Quantification of Training in Human Thought using Chaos Theory:

Degree of Decreasing Entropy as an Indicator

HIDEAKI YANAGISAWA

Gunma Plant, General Affairs Team,

Marelli Corporation,

132 Shin-nakano, Ora-cho, Ora-gun, Gunma 370-0612,

JAPAN

Abstract: - Many analytical methods used to quantify the technical training of human resources have been

reported, but they are poor at assessing the training of human thought. Chaos theory and the degree of

decreasing entropy have not yet been used in such analytics, although all phenomena—except for those in a

completely fixed state (e.g., mathematics, historical facts, and true natural laws) or a random state (e.g., dialing

a random digit)—are based on chaos theory. Chaos theory must therefore be considered in the quantification of

the technical and intellectual training of human resources because human thinking is involved.

This report compares a method for chaos theoretical quantification of human resource training to methods

that do not involve chaos theory. The development of the ability to change to a fixed state from a chaotic state

beyond the Feigenbaum point is the goal for the training in thinking; this process, which involves a decrease in

entropy, is the most difficult, and important process in such training. In a chaotic state with an infinite number

of solutions, humans can never show their own state using any method. In many reports, each goal in a fixed

state is considered after thinking has been rearranged; however, a chaotic state in human thinking can never be

expressed using indices such as productivity, efficiency, or job satisfaction. Indeed, many reported results are

merely a part of human resource training, but the change of entropy in the fixed state is small (this corresponds

to the “creativity” of artificial intelligence). By contrast, the change in entropy from a chaotic state to a fixed

state is large, and this corresponds to human intuitive. Considered in terms of chaos theory, goal achievement

for thinking must therefore be quantified in terms of the degree of decreasing entropy (i.e., the problem-solving

speed, and degree of problem difficulty). This type of problem-solving speed is not about achieving ta goal but

about creating a new idea. The concrete methods considered are the Schedule for the Evaluation of Individual

Quality of Life-Direct Weighting method, the Kawakida Jiro method, and the Mandala chart. Based on the

findings presented here, considering entropy change in the chaotic state should become an index for evaluating

the creation of a new idea instead of the repetition of an existing idea, as this type of thinking is beyond the

scope of artificial intelligence. Given that almost all natural phenomena, including human thinking, are based

on chaos theory, using this theory can promote scientific development in all academic fields.

Key-Words: - SEIQoL-DW method, Mandala chart, KJ method, chaos theory, human resource, decreasing

entropy, human thinking

Received: May 9, 2022. Revised: April 15, 2023. Accepted: May 16, 2023. Published: June 27, 2023.

1 Introduction

There are two kinds of human resource training, [1],

[2], [3], [4], [5], [6], [7]: technical and intellectual

(thinking-based) training; In theory, technical

training is easier to accomplish than the training of

human thinking patterns. It thus takes less time to

consider technical training than intellectual training.

Humans cannot objectively express their

thinking during the consideration period in a chaotic

state, [8], [9], [10], [11], [12], [13]. Objective

expressions are possible only in the fixed state

because there are infinite possibilities for expression

in the chaotic state. For example, indices such as

productivity, efficiency, and job satisfaction are

expressions in a fixed state, and change during a

chaotic period is objectively ignored.

However, a degree of change (that is, movement

from the problem to the solution) can, in theory, be

shown with the degree of decreasing entropy in

chaos. The change of entropy in human thinking to a

localized chaotic state from a proliferating chaotic

state is greater than that in a fixed state, [8], [9], [10],

[11], [12], [13]. The degree of decreasing entropy

(speed of problem-solving and degree of difficulty)

in human thinking must be considered in the

quantification of human resource training according

International Journal of Applied Sciences & Development

DOI: 10.37394/232029.2023.2.5

Hideaki Yanagisawa

E-ISSN: 2945-0454

Volume 2, 2023

to chaos theory. Entropy change in the chaotic state

can become an index to evaluate the creation of new

ideas instead of repeating existing ideas, which is,

for example, a degree of problem difficulty that a

chat generative pre-trained transformer (GPT), [14],

cannot solve. In this way, chaos theory can be used

to promote scientific development in all academic

fields.

