The Problem of Estimating the Sustainable Development of Technogenic
Production System in According to Cognitive Factors in the Innovation
Economy
Sultan K. Ramazanov
1 a
, Bohdan O. Tishkov
1 b
, Oleksandr H. Honcharenko
1 c
and Alexey M. Hostryk
2 d
1
Kyiv National Economic University named after Vadym Hetman, 54/1 Peremohy Ave., Kyiv, 03057, Ukraine
2
Odessa National Economic University, 8 Preobrazhenskaya Str., Odessa, 65000, Ukraine
Keywords:
Sustainable Development, Technogenic Production System, Cognitive Production Factors, Innovation
Economy, Knowledge-Intensive Enterprise, Industry 4.0, Convergence, Stochastic, Human Capital.
Abstract:
Development of integrated models based on the use of mathematical methods, models and innovative technolo-
gies to manage and predict the nonlinear dynamics of ecological, economic and socio-humanitarian systems
in modern conditions of instability and crises is an urgent problem. Synthesis of integrated models taking
into account humanitarian and cognitive variables to assess sustainable and safe development is also important
and relevant. The article summarizes and develops the results of previous works of the authors to solve the
problem of estimating the sustainable development of complex technogenic production systems, taking into
account cognitive factors in the conditions of innovative economy. The integration model of sustainable devel-
opment is presented as a family of models for creating integrated information systems of ecological, economic
and socio-humanitarian management of various social and organizational systems and especially economic
objects of anthropogenic nature to ensure sustainable and viable development. A cognitive model of nonlinear
system dynamics is presented, taking into account the dynamics of the humanitarian component with manage-
ment in general. Since innovative capital is wider than intellectual capital by its nature and content, the paper
also presents a model of innovation capital dynamics for the eco-economic and socio-humanitarian system
(EESHS). An extended integral model of nonlinear stochastic dynamics of EESHS in the innovation space is
obtained. The basis and paradigms of fundamental research in the works of the authors are: systems of type
“X”, integral models and the problem of sustainable development, models such as “NMSSD” and systems
such as “SEEHS”, convergent technologies “NBIC” and “NBIC⊕SG”.
1 INTRODUCTION
The relevance and need for basic and applied re-
search in elaborating and solving sustainable devel-
opment problems is defined by the 17 goals, which
were adopted by all UN member states in 2015 as
part of the 2030 Agenda for Sustainable Develop-
ment, which sets out a 15-years plan to achieve them.
Currently, there is some progress in many areas, but
in general, actions to implement the goals have not
yet reached the necessary pace and scale. These goals
have also been adapted and accepted for implementa-
a
https://orcid.org/0000-0002-8847-6200
b
https://orcid.org/0000-0003-3381-9103
c
https://orcid.org/0000-0003-4861-2439
d
https://orcid.org/0000-0001-6143-6797
tion in Ukraine (Rep, 2017).
The work proposed by the authors is an extension
of the results presented earlier in (Ramazanov and
Honcharenko, 2021; Ramazanov et al., 2020).
2 MAIN RESULTS
Currently, the determining factors of a knowledge-
intensive enterprise (KE) are not so much production
capacity, but rather knowledge, know-how, research
and development.
The theory of production factors (PF) by the be-
ginning of XXI century became one of the actual re-
search directions, covering the methodology of eco-
nomic analysis and management of economic sub-
196
Ramazanov, S., Tishkov, B., Honcharenko, O. and Hostryk, A.
The Problem of Estimating the Sustainable Development of Technogenic Production System in According to Cognitive Factors in the Innovation Economy.
DOI: 10.5220/0011932800003432
In Proceedings of 10th International Conference on Monitoring, Modeling Management of Emergent Economy (M3E2 2022), pages 196-203
ISBN: 978-989-758-640-8; ISSN: 2975-9234
Copyright
c
 2023 by SCITEPRESS – Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

jects. The main postulate of the theory of produc-
tion factors is that the ratio of external factors of pro-
duction and the internal state of the economic en-
tity determines its strategic position in a complex
and multidimensional market space, i.e. its organi-
zational, economic and structural sustainability (Moi-
seev, 1981; Kolemaev, 2005; Krass and Chuprinov,
2006; Vorontsovsky and Vyunenko, 2016).
