neutrosophic probability

neutrosophic probability

Neutrosophic probability

is a

probability

in which

•

the chance that an event

𝔄

occurs is

T

;

•

the

indeterminate

chance (i.e. neither occurring nor not-occurring, unknown chance) is

I

;

•

and the chance that the event does not occur is

F

;

where

T

,

I

,

F

are standard or

non-standard real

subsets of the non-standard unit interval

]

-

0

,

1

+

[

.

We note the neutrosophic probability

N

⁢

P

⁢

(

𝔄

)

=

(

T

,

I

,

F

)

, and

T

,

I

,

F

are called

neutrosophic probabilistic

components

.

Now let’s explain the previous notations:

A number

ε

is said to be

infinitesimal

if and only if for all positive integers

n

one has

|

ε

|

<

1

n

. Let

ε

>

0

be a such infinitesimal number. The

hyper-real number set

is an

extension

of the

real number

set, which includes classes of

infinite

numbers and classes of infinitesimal numbers.

Generally, for any real number

a

one defines

a

-

which signifies a

monad

, i.e. a set of hyper-real numbers in

non-standard analysis

, as follows:

a

-

=

{

a

-

ε

:

ε

∈

R

*

,

ε

is infinitesimal

}

,

and similarly one defines

a

+

, which is also a monad, as:

a

+

=

{

a

+

ε

:

ε

∈

R

*

,

ε

is infinitesimal

}

.

A

binad

a

+

-

is a union of the above two monads, i.e.

a

+

-

=

-

a

∪

a

+

.

For example: The non-standard finite number

1

+

=

1

+

ε

, where

1

is its

standard part

and

ε

its

non-standard part

, and similarly the non-standard finite number

0

-

=

0

-

ε

, where

0

is its standard part and

ε

its

non-standard part

.

Similarly for

3

+

=

3

+

ε

, etc.

Note that

]

-

0

,

1

+

[

is called the

non-standard unit interval

.

More information on hyperreal

intervals

http://www.gallup.unm.edu/

smarandache

/

Introduction

.pdf

is available.

The superior sum of the neutrosophic components is defined as

n

s

⁢

u

⁢

p

=

s

u

p

(

T

)

+

s

u

p

(

I

)

+

s

u

p

(

F

)

∈

]

-

0

,

3

+

[

which may be as high as 3 or

3

+

.

While the inferior sum of the neutrosophic components is defined as

n

i

⁢

n

⁢

f

=

i

n

f

(

T

)

+

i

n

f

(

I

)

+

i

n

f

(

F

)

∈

]

-

0

,

3

+

[

which may be as low as 0 or

0

-

.

Neutrosophic probability was introduced by Florentin Smarandache in 1995 and this entry contains excerpts from his below book which is for free online.

NP is a

generalization

of (a)

classical probability

, i.e. when the set

I

is empty and the other two probabilistic components are each

reduced

to a crisp number:

t

∈

T

and it is understood that

f

∈

F

such that

t

+

f

=

1

, and (b)

imprecise probability

, i.e. when the set

I

is also empty, but

T

⊂

[

0

,

1

]

is a subset with an

upper bound

and a

lower bound

, not a crisp number

p

∈

[

0

,

1

]

, while

F

is understood to be the opposite of

T

.

Unlike the classical probability and imprecise probability, which both use the standard unit interval

[

0

,

1

]

, neutrosophic probability uses the non-standard unit interval

]

-

0

,

1

+

[

in order to make a distinction between

absolutely sure event

, which is an event that

occurs in

all possible worlds, and

relatively sure event

, which is an event that occurs in at least one world. Similarly for

absolutely unsure event

and

relatively unsure event

, or

absolutely indeterminate event

and

relatively indeterminate event

.

The

universal set

, endowed with a neutrosophic probability defined for each of its subset, forms a

neutrosophic probability space

.

Four examples

:

a) From a pool of refugees, waiting in a political refugee camp in Turkey to get the American visa,

a

%

have the chance to be accepted - where

a

varies in the set

A

,

r

%

to be rejected - where

r

varies in the set

R

, and

p

%

to be in pending (not yet decided) - where

p

varies in

P

, with the sets

A

,

R

,

P

⊂

[

0

,

1

]

. In technical applications, where there is no need for distinctions between absolutely sure event and relatively sure event, we can use standard subsets instead of non-standard subsets and respectively the unit interval

[

0

,

1

]

instead of the non-standard unit interval

]

-

0

,

1

+

[

.

b) The probability that candidate

C

will win an election is say 0.25-0.30 true (

percent

of people voting for him), 0.35 false (percent of people voting against him), and 0.40 or 0.41 indeterminate (percent of people not coming to the ballot box, or giving a blank vote i.e. not selecting anyone, or giving a

negative

vote i.e. cutting all candidates on the list).

c) The probability that tomorrow it will rain is say 0.50-0.54 true according to meteorologists who have investigated the past years’ weather, 0.30 or 0.34-0.35 false according to today’s very sunny and droughty summer, and 0.10 or 0.20 undecided (indeterminate) because of some unexpected and unknown

parameters

.

d) The probability that Yankees will win tomorrow versus Cowboys is 0.60 true (according to their confrontation’s history giving Yankees’

satisfaction

), 0.30-0.32 false (supposing Cowboys are actually up to the mark, while Yankees are declining), and 0.10 or 0.11 or 0.12 indeterminate (left to the hazard: sickness of players, referee’s mistakes, atmospheric conditions during the game). These parameters act on players’ psychology.

Remarks

:

- Neutrosophic probability are useful to those events which involve some degree of indeterminacy (unknown) and more criteria of evaluation as above. This kind of probability is necessary because it provides a better approach than classical probability to uncertain events.

- This probability uses a subset-approximation for the truth-value (like imprecise probability), but also subset-approximations for indeterminacy- and falsity-values.

- In the case when the truth and false components are complementary, i.e. no indeterminacy exists and the sum of the neutrosophic components is 1, one falls to the classical probability. As, for example, tossing dice or coins, or drawing cards from a well shuffled deck, or drawing balls from an urn.

References

1

S. Bhattacharya,

Utility, Rationality and Beyond, from Behavioral Finance to Informational Finance

(using Neutrosophic Probability), Ph. D. Dissertation, Bond University, Australia, 2004.

2

F. Smarandache,

A Unifying Field in Logics:

Neutrosophic Logic

. Neutrosophy,

Neutrosophic Set

, Neutrosophic Probability and

Statistics

, third edition, Xiquan, Phoenix, 2003.

http://www.gallup.unm.edu/ smarandache/eBook-Neutrosophics2.pdf

Also online.

3

F. Smarandache,

An Introduction to the Neutrosophic Probability Applied in Quantum Physics

, in

Bulletin of Pure and Applied Sciences

, Physics, 13-25, Vol. 22D, No. 1, 2003.

Title

neutrosophic probability

Canonical

name

NeutrosophicProbability

Date of creation

2013-03-22 15:23:22

Last modified on

2013-03-22 15:23:22

Owner

para0doxa (5174)

Last modified by

para0doxa (5174)

Numerical id

8

Author

para0doxa (5174)

Entry type

Definition

Classification

msc 60A10

Defines

neutrosophic probablistic components

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