2 Method

2.1 Explanation of Chaos Theory

Here, we explain chaos theory and the relationship

between human thinking and chaos theory, and we

present some important preliminary results on the

topic. The content of this section is similar to that

found in the author’s previous articles, [8], [9], [10],

[11], [12], [13], but it is repeated in this report

because of its importance.

2.1.1 Definition of Chaos Theory

Chaos theory can be defined as “the qualitative

study of unstable a periodic behavior in

deterministic nonlinear dynamical systems”, [15].

Chaos theory is part of complexity theory that

concerns itself with nonlinear dynamic systems

whose behavior does not follow clearly predictable

and repeatable pathways. In linear systems, the

relationship between an environmental factor and

system behavior is predictable and easily modeled.

In such systems, as the presence of an

environmental factor increases, system behavior

changes linearly in response to it. In contrast,

behavior in chaotic systems might be perceived as

being unpredictable, [16]. It is important, however,

that a chaotic state is not confused with the term

“random.” In mathematical terms, “random” refers

to “statistics governed by or involving equal

chances for each item” (New Oxford American

Dictionary).

2.1.2 Relationship between Continuous

Covariation and Chaos Theory

A chaotic equation requires three or more variables

and continuous covariation, [8], [9], [10], [11], [12],

[13], [17]. Fixed and chaotic solutions that are

continuous and have a bifurcation point between

them, known as the Feigenbaum point can be

obtained in any chaos equation, [18]. For example,

an equation that is representative of chaos is

expressed as follows:

󰇛  󰇜 󰇟  󰇛󰇜󰇠󰇛󰇜 (1)

In Figure 1, a schema near the Feigenbaum

point is shown in parts Q, R, S, T, and U, which

illustrates the converging fixed (parts Q, R, and S),

localized (part T), and proliferating chaotic (part U)

states. The dotted line F is the Feigenbaum point.

The vertical axis is Y(n), and the horizontal axis is p.

All natural phenomena (except mathematical

principles and historical facts) obey chaos theory,

because of the existence of three or more variables

and their continuous covariation between several

phenomena, including matter and the mind, [8], [9],

[10], [11], [12], [13], [17], [19].

Three or more variables and continuous

covariation exist between humans and the human

environment. A chaotic state is changed to a fixed

state by a living creature. This phenomenon can be

confirmed in thinking and evolution. However, the

fixed state changes to a new chaotic state with the

environment, and the new chaotic state changes to a

new fixed state with the living creature. Therefore,

human thinking obeys chaos theory because it fills

the necessary conditions for it. Human thinking

incorporates both a fixed state and a chaotic state. If

human thinking shifts excessively to one side, the

person in question may have some form of mental

illness. People must thus understand both fixed and

chaotic states and change their thinking according to

the environment.

2.2 Mathematical Classification: Inside and

Outside Chaos Theory

A chaos equation has either possible or impossible

solutions. Impossible solutions are those with either

no solution or with an infinite number of solutions,

but possible solutions comprise complete fixed,

incomplete fixed, chaotic, and random states, [8],

[9], [10], [11], [12], [13]. In complete fixed states,

information related to who, when, what, why,

where, and how is not required, because no change

occurs. Examples of this would include

mathematical principles, historical facts, and true

natural laws, which do not change with the

environment. A fixed state in chaos theory can

become a chaotic state depending on the variables in

the equation, which means that the state of a

solution can also change as the environment

changes; a fixed state is thus incomplete in chaos

theory.

A schema of complete fixed, incomplete fixed,

chaotic, and random states is shown in Figure 2. The

extreme left side of part Q (part K) is a completely

fixed state and lies outside chaos theory. However,

both the incomplete fixed (parts Q, R, and S) and

the chaotic (parts T and U) states, as in Figure 1, are

amenable to chaos theory. The extreme right side of

International Journal of Applied Sciences & Development

DOI: 10.37394/232029.2023.2.5

Hideaki Yanagisawa

E-ISSN: 2945-0454

Volume 2, 2023

part U (part V) is a random state, and it is not

amenable to chaos. Because a chaos equation is

based on mathematical principles, it is a completely

fixed state, and it can be used to resolve incomplete

fixed and chaotic states as well.

