Measuring Complexity using Information
Опубликована: Июнь 13, 2024
Measuring
complexity
in
multidimensional
systems
with
high
degrees
of
freedom
and
a
variety
types
information,
remains
an
important
challenge.
Complexity
system
is
related
to
the
number
components,
type
interactions
among
them,
degree
redundancy,
system.
Examples
show
that
different
disciplines
science
converge
measures
for
low
dimensional
problems.
For
systems,
such
as
coded
strings
symbols
(text,
computer
code,
DNA,
RNA,
proteins,
music),
Shannon’s
Information
Entropy
(expected
amount
_information_
event
drawn
from
given
distribution)
Kolmogorov‘s
Algorithmic
(the
length
shortest
algorithm
produces
object
output),
are
used
quantitative
measurements
complexity.
more
dimensions
(ecosystems,
brains,
social
groupings),
network
provides
better
tools
purpose.
complex
highly
none
former
methods
useful.
Useful
Φ,
proposed
by
Infodynamics,
can
be
It
quantified
measuring
thermodynamic
Free
Energy
F
and/or
useful
Work
it
produces.
measured
Total
I,
then
defined
information
system,
includes
useless
or
Noise
N,
Redundant
R.
one
these
variables
allows
quantifying
classifying
Язык: Английский
Measuring Complexity using Information
Опубликована: Июнь 24, 2024
Measuring
complexity
in
multidimensional
systems
with
high
degrees
of
freedom
and
a
variety
types
information,
remains
an
important
challenge.
Complexity
system
is
related
to
the
number
components,
type
interactions
among
them,
degree
redundancy,
system.
Examples
show
that
different
disciplines
science
converge
measures
for
low
dimensional
problems.
For
systems,
such
as
coded
strings
symbols
(text,
computer
code,
DNA,
RNA,
proteins,
music),
Shannon’s
Information
Entropy
(expected
amount
_information_
event
drawn
from
given
distribution)
Kolmogorov‘s
Algorithmic
(the
length
shortest
algorithm
produces
object
output),
are
used
quantitative
measurements
complexity.
more
dimensions
(ecosystems,
brains,
social
groupings),
network
provides
better
tools
purpose.
complex
highly
none
former
methods
useful.
Useful
Φ
(Information
thermodynamic
free
energy)
can
be
quantified
by
measuring
Free
Energy
F
and/or
useful
Work
it
produces.
Here
I
propose
measure
Total
I,
defined
information
system,
including
Φ,
useless
or
Noise
N,
Redundant
R.
one
these
variables
allows
quantifying
classifying
two
windows
overlooking
same
fundamental
phenomenon
broadening
out
quantify
both.
Язык: Английский
Measuring Complexity using Information
Опубликована: Июль 3, 2024
Measuring
complexity
in
multidimensional
systems
with
high
degrees
of
freedom
and
a
variety
types
information,
remains
an
important
challenge.
The
system
is
related
to
the
number
components,
type
interactions
among
them,
degree
redundancy,
system.
Examples
show
that
different
disciplines
science
converge
measures
for
low
dimensional
problems.
For
systems,
such
as
coded
strings
symbols
(text,
computer
code,
DNA,
RNA,
proteins,
music),
Shannon’s
Information
Entropy
(expected
amount
information
event
drawn
from
given
distribution)
Kolmogorov‘s
Algorithmic
Complexity
(the
length
shortest
algorithm
produces
object
output),
are
used
quantitative
measurements
complexity.
more
dimensions
(ecosystems,
brains,
social
groupings),
network
provides
better
tools
purpose.
highly
complex
none
former
methods
useful.
Here,
can
be
ranging
subatomic
ecological,
social,
mental
AI.
Useful
Φ
(Information
thermodynamic
free
energy)
quantified
by
measuring
Free
Energy
and/or
useful
Work
it
produces.
measured
Total
I
system,
includes
Φ,
useless
or
Noise
N,
Redundant
R.
one
these
variables
allows
quantifying
classifying
two
windows
overlooking
same
fundamental
phenomenon,
broadening
out
explore
deep
structural
dynamics
nature
at
all
levels
complexity,
including
natural
artificial
intelligence.
Язык: Английский
Measuring Complexity using Information
Опубликована: Июль 16, 2024
Measuring
complexity
in
multidimensional
systems
with
high
degrees
of
freedom
and
a
variety
types
information,
remains
an
important
challenge.