The main provisions of the modern theory of PF
can be formulated as follows: organizational and eco-
nomic sustainability of the economic entity is deter-
mined by the ratio of available factors of production
and their effective management; competitive advan-
tages of the economic entity depend on the availabil-
ity (including ownership) of strategic resources; ef-
fective management of available factors of production
is provided by organizational capabilities of KE; tak-
ing into account cognitive, stochastic, humanitarian
and “NOT-” factors.
A logical question arises: what properties should
the factors of production have, so that the innovative
development of the KE could be effective, intensive
and adaptive?
To answer this question, it is necessary to clarify
the list of PF, which play a key role for the sustain-
able functioning and development of KEs, to intro-
duce the concept and give a definition of cognitive
factors of production; to develop a classification of
cognitive factors of production, etc.
To implement this task, we will use the system
paradigm, analyze the known concepts of PF and
identify the main characteristics of cognitive pro-
duction factors, which determine the organizational
and economic sustainability of KE
1
(figure 1) (Ra-
mazanov and Honcharenko, 2021).
Cognitive production factors (CPF). The analysis
of the development of the theory of production factors
and the emergence of their new types shows that the
composition and role of production factors are most
closely connected both with changes in production it-
self, and with the development of economic science,
identifying and explaining the emergence and purpose
of certain production factors by increasing opportuni-
ties for economic growth of knowledge-intensive en-
terprise.
Thus, according to the theory of human capital
(the term was introduced by G. Becker (Makarov
et al., 2009; Machlup, 1962; Milner, 2003)), the stock
of knowledge, abilities and motivation embodied in a
person contributes to the growth of human productive
1
KE – knowledge-intensive enterprises of the high-tech
sector of the economy. Knowledge-intensive enterprises (in
other words, high-tech enterprises – HE) are technological
leaders in the national innovation economy.
Figure 1: “Octagon” of basic assets/resources that support
the sustainability and safety of the system.
power. Human resources are to a certain extent sim-
ilar to natural resources and physical capital, but in
this interpretation they are divided into two parts. The
unit of “human capital” is not the worker himself, but
his knowledge. However, this capital does not exist
outside of its bearer. And this is the fundamental dif-
ference between human capital and physical capital –
machines and equipment.
By its economic essence, human capital is closer
to the intangible fixed assets of an enterprise. Accord-
ing to the theory of human capital, investments in hu-
man beings are regarded as a source of economic de-
velopment, no less important than “ordinary” capital
investments. This means that an economic dimension
is applied to a person.
The modern stage of KE development is charac-
terized by qualitative changes in the types of socially
significant human activity: labor characteristic of an
industrial society is replaced by creativity in a post-
industrial society. Machine technology gives way to
“intellectual technology”. As a result, knowledge and
information become the leading factors of production,
which leads to a decrease in the role of material fac-
tors of production. Radical changes in production re-
lations have led to special requirements for the qual-
ity of human resources, highlighting their intellectual
component and making them an independent factor of
production.
Let us introduce the concept of cognitive produc-
The Problem of Estimating the Sustainable Development of Technogenic Production System in According to Cognitive Factors in the
Innovation Economy
197

tion factor (CPF) – it is an embodied in an economic
entity totality of knowledge, abilities, skills, which
contribute to the growth of human productive power
in the creation of an intellectual product demanded by
the market.
The convergence of intellectual resources and in-
formation technology as a productive force causes the
emergence of new types of factors of production –
cognitive production factors (CPF, C
f
) – which means
specific, difficult to imitate resources of an industrial
enterprise to create a product and added value, de-
manded by the market.
CPF are considered as a productive force arising
from the convergence of human cognitive abilities and
information technology.
Cognition as a scientific-cognitive action, is mov-
ing to a new quality, providing relevant knowledge
for complex research. Artificial intelligence, neuro-
computers, technologies of various interfaces based
on the use of the properties of the human brain (Lukia-
nenko and Strelchenko, 2021) – a fundamentally new
environment of human productive activity. The use
of cognitive principles in economics allows to bring
the main production processes to an intellectually new
level.