2.3 Relationship between Entropy Change

and Chaos Theory

The explanation of decreasing entropy is repeated in

this report because of its importance, [8], [9], [10],

[11], [12], [13]. Entropy is a statistical word and

was originally unrelated to physical phenomena

[20]; entropy decreases when there is a change of

direction from a chaotic state to a fixed state, [8],

[9], [10], [11], [12], [13], [21], which is shown as

the arrow G in Fig. 2. A schema near the

Feigenbaum point (dotted line F) are shown as parts

Q, R, S, T, and U in Figure 2. Entropy increases,

however, whenever there is a change of direction

from a fixed state to a chaotic state; this is shown as

arrow H in Figure 2.

2.4 Goal of Human Resource Training in

Thinking

The goal of human resource training is for a person

who receives the training to obtain answers in terms

of thinking. The result of “no answer” is also

considered. All problems are solved in a fixed state

because they are never solved in a chaotic state.

Therefore, all natural phenomena, except for fixed

and random states, are in a chaotic state. That is, all

problems whose answers cannot be given are in a

chaotic state. A state in which a person cannot

recognize a problem is chaotic.

Thus, solving a problem in a chaotic state

creates a shift to a fixed state in human thinking—

that is, it is equivalent to a decrease in entropy in

human thought. The goal of human resource training

is thus the development of the ability to decrease

entropy, which is thus equivalent to human

development, [22], [23]. That is, the degree to which

entropy decreases must be shown as the

quantification of human resource training in

thinking. To the best of the author’s knowledge, no

study has reported on this topic, [1], [2], [3], [4], [5],

[6], [7]. The degree of decreasing entropy can be

shown with the variable deciding the fixed or

chaotic state—for example, p in Equation (1). The

quantification of human resource training can thus

be confirmed with these indices.

2.5 Concrete Methods for Recognizing p in

Equation (1)

The concrete methods involved are the Schedule for

the Evaluation of Individual Quality of Life-Direct

Weighting (SEIQoL DW) method, [24], the

Kawakida Jiro (KJ) method, and the Mandala chart,

[25].

2.5.1 Chaos Theoretical Explanation for the

Methods Rearranging Human Thinking

Human thought can be rearranged with the methods

mentioned above. The content of this section is

similar to the author’s previous articles, [8], [24],

but it is repeated in this report because of its

importance.

From Equation (1), the Z(m) axis is

perpendicular to the p and Y(n) axes. In addition to

Equation (1), the following chaos equation is

assumed:

)()](1[)1( mZmZpmZ 

(2)

The axis of Z(m) is vertical to the axes of Y(n)

and p. From Equations (1) and (2), a three-

dimensional logistic map can be imagined. An

equation for the plane including the Y(n) and Z(m)

axes is as follows:

)()](1[

)1(

mZmZ

nYnY







(3)

Note that p is omitted from Equation (3). When

p changes from 3.0 to 4.0, the number of answers to

Equation (3) changes to 1, 4, 16, localized chaotic

state, and proliferated chaotic state. The processes

from the chaotic state to the fixed state are

equivalent to the methods for organizing thoughts.

The information collected at random is unified into

one thought by these processes. As one of its

procedures, the SEIQoL-DW and KJ methods are

compared with Equation (3). The relationship

between each of the states in Equation (3) and p

from Equation (1) is shown on the left side of Figure

3, Figure 4, Figure 5, Figure 6, and Figure 7.

The empty circles on the left-hand plane are all

correct answers and are shown with no organization

of thinking. The relationship between the localized

chaotic state (Feigenbaum point neighborhood) of

Equation (3) and the p of Equation (1) is shown in

Figure 3.

Each left-hand plane in Figure 3, Figure 4,

Figure 5, Figure 6 and Figure 7 is equivalent to the

SEIQoL-DW method. The left-hand plane in Figure

International Journal of Applied Sciences & Development

DOI: 10.37394/232029.2023.2.5

Hideaki Yanagisawa

E-ISSN: 2945-0454

Volume 2, 2023

6 and Figure 7 is equivalent to the KJ method. The

total of the left-hand planes in Figure 3, Figure 4,

and Figure 5 is equal to a Mandala chart. In other

words, The SEIQoL DW method is equivalent to the

sum of the KJ method and the Mandala chart.

