite
number of possible models explaining those same data. This is because a
model normally represents an infinite number of possible cases, of which the
observed cases are only a finite subset. The non-observed cases are inferred
by postulating general rules covering both actual and potential
observations.
For example, through two data points in a diagram you can always draw a
straight line, and induce that all further observations will lie on that
line. However, you could also draw an infinite variety of the most
complicated curves passing through those same two points, and these curves
would fit the empirical data just as well. Only Occam's razor would in this
case guide you in choosing the "straight" (i.e. linear) relation as best
candidate model. A similar reasoning can be made for n data points lying in
any kind of distribution.
Occam's razor is especially important for universal models such as the ones
developed in General Systems Theory, mathematics or philosophy, because
there the subject domain is of an unlimited complexity. If one starts with
too complicated foundations for a theory that potentially encompasses the
universe, the chances of getting any manageable model are very slim indeed.
Moreover, the principle is sometimes the only remaining guideline when
entering domains of such a high level of abstraction that no concrete tests
or observations can decide between rival models. In mathematical modelling
of systems, the principle can be made more concrete in the form of the
principle of uncertainty maximization: from your data, induce that model
which minimizes the number of additional assumptions.
This principle is part of epistemology, and can be motivated by the
requirement of maximal simplicity of cognitive models. However, its
significance might be extended to metaphysics if it is interpreted as saying
that simpler models are more likely to be correct than complex ones, in
other words, that "nature" prefers simplicity.
Bob Kramer
rkramer3@austin.rr.com
----- Original Message -----
From: "Hugh Barber" <tr6nut@sbcglobal.net>
To: "Henry Frye" <henry@henryfrye.com>
Cc: "Jack W. Drews" <vinttr4@geneseo.net>; <fot@autox.team.net>
Sent: Sunday, April 09, 2006 9:03 AM
Subject: Re: [FOT] ignition problem
> Is Henry becoming FOT's Occam ?
>
> /entia non sunt multiplicanda praeter necessitatem
>
> /...like Henry Frye, my hero of last weekend says, "simple is good". Or
> something like that.
>
>
> === Help keep Team.Net on the air
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