CPF provide internal (endogenous) opportunities
for the development of industrial enterprises and, in
fact, become one of the sources of endogenous eco-
nomic growth (Kolemaev, 2005; Krass and Chupri-
nov, 2006). The management of CPF means the emer-
gence in the practice of industrial enterprises of a spe-
cific type of organizational and economic activity as-
sociated with their identification, ranking, analysis,
evaluation and monitoring at all stages of the repro-
duction cycle to achieve the goals of long-term eco-
nomic growth.
The allocation of CPF as a new type of produc-
tive force necessitates the development of appropriate
methods and models of their management, the prac-
tical implementation of which is possible due to the
mechanism of integration into the overall manage-
ment circuit of the industrial enterprise.
The effectiveness of methods used in the manage-
ment of traditional factors of production is becom-
ing less effective, since it does not take into account
the dynamics of modern changes, the need to pro-
cess a large amount of data, the structural complex-
ity of management tasks, the need to use coordination
mechanisms.
The study of theoretical and practical results of
production factor management allowed us to conclude
that CPF management should be integrated into the
overall management circuit of a high-tech enterprise
and be supported primarily by end-to-end activities
implemented through appropriate business processes.
The increasing intellectualization of industrial
production contributes to the fact that the distinctive
features of enterprises become:
• significant individualization of products in condi-
tions of high flexibility of high-volume produc-
tion;
• the modern vector of civilizational development
of society is represented by the intensive spread
of global technologies: nano-, bio-, information
and communication technologies. Cognitive tech-
nologies refer to the technologies of the global
level, the transformative effect of which gives a
new quality of interacting elements and leads to
the formation of a fundamentally new technologi-
cal platform for economic development;
• integration of consumers and manufacturers in
end-to-end processes of the entire product lifecy-
cle and value chain;
• integration of information and data within produc-
tion networks, reflecting all aspects of require-
ments, design, development, production, logis-
tics, operation, service, etc., i.e. creation of “pro-
duction intelligence”;
• globalization of product/goods development
teams, as the complexity of products requires a
variety of competencies;
• formation of a networked production “ecosystem”
through cooperation and reduction of barriers be-
tween enterprises and customers;
• development of cloud technologies as a way to
implement customized production on demand;
use the production capabilities of virtual produc-
tion networks based on united production sites,
and support them with special software;
• isolation and accumulation of intangible func-
tions, such as research and forecasting of the mar-
ket and demand, formation of the product concept,
formation of technical requirements, etc.; since
intangible components take an increasing share in
the cost and price of the finished product;
• formation of the market value of enterprises
due to the knowledge of employees, know-how,
knowledge-intensive technologies, inventions, in-
dustrial designs and other intangible assets. The
qualitative change of production factors puts for-
ward a set of interrelated tasks for industrial enter-
prises (Krass and Chuprinov, 2006; Vorontsovsky
and Vyunenko, 2016);
• the integration into Industry 4.0, increasing the
continuity and flexibility of production, the tran-
sition to flexible production systems that ensure
M3E2 2022 - International Conference on Monitoring, Modeling Management of Emergent Economy
198

the adaptation of the production infrastructure to
innovative activities, changes in market require-
ments demand different approaches to the com-
position and configuration of key factors of pro-
duction (Kobets and Yatsenko, 2019);
• increased consistency in the duration and produc-
tivity of all interrelated subdivisions of industrial
enterprises causes the accounting of results not
only at the place of application of production fac-
tors, but also in related units from the perspective
of their impact on the economic performance of
enterprises;
• rational increase in the growth of R&D costs,
which ensures the implementation of scientific
and technological policy directly in the process of
scientific and production activities, determines the
assessment of their relationship with the share of
revenues from new types of products;
• the uncertainty of the economic environment,
high risks in the development of innovative prod-
ucts create the preconditions for the development
of economic-mathematical models that are ade-
quate to the object of research and improve the
quality of the effectiveness of industrial enter-
prises.