2.5.2 Difference between the Degree of Goal

Achievement and Decrease in Entropy

Setting a goal is key in the quantification of human

resource training due to the decreasing entropy

beyond the Feigenbaum point. The objective degree

of goal achievement—such as productivity,

efficiency, and job satisfaction—is used to quantify

human resource training, [1], [2], [3], [4], [5], [6],

[7]. The last goal is shown as point Q in Fig. 1 and

Fig. 3, which indicates the problem solution. A state

in which people cannot understand or recognize

goals at all is called the proliferating chaotic state

(Point U) in Figure 1, Figure 2, Figure 3, Figure 4,

Figure 5, Figure 6 and Figure 7. A state in which

people can understand goals with some objective

expressions is equivalent to the incomplete fixed

state (Point S) in Figure 1 and Figure 5. People still

cannot understand the final goal at this time. The

final goal is point Q in Figure 1 and Figure 3. The p

of points Q, S, and U is 2.4, 3.5, and 4.0,

respectively.

  

   (4)

Here, N is the degree goal achievement.

However, this is incorrect, because the degree

of decreasing entropy is not considered. The part in

which entropy decreases the most is a process from

the proliferating chaotic state (Point U) to the

localized chaotic state (Point T) in Figure 1, Figure

5 and Figure 6. This is shown in the KJ method;

however, objective expression is impossible in this

process. Decreasing entropy from point U almost

reaches the Feigenbaum point in Figure 1 and

Figure 7. The chaos theoretical degree of goal

achievement is thus almost 100% at point S (i.e.,

goal achievement is completed with the submission

of this manuscript).

In an upcoming manuscript, an author will

report on which limit of AI is chaos theoretically

explained with the degree decreasing entropy. The

intuitive creativity of human beings is based on the

memory of 3.5 billion years, which cannot exist in

AI because of no input information. There were two

creative steps when a new cosmology, [9], [10],

[26], denying the Big Bang theory, [27], [28], was

reported.

The first step is “creativity,” which involved an

intuitive idea, based on many experiences, of what

things were like 3.5 billion years ago, [9], [10], [21].

If the Big Bang theory is correct, more than 3.5

billion years ago the universe had a considerably

higher density of matter, [9], [10]. However,

information is repeated by current DNA in living

creatures. This means that the density of matter

more than 3.5 billion years ago is the same as that of

the present, [9], [10], [21]. Changing the idea into a

manuscript. Step one involves decreasing entropy in

a chaotic state, while the second step involves

decreasing entropy in a fixed state. When the author

noticed a relationship between a new equation and

the Big Bang theory, the author’s thinking changed

to a fixed state, such as point S beyond the

Feigenbaum point in Figure 1 and Figure 5.

However, the author still could not show any

objective expression but had used most available

energy for thinking to solve the contradiction of the

Big Bang theory during this period. The author

thought that the manuscript denying the Big Bang

theory had almost been completed at that point.

The author’s thoughts were in Japanese, which

was then translated into English. The energy of the

author’s thoughts, with decreasing entropy, was not

almost used up in this process. That is, many people,

except for the author, could have created the

manuscript denying the Big Bang theory if they

were aware of the relationship between the new

equation and the Big Bang; indeed, Chat GPT

would be able to make a manuscript, too, [14]. The

present quantification of human resource training

mostly confirms this last point.

The creation of an idea is more difficult than the

objective expression of the idea thus created. In this

way, the achievement of a goal in thinking finishes

at a moment beyond the Feigenbaum point, chaos

theoretically. The quantification of human resource

training in thinking must therefore be shown with

the degree of decreasing entropy. The degree of

difficulty is shown with the diffusing degree of

point U in Fig. 1 and Figure 7, which ranges from 0

to 1.0 in Equation (1). The speed of problem-solving

is shown with the time required for the change in p

from point U to point S in Figure 1, Figure 5 and

Figure 7; both must therefore be considered in the

degree of decreasing entropy. Originally, technical

training had to be shown with the degree of

decreasing entropy, and considering a change in

entropy in the chaotic state would become an index

for evaluating the creation of a new idea instead of

the application of an existing idea. For example, is

the idea only repeating an existing idea? If so, the

idea can be searched using AI, such as Chat GPT,

International Journal of Applied Sciences & Development

DOI: 10.37394/232029.2023.2.5

Hideaki Yanagisawa

E-ISSN: 2945-0454

Volume 2, 2023

[14]. If not, the idea can never be found using AI.