The
system
is
related
to
the
number
components,
type
interactions
among
them,
degree
redundancy,
system.
Examples
show
that
different
disciplines
science
converge
measures
for
low
dimensional
problems.
For
systems,
such
as
coded
strings
symbols
(text,
computer
code,
DNA,
RNA,
proteins,
music),
Shannon’s
Information
Entropy
(expected
amount
information
event
drawn
from
given
distribution)
Kolmogorov‘s
Algorithmic
Complexity
(the
length
shortest
algorithm
produces
object
output),
are
used
quantitative
measurements
complexity.
more
dimensions
(ecosystems,
brains,
social
groupings),
network
provides
better
tools
purpose.
highly
complex
none
former
methods
useful.
Here,
can
be
ranging
subatomic
ecological,
social,
mental
AI.
Useful
Φ
(Information
thermodynamic
free
energy)
quantified
by
measuring
Free
Energy
and/or
useful
Work
it
produces.
measured
Total
I
system,
includes
Φ,
useless
or
Noise
N,
Redundant
R.
one
these
variables
allows
quantifying
classifying
two
windows
overlooking
same
fundamental
phenomenon,
broadening
out
explore
deep
structural
dynamics
nature
at
all
levels
complexity,
including
natural
artificial
intelligence.
Язык: Английский
Measuring Complexity using Information
Опубликована: Июль 22, 2024
Measuring
complexity
in
multidimensional
systems
with
high
degrees
of
freedom
and
a
variety
types
information,
remains
an
important
challenge.
The
system
is
related
to
the
number
components,
type
interactions
among
them,
degree
redundancy,
system.
Examples
show
that
different
disciplines
science
converge
measures
for
low
dimensional
problems.
For
systems,
such
as
coded
strings
symbols
(text,
computer
code,
DNA,
RNA,
proteins,
music),
Shannon’s
Information
Entropy
(expected
amount
information
event
drawn
from
given
distribution)
Kolmogorov‘s
Algorithmic
Complexity
(the
length
shortest
algorithm
produces
object
output),
are
used
quantitative
measurements
complexity.
more
dimensions
(ecosystems,
brains,
social
groupings),
network
provides
better
tools
purpose.
highly
complex
none
former
methods
useful.
Here,
can
be
ranging
subatomic
ecological,
social,
mental
AI.
Useful
Φ
(Information
thermodynamic
free
energy)
quantified
by
measuring
Free
Energy
and/or
useful
Work
it
produces.
measured
Total
I
system,
includes
Φ,
useless
or
Noise
N,
Redundant
R.
one
these
variables
allows
quantifying
classifying
two
windows
overlooking
same
fundamental
phenomenon,
broadening
out
explore
deep
structural
dynamics
nature
at
all
levels
complexity,
including
natural
artificial
intelligence.
Язык: Английский
Measuring Complexity using Information
Опубликована: Июль 26, 2024
Measuring
complexity
in
multidimensional
systems
with
high
degrees
of
freedom
and
a
variety
types
information,
remains
an
important
challenge.
The
system
is
related
to
the
number
components,
type
interactions
among
them,
degree
redundancy,
system.
Examples
show
that
different
disciplines
science
converge
measures
for
low
dimensional
problems.
For
systems,
such
as
coded
strings
symbols
(text,
computer
code,
DNA,
RNA,
proteins,
music),
Shannon’s
Information
Entropy
(expected
amount
information
event
drawn
from
given
distribution)
Kolmogorov‘s
Algorithmic
Complexity
(the
length
shortest
algorithm
produces
object
output),
are
used
quantitative
measurements
complexity.
more
dimensions
(ecosystems,
brains,
social
groupings),
network
provides
better
tools
purpose.
highly
complex
none
former
methods
useful.
Here,
can
be
ranging
subatomic
ecological,
social,
mental
AI.
Useful
Φ
(Information
thermodynamic
free
energy)
quantified
by
measuring
Free
Energy
and/or
useful
Work
it
produces.
measured
Total
I
system,
includes
Φ,
useless
or
Noise
N,
Redundant
R.
one
these
variables
allows
quantifying
classifying
two
windows
overlooking
same
fundamental
phenomenon,
broadening
out
explore
deep
structural
dynamics
nature
at
all
levels
complexity,
including
natural
artificial
intelligence.
Язык: Английский