Thus, sustainable economic growth and develop-
ment of modern industrial enterprises determines not
so much the number of personnel, but the presence of
workers who are able to conduct scientific and tech-
nological development at the modern level, to create
competitive products and services on their basis, to
propose new ways of organizing production, to de-
termine the process of forming new trends in techno-
logical development in the market environment. In
this regard, we need a different system of productive
forces, surpassing the capabilities of industrial type of
production and other ways of combining human and
material labor.
The convergence of intellectual resources and in-
formation technologies as a productive force causes
the emergence of new types of production factors –
cognitive factors of production – which are under-
stood as specific, difficult to imitate resources of an
industrial enterprise that allow creating a product that
is in demand by the market.
Cognitiveness, as a scientific and cognitive action,
is moving into a new quality, providing appropriate
knowledge for comprehensive research. Artificial in-
telligence, neurocomputers, technologies of various
interfaces based on the use of the properties of the
human brain are a fundamentally new environment
for human production activities. The use of cogni-
tive principles in the economy allows you to bring
the main production processes to an intellectually new
level.
Cognitive production factors provide internal op-
portunities for the development of industrial enter-
prises and, in fact, become one of the sources of en-
dogenous economic growth (Kolemaev, 2005; Krass
and Chuprinov, 2006; Vorontsovsky and Vyunenko,
2016). Cognitive production factors management
means the emergence in the practice of industrial en-
terprises of a specific type of organizational and eco-
nomic activity related to their identification, ranking,
analysis, evaluation, monitoring at all stages of the re-
production cycle in order to achieve the goals of long-
term economic growth.
The identification of cognitive factors of produc-
tion as a new type of productive force necessitates
the development of appropriate methods and mod-
els of their management, the practical implementation
of which is possible due to the mechanism of inte-
gration into the overall control loop of an industrial
enterprise (Ramazanov and Honcharenko, 2021; Ra-
mazanov et al., 2020; Pankratov, 2017; Gorlacheva,
2020).
So, cognitive production factors (CPF, C
f
) – are
the result of the convergence of intellectual resources
/ intellectual capital and information technology:
“IR/IC” & “IT”,
where & – here is a conditional symbol of conver-
gence.
Cognitive basis of high-tech activity, which in-
cludes the unity of knowledge, experience, creativity
and information technology. Structural elements of
CPF are: knowledge, experience, creativity and skills
in the use of information technology, i.e. CPF – is a
tuple <knowledge, experience, creativity, level of use
of IT, ...>.
One of the variants of correlations of cognitive
production factors (CPF), human capital (HC) and
intellectual capital (IC) by three comparison param-
eters.
1. Structural elements:
• CPF: Knowledge, experience, creativity, skills,
in the use of information systems and technol-
ogy.
• HC: Level of education, health status.
• IC: Market assets, human assets, intellectual
property, infrastructure assets.
2. Methods of evaluation and measurement:
• CPF: Indicator based on up-to-date financial
and accounting statements.
• HC: Aggregated indices, the calculation of
which requires an extensive information base.
The Problem of Estimating the Sustainable Development of Technogenic Production System in According to Cognitive Factors in the
Innovation Economy
199

• IC: Ratio of market value to book value; Intel-
lectual coefficient of value added.
3. Correlation with performance results:
• CPF: Production function.
• HC: The balanced scorecard system.
• IC: Aggregate of IC and capital involved.
Note that the presented list of CPF is not exhaus-
tive, it can and should be supplemented and improved.
So, CPF is a set of both active and intensional, as
well as tangible and intangible factors of production:
• tangible-active can include those CPF, which are
embodied and directly used in the economic
turnover. These include local computer networks
for information exchange, flexible manufactur-
ing systems (FMS), simple/complex robots, auto-
mated information storage and retrieval systems,
planning systems (ERPI, ERPII), design systems
(CFD, CAE, PLM), electronic document manage-
ment systems, vision systems;
• intangible assets include objects of intellectual
property: know-how, technical solutions, licenses,
patents, databases, information about customers
and suppliers, etc;
• material-intentional cognitive factors include the
potential use of advanced technologies, such as
augmented reality technologies, artificial intelli-
gence technologies: Internet of Things technolo-
gies, big data, cloud computing, deep learning,
5G, etc;
• intangible-intentional include personal character-
istics of employees, experience, culture of think-
ing, ability to learn, creativity, insight, intuition,
level of education, level of digital literacy, abil-
ity to cognitive activity, analysis, reflection, self-
regulation, communication abilities, compliance
with ethical and social norms.
Let us also note now that innovation capital is one
of the most important and specific forms of capital, re-
flecting the ability of industrial enterprises as partici-
pants in the innovation cluster to generate income due
to the development of innovative activity and acquisi-
tion of a special status due to the dynamics of inno-
vation potential as an institution capable of transfor-
mation into capital as a result of the synergistic effect
of interaction between economic entities in the pro-
cess of innovation development. Innovation capital
from the point of view of classical economic theory
is characterized by three essential features, namely, it
is a product of past labor, the role of which is played
by innovation potential; it is a production or product
stock in the form of innovations produced and ready
for implementation, as well as innovations requiring
further improvement and innovations that can be ac-
cumulated in the form of innovation potential; it is a
source of income based on the effective commercial-
ization of innovation (Milner, 2009; Geets and Semi-
nozhenko, 2006).
By its nature and content, innovation capital is
wider than intellectual capital, which according to the
concept presented in the works of Milner (Milner,
2009, 2003), consists of three elements: 1) human
capital; 2) organizational (structural) capital; 3) con-
sumer capital. Machlup (Machlup, 1962) in 1966, an-
alyzing the processes of knowledge production and
dissemination in the United States, without down-
playing the role and importance of material produc-
tion, reasonably proved that the economic develop-
ment of the “new age” is determined not so much by
the availability and productivity of material resources
as by the availability and speed of information distri-
bution in society and the amount of intellectual capital
(Moiseev, 1981; Kolemaev, 2005; Vorontsovsky and
Vyunenko, 2016).
Let us present a cognitive model of the nonlinear
dynamics of the system, taking into account the dy-
namics of the humanitarian component with control
(as an extension of the integral model (Ramazanov
and Honcharenko, 2021; Ramazanov et al., 2020)), in
general terms it can be represented as stochastic dif-
ferential equations:
dH
U
(t)
dt
= χ
+
H
+
U
(t) − χ
−
H
−
U
(t)+
σ
H
U
(H
U
,t)dW
H
U
(t) + b
H
U
U
H
U
(t). (1)
dC
f
(t)
dt
= ϑ
+
C
+
f
(t) − ϑ
−
C
−
f
(t)+
σ
C
f
(C
f
,t)dW
C
f
(t) + ϑ
C
f
U
C
f
(t). (2)
The model of the dynamics of innovativeness
of the eco-economic and socio-humanitarian system
(EESHS) can also be represented in the form of an
equation of dynamics:
dI
c
(t)
dt
= ς
+
I
+
c
(t) − ς
−
I
−
c
(t)+
σ
I
c
(I
c
,t)dW
I
c
(t) + ϑ
I
c
U
I
c
(t). (3)
In equations (1)-(3) the variable H
U
(t) is a hu-
manitarian variable, C
f
(t) – cognitive variable, I
c
(t) –
variable (level) of innovativeness in the integral model
EESHS [2]; χ
+
,χ
−
,ϑ
+
,ϑ
−
,ς
+
,ς
−
– parameters, and
other designations are given in the same work.
So, supplementing the system of equations of the
integral model (Ramazanov and Honcharenko, 2021;
Ramazanov et al., 2020; Halitsin and Ramazanov,
2016) with equations (1) – (3) we obtain an extended
M3E2 2022 - International Conference on Monitoring, Modeling Management of Emergent Economy
200

(generalized) integral model of nonlinear stochastic
dynamics of EESHS in the innovation space.
The generalized production and technological
function (PTF) can now be represented as:
Y (t) = F[K(t, L(t),H(t),N(t), Φ(t),
S(t), I
c
(t),C
f
(t);~c]. (4)
It can be used to study sustainable development.
In the general case, the integral level of sustain-
able development can be represented as a nonlinear
function:
Y
sdl
(t) = F
sdl
[]K(t), L(t), H(t), N(t),
Φ(t), S(t), I
c
(t),C
f
(t),~c]. (5)
Private versions of the PTF model:
a) Mankiw-Romer-Weil model. Option of ac-
counting for human capital H in the production func-
tion (PF), along with physical capital (K), labor (L)
and natural (N) resources:
Y (t) = K
α
(t) · H
β
(t) · [A(t) · L(t)]
1−α−β
, (6)
where α, β > 0, α + β < 1; H; A(t) – function of sci-
entific and technological progress. Note that α – is a
part of capital provided by investment growth (capital
costs); β is similar.
b) Model of accounting for all fixed assets:
Y (t) = A(t)K
α
(t) · L
β
(t) · H
γ
(t) · S
ρ
(t)·
Φ
q
(t) · N
τ
(t) · I
ν
(t), (7)
where α,β, γ,ρ, q,τ, ν > 0 and α + β+γ + ρ +q+τ +
ν = 1.
The following notations are also used here: K –
physical capital, L – labor (labor), H – human capi-
tal, S – social capital, Φ – financial capital, N – natu-
ral resources (land, water, etc.), A(t) is a function of
the level of scientific, technical and technological de-
velopment, for example, A(t) = aT
S
(t), where T (t) –
volume of innovative technologies (resources).
In (Ramazanov et al., 2020), the equation of the
dynamics of the potential of the R&D sector in the
integral model is presented as:
d
dt
[
˙
ϕ(t)] − δ
ϕ
ϕ(t) = G[ϕ(t)]
γ
1
· [α
1
L
1
(t)L
1
(t)]
γ
2
·
[α
1
K
(t)K(t)]
γ
3
· [s(t)]
γ
4
+ σ
ϕ
(ϕ,t)e
ϕ
(t), (8)
where ϕ(t) – stock of knowledge and technologies in
the economy – the number of inventions that have not
lost their relevance by the year t;
˙
ϕ(t) – increase in
the stock of knowledge per unit of time – the number
of new inventions per year t minus obsolete; L
1
(t) –
the volume of skilled (more precisely – highly skilled)
labor (skilled labor force with qualifications, i.e. the
product of the number of skilled workers L
1
(t) and the
level of qualification of the average employee h(t),
i.e. h(t)L
1
(t)); s(t) – social index; δ
ϕ
– the rate of
knowledge attrition due to its obsolescence δ
ϕ
> 0;
α
1
L
1
(t) – share of skilled labour employed in the R&D
sector 0 ≤ α
1
L
1
(t) ≤ 1; γ
1
,γ
2
,γ
3
– static parameters
0 ≤ γ
1
≤ 1, 0 ≤ γ
2
≤ 1, 0 ≤ γ
3
≤ 1; G – scale pa-
rameter: G > 0. Here {e
ϕ
(t),t ∈ T } – white noise
with continuous time; σ
ϕ
(ϕ,t) – volatility coefficient.
From (Ramazanov and Honcharenko, 2021; Ra-
mazanov et al., 2020) we have a more general equa-
tion of dynamics, i.e. the equation of the STP index
(STP weight), which shows the growth and efficiency
of the use of labor, capital and technology in produc-
tion, i.e. τ(t):
d
dt
[
˙
τ(t)] + δ
τ
τ(t) = B[
˙
ϕ(t) + δ
ϕ
ϕ(t)]
β
1
·
[
˙
σ(t) + δ
σ
σ(t)]
β
2
[ ˙s(t) + δ
s
s(t)]
β
3
[˙z(t) + δ
z
z(t)]
β
4
(9)
where
˙
τ(t) is the increase of the STP index caused
by the change in the number of advanced production
technologies used in production per unit of time, δ
τ
–
the rate of decrease of the STP index due to the obso-
lescence of advanced production technologies, δ
τ
> 0;
β
1
,β
2
,β
3
,β
4
– static parameters, 0 ≤ β
1
≤ 1, 0 ≤ β
2
≤
1,0 ≤ β
3
≤ 1, 0 ≤ β
4
≤ 1; B – scale parameter; B > 0.
Note that τ(t) – STP index, dependent on the num-
ber of advanced production technologies w(t) and
used in production, for example, τ(t) = [w(t)]
d
, where
d − const.
Now in this generalized and integral variant we
can use the conditions of development stability from
(Ramazanov and Honcharenko, 2021; Ramazanov
et al., 2020; Zgurovsky, 2006).
This construction of the indicator will reflect
the importance of each of the considered compo-
nents: eco-economic and socio-humanitarian subsys-
tems (spheres) in the performance of the objective
function. A change in any of the private indicators
leads to a change in the value of the aggregate indi-
cator and captures a change in the steady state of the
region. In the general case, all indicators change over
time, i.e. have a certain dynamic.
Simple conditions for sustainable development
(SD) are defined as follows.
1) Condition of weak stability:
dF[·]
dt
≥ 0 or F
t+1
[·] ≥ F
t
[·], (10)
where
F
t
[·] = F[K(t),L(t),H(t), N(t),
Φ(t), S(t), I
c
(t),C
f
(t),~c]
(11)
The Problem of Estimating the Sustainable Development of Technogenic Production System in According to Cognitive Factors in the
Innovation Economy
201

2) Condition of strong stability:
dF[·]
dt
≥ 0 , N = N
C
+ N
S
and
dN
C
dt
≥ 0, (12)
or N
C
t+1
≥ N
C
t
, N = 1...4
where N
C
– critical part of natural capital, and N
S
–
natural capital, which can be replaced by artificial.
For example, given critical natural capital N
C
, sus-
tainable development can be supplemented by a time
limit on depletion of this value. For a time-decreasing
production function, the arguments of which are ag-
gregated variables: labor – L, capital – K and natural
– resource N, we will have the ratio:
F
t
(K,L,N) ≤ F
t+1
(K,L,N) (13)
or, in the general case:
F(K(t), L(t), H(t), N(t), Φ(t),S(t),I
c
(t),C
f
(t),~c) ≤
F(K(t + 1), L(t + 1),H(t + 1), N(t + 1), Φ(t + 1),
S(t + 1), I
c
(t + 1),C
f
(t + 1),~c) (14)
And it also requires compliance with the condi-
tion of not decreasing in time the value of N
C
, i.e.
N
t
= N
C
t
+ N
S
t
, as well as the condition of par-
tial replacement of natural capital N by artificial N
S
(or non-renewable resource for renewable resource):
N
t
= N
C
t
+ N
S
t
.
The integrated level of sustainable development
for all capital (resources) can be defined, for exam-
ple, in the case of linear dependence as:
Y
sdl
(t) = c
1
K(t) + c
2
L(t) + c
3
H(t) + c
4
N(t)+
c
5
Φ(t) + c
6
S(t) + c
7
I
c
(t) + c
8
C
f
(t), (15)
where c
1
,c
2
,c
3
,c
4
,c
5
,c
6
,c
7
,c
8
are weight (normaliz-
ing and scaling) coefficients.
3 CONCLUSION
The article summarizes and develops the results of the
authors’ earlier works in solving the problem of es-
timating the sustainable development of technogenic
production system, taking into account cognitive fac-
tors in the context of innovation economy.
Integration model of sustainable development is
presented as a family of models for creating in-
tegrated information systems of eco-economic and
socio-humanitarian management of various social and
organizational systems and especially economic ob-
jects of anthropogenic nature to ensure sustainable
and viable development.
A cognitive model of nonlinear system dynam-
ics is presented, taking into account the dynamics
of the humanitarian component with management in
general. Since innovative capital is wider than intel-
lectual capital by its nature and content, the paper
also presents a model of innovation capital dynam-
ics for the eco-economic and socio-humanitarian sys-
tem (EESHS). An extended integral model of nonlin-
ear stochastic dynamics of EESHS in the innovation
space is obtained.
The transition to an information society leads to a
change in the structure of total capital in favor of hu-
man capital, an increase in intangible flows, knowl-
edge flows, intellectual and innovative capital. The
problem of sustainable development based on 8 im-
portant assets that support the sustainability and via-
bility of EESHS was investigated.