Such thinking should be given the highest priority in

human resource training, as the former will be

accomplished with AI, [29], [30]. Thus, the index

for human resource training must be a problem of

such difficulty that AI can never solve it.

3 Results

All phenomena—except for a completely fixed state

(e.g., mathematics, historical facts, and true natural

laws) and a random state (e.g., dialing a random

digit)—follow chaos theory. Objective expressions

in the fixed state received attention in human

resource analysis; however, the change to a fixed

state from a chaotic state is the most important thing

in the development of human thinking. This change

can be expressed as a phenomenon of decreasing

entropy in thinking, and chaos theory, and can never

be transformed into AI. Therefore, the quantification

of human resource training, both intellectual and

technical, must be shown through the degree of

decreasing entropy (i.e., the speed of problem-

solving and the degree of problem difficulty).

4 Discussion

Entropy never decreases in all natural phenomena,

except the phenomenon in which the entropy of

living creatures decreases through thinking and

evolution. Entropy increases when humans are

confused. One example of increasing entropy is war,

[9]. Confusing a person is not the goal of human

resource training; rather, the goal of such training is

the development of the ability to achieve a fine

target. Given that novel thinking is a phenomenon

with decreasing entropy, the degree to which

entropy decreases must be used in the quantification

of human resource training. Objective expressions—

such as productivity, efficiency, and job

satisfaction—have already received much attention,

[1], [2], [3], [4], [5], [6], [7], [31], and human

resource analysis is expected to become an

established discipline by 2025, [31]. However, the

field as imagined is insufficient, because chaos

theory and decreasing entropy are not considered.

The change to a fixed state from a chaotic state is

the most important process in the development of

human thinking, [8], [22], [23], and this ability

appears quite late in developmental disorders. The

development of the ability to decrease entropy is the

goal of human resource training and is equivalent to

a noticeable ability in human development. The key

goal of training is the experience beyond the

Feigenbaum point, where humans may feel the

presence of God during the moment beyond the

Feigenbaum point, [9], [32], [33]. Problems with a

degree of difficulty that AI can never solve must be

the index for human resource development. Using

chaos theory thus promotes scientific development

in all academic fields because almost all natural

phenomena, including human thinking, follow chaos

theory.

5 Conclusion

As has been outlined above, almost all natural

phenomena, including human thinking, follow chaos

theory, but only objective expressions in the fixed

state currently receive attention in human resource

analysis. However, the change from a chaotic state

to a fixed state is the most important thing in the

development of human thinking. The quantification

of human resource training in thought, as well as in

technical training, must be shown through the

degree of decreasing entropy. The change of entropy

from the proliferating chaotic state to the localized

chaotic state in human thought is larger than in the

fixed state. When considering the degree of

decreasing entropy, many factors must be addressed,

such as the speed of problem-solving and the degree

of problem difficulty. Considering entropy change

in the chaotic state can become an index for

evaluating the creation of a new idea instead of the

repetition or redevelopment of an existing idea (i.e.,

problems that Chat GPT cannot solve). Used in this

way, using chaos theory can promote scientific

development in all academic fields.

References:

[1] Celik M, A theoretical Approach to the Job

Satisfaction. Polish Journal of Management

Studies, Vol. 4, 2011, 7-14.

[2] Ghosh P, Satyawadi R, Prasad Joshi J P,

Ranjan R, & Singh P, Towards more effective

training programmes: A study of trainer

attributes. Industrial and Commercial

Training, Vol. 44, No. 4, 2012, 194-202.

https://doi.org/10.1108/00197851211231469

[3] Armstrong M, Black D, & Miller A, Quality

criteria for core medical training: A resume of

their development, impact and future plans.

