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

Kyiv National Economic University named after Vadym Hetman, 54/1 Peremohy Ave., Kyiv, 03057, Ukraine

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-

https://orcid.org/0000-0002-8847-6200

https://orcid.org/0000-0003-3381-9103

https://orcid.org/0000-0003-4861-2439

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

 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

(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

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

) – 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

) – 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:

(t)

= χ

(t) − χ

−

(t)+

,t)dW

(t) + b

(t). (1)

(t)

= ϑ

(t) − ϑ

−

(t)+

,t)dW

(t) + ϑ

(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:

(t)

= ς

(t) − ς

−

(t)+

,t)dW

(t) + ϑ

(t). (3)

In equations (1)-(3) the variable H

(t) is a hu-

manitarian variable, C

(t) – cognitive variable, I

(t) –

variable (level) of innovativeness in the integral model

EESHS [2]; χ

,χ

−

,ϑ

−

,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

(t),C

(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:

sdl

(t) = F

sdl

[]K(t), L(t), H(t), N(t),

Φ(t), S(t), I

(t),C

(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

(t) · H

(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

(t) · L

(t) · H

(t) · S

(t)·

(t) · N

(t) · I

(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

(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:

[

ϕ(t)] − δ

ϕ(t) = G[ϕ(t)]

· [α

(t)L

(t)]

[α

(t)K(t)]

· [s(t)]

+ σ

(ϕ,t)e

(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

(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

(t) and the

level of qualiﬁcation of the average employee h(t),

i.e. h(t)L

(t)); s(t) – social index; δ

– the rate of

knowledge attrition due to its obsolescence δ

> 0;

(t) – share of skilled labour employed in the R&D

sector 0 ≤ α

(t) ≤ 1; γ

,γ

– static parameters

0 ≤ γ

≤ 1, 0 ≤ γ

≤ 1; G – scale pa-

rameter: G > 0. Here {e

(t),t ∈ T } – white noise

with continuous time; σ

(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):

[

τ(t)] + δ

τ(t) = B[

ϕ(t) + δ

ϕ