The use of these methods will increase the effi-
ciency of solutions in the management of technogenic
production systems, will increase the efficiency of the
use of innovations and will identify areas of innova-
tion strategies of the regions.
The presented result requires further research,
generalizations and computer experiments on real
data.
REFERENCES
(2017). Sustainable Development Goals:
Ukraine. Technical report, Ministry of Eco-
nomic Development and Trade of Ukraine.
https://me.gov.ua/Documents/Download?id=
05822f66-290b-4b51-a392-347e76ebeb5f.
Geets, V. M. and Seminozhenko, V. P. (2006). Innovative
prospects of Ukraine. Constanta, Kharkiv.
Gorlacheva, E. (2020). Metodologiia upravleniia kogni-
tivnymi faktorami proizvodstva vysokotekhnologich-
nykh promyshlennykh predpriiatii. D.Sc. thesis, spe-
cialty 08.00.05, N.E. Bauman Moscow State Techni-
cal University, Moscow.
Halitsin, V. and Ramazanov, S. K. (2016). Integral stochas-
tic nonlinear model of dynamics of innovation econ-
omy. Modeling and information systems in the econ-
omy, (92):50–64. https://ir.kneu.edu.ua/handle/2010/
22805.
Kobets, V. and Yatsenko, V. (2019). Influence of the Fourth
industrial revolution on divergence and convergence
of economic inequality for various countries. Neiro-
Nechitki Tekhnolohii Modelyuvannya v Ekonomitsi,
2019(8):124–146.
Kolemaev, V. (2005). Economic and mathematical model-
ing. Modeling of macroeconomic processes and sys-
tems. Unity-Dana, Moscow.
Krass, M. and Chuprinov, B. (2006). Mathematical Meth-
ods and Models for Master’s Students in Economics.
Saint-Petersburg.
Lukianenko, D. and Strelchenko, I. (2021). Neuromodeling
of features of crisis contagion on financial markets be-
M3E2 2022 - International Conference on Monitoring, Modeling Management of Emergent Economy
202

tween countries with different levels of economic de-
velopment. Neiro-Nechitki Tekhnolohii Modelyuvan-
nya v Ekonomitsi, 2021(10):136–163.
Machlup, F. (1962). The Production and Distribution of
Knowledge in the United Statess. Princeton University
Press, Princeton, New Jersey.
Makarov, V. L., Bahtizin, A. R., and Bahtizina,
N. V. (2009). A computable model of the
knowledge economy. Ekonomika i matematich-
eskie metody, 45(1):70–82. https://naukarus.com/
vychislimaya-model-ekonomiki-znaniy.
Milner, B. Z. (2003). Knowledge Management: The Evo-
lution and Revolution in the Organization. Infra-M,
Moscow.
Milner, B. Z. (2009). Innovative Development: Economics,
Intellectual Resources, and Knowledge Management.
Infra-M, Moscow.
Moiseev, N. (1981). Mathematical problems of systems
analysis. Nauka, Moscow.
Pankratov, V., editor (2017). Strategy of socio-economic
systems development based on foresight and cognitive
modeling methodologies. Kyiv.
Ramazanov, S. and Honcharenko, O. (2021). The prob-
lem of estimating the sustainable development of the
integrated technogenic industrial system. SHS Web
of Conferences, 107:11002. https//doi.org.10.1051/
shsconf/202110711002.
Ramazanov, S., Stepanenko, O., and Ustenko, S. (2020).
Information and innovative management technologies
in ecological and economic systems. Kyiv national
economic univeristy named after Vadym Hetman.
Vorontsovsky, A. V. and Vyunenko, L. F. (2016). Forecast-
ing of economic development based on a stochastic
model of economic growth, taking into account the
pivot point. Vestnik of the St. Petersburg University.
Series 5. Economics, (4):4–32. https://econpapers.
repec.org/article/scn003571/16949689.htm.
Zgurovsky, M. Z. (2006). Sustainable development in the
global and regional dimensions: analysis according
to 2005. Kyiv.
The Problem of Estimating the Sustainable Development of Technogenic Production System in According to Cognitive Factors in the
Innovation Economy
203