Journal of the Royal College of Physicians of

Edinburgh, Vol. 49, No. 3, 2019, 230-236.

https://doi.org/10.4997/JRCPE.2019.313

[4] Dietz D, & Zwick T, The retention effect of

training: Portability, visibility, and

credibility1. International Journal of Human

International Journal of Applied Sciences & Development

DOI: 10.37394/232029.2023.2.5

Hideaki Yanagisawa

E-ISSN: 2945-0454

Volume 2, 2023

Resource Management, Vol. 33, No. 4, 2022,

710-741.

https://doi.org/10.1080/09585192.2020.17378

[5] Hassett M P, The effect of access to training

and development opportunities, on rates of

work engagement, within the U.S. federal

workforce. Public Personnel Management,

Vol. 51, No. 3, 2022, 380-404.

https://doi.org/10.1177/00910260221098189

[6] Kozhakhmet S, Moldashev K, Yenikeyeva A,

& Nurgabdeshov A, How training and

development practices contribute to research

productivity: A moderated mediation model.

Studies in Higher Education, Vol. 47, No. 2,

2022, 437-449.

https://doi.org/10.1080/03075079.2020.17547

[7] Hassett M P, The effect of access to training

and development opportunities, on rates of

work engagement, within the U.S. federal

workforce. Public Personnel Management,

Vol. 51, No. 3, 2022, 380-404.

https://doi.org/10.1177/00910260221098189

[8] Yanagisawa H, Decreasing entropy in

thoughts and evolution: Main ability related to

inside nature. Mediterranean Journal of

Clinical Psychology, Vol. 5, No. 2, 2017,

http://cab.unime.it/journals/index.php/MJCP/a

rticle/view/1498/pdf

[9] Yanagisawa H, 2019, Relations Between

Human Thinking and Chaos Theory.

Scientific & Academic Press, USA.

[10] Yanagisawa H, Chapter 2, Chaos theoretical

explanation to each development of evolution

theory, psychology, physics, and philosophy,

In (Ed. Nima) Integrated Science Books, Vol.

5, pp. 13-37, 2022, Springer Nature.

[11] Yanagisawa H, Relationship between chaos

theory and history of philosophy. General

Scientific Journal, 2020,

http://www.gsjournal.net/ReseachPapers-

Philosophy/Download/8119

[12] Yanagisawa H, in press, Using mathematics

except statistics for the scientific development

of psychology: Decreasing entropy of clients’

and counselors’ thinking in the counseling

process.

[13] Yanagisawa H, Correcting the Herrmann’s

Whole Brain Model Using Chaos Theory and

Time’s Arrow to Decrease Personal Stress in

Human Relations. (in submission).

[14] Kalla D, Nathan Smith N. Study and analysis

of chat GPT and its impact on different fields

of study. International Journal of Innovative

Science and Research Technology, Vol. 8, No.

3, 2023, 827-833.

[15] Kellert H S, In the Wake of Chaos:

Unpredictable Order in Dynamical Systems

(Chicago, University of Chicago Press, 1993),

cited in Bedford C W, The case of chaos, In

Mathematics Teacher, Vol. 91, No. 4, April

1998, 276-281.

[16] McBride N, Chaos theory as a model for

interpreting information systems in

organizations. Information Systems Journal,

Vol. 15, No. 3, 2005, 233-254

[17] Yanagisawa H, 1996, Contradiction and

Development of Modern Science – Beyond

Chaos Theory. Kirishobo Co., pp. 115-123,

Tokyo.

[18] Feigenbaum M J, Quantitative universality for

a class of nonlinear transformations. Journal

of Statistical Physics, Vol. 19, No. 1, 1978,

25-52.

[19] Chay T. R., Chaos in a three-variable model

of an excitable cell. Physica D: Nonlinear

Phenomena, Vol. 16, No. 2, 1985, 233-242.

http://doi.org/10.1016/0167-2789(85)90060-0

[20] Stanford encyclopedia of philosophy,

information processing and thermodynamic

entropy, 2009,

http://plato.stanford.edu/entries/information-

entropy/

[21] Yanagisawa H, Relation of heart (mind) to

gene; The evolutionary theory with gene's

learning function. Kiri-Shobou, 1992, Tokyo.

[22] Yanagisawa H, Relationship of a chaos

equation to Piaget’s developmental theory and

selective attention deficits. Romanian Journal

of Applied Psychology, Vol. 18, No. 1, 2016,

18-23.

[23] Yanagisawa H, Relation between Chaos

Theory and Lewis’s Development theory:

Common Understanding is never achieved by

covariation alone. Mediterranean Journal of

Clinical Psychology, Vol. 4, No. 3, 2016,

http://cab.unime.it/journals/index.php/MJCP/a

rticle/view/1193/pdf_1

[24] Yanagisawa H, Relation of the Chaos

Equation to the SEIQoL-DW (Schedule for

the Evaluation of Individual Quality of Life-

Direct Weighting) method. Mediterranean

Journal of Clinical Psychology, 2014,

http://cab.unime.it/journals/index.php/MJCP/a

rticle/view/974/pdf_40

[25] David S, Visual Intelligencee: Using the deep

patterns of visual language to build cognitive

skill. Theory Into Practice, Vol. 47, No. 1,

2008, 118-127.

International Journal of Applied Sciences & Development

DOI: 10.37394/232029.2023.2.5

Hideaki Yanagisawa

E-ISSN: 2945-0454

Volume 2, 2023

[26] Yanagisawa H, Relation of Cosmos to living

creature; equation of energy and space-time.

Kiri-Shobou, Tokyo, 1995.

[27] Chernin A D, George Gamow and the big

bang. Space Science Reviews, Vol. 74, No. 3-

4, 1995, 447-454.

[28] Gamow G, Expanding universe and the origin

of elements. Physical Review, Vol. 70, No. 7-

8, 1946, 572-573.

[29] Losbichler H, & Lehner O M, Limits of

artificial intelligence in controlling and the

ways forward: A call for future accounting

research. Journal of Applied Accounting

Research, Vol. 22, No. 2, 2021, 365-382.

[30] Ford M, Rule of the robots: How AI will

transform everything. The New York Times

Bestselling, 2021.

[31] van den Heuvel S, & Bondarouk T, The Rise

(and fall) of HR Analytics: A study into the

future application, value, structure, and

system support. Journal of Organizational

Effectiveness: People and Performance, Vol.

4, No. 2, 2017, 157-178.

[32] Yanagisawa H, Relation of the chaos equation

to God perceived by Pascal, Nietzsche and

Nightingale. Scientific God. Journal, Vol. 3,

No. 2, 2012, 207-212.

[33] Yanagisawa H, Relationship between chaos

theory and God’s laws in Florence

Nightingale’s philosophy. Asian Journal of

Nursing Education and Research, Vol. 6, No.

1, 2016, 81-84.

International Journal of Applied Sciences & Development

DOI: 10.37394/232029.2023.2.5

Hideaki Yanagisawa

E-ISSN: 2945-0454

Volume 2, 2023

Appendix

Fig. 1: Logistic schema of equation (1)

Fig. 2: Schema of complete fixed, incomplete fixed,

chaotic, and random states

Fig. 3: Relationship between the fixed state of

Equation (3) and p = 2.4 in Equation (1)

Fig. 4: Relationship between the fixed state of

Equation (3) and p = 3.2 in Equation (1)

Fig. 5: Relationship between the fixed state of

Equation (3) and p = 3.5 in Equation (1)

Fig. 6: Relationship between the localized chaotic

state of Equation (3) and p = 3.6 in Equation (1)

International Journal of Applied Sciences & Development

DOI: 10.37394/232029.2023.2.5

Hideaki Yanagisawa

E-ISSN: 2945-0454

Volume 2, 2023

Fig. 7: Relationship between the proliferating

chaotic state of Equation (3) and p = 4.0 in Equation

(1)

Contribution of Individual Authors to the

Creation of a Scientific Article (Ghostwriting

Policy)

This article is the work of Hideaki Yanagisawa

alone.

Sources of Funding for Research Presented in a

Scientific Article or Scientific Article Itself

No funding was received for conducting this study.

Conflict of Interest

The authors have no conflict of interest to declare.

Creative Commons Attribution License 4.0

(Attribution 4.0 International, CC BY 4.0)

This article is published under the terms of the

Creative Commons Attribution License 4.0

https://creativecommons.org/licenses/by/4.0/deed.en

_US

International Journal of Applied Sciences & Development

DOI: 10.37394/232029.2023.2.5

Hideaki Yanagisawa

E-ISSN: 2945-0454

Volume 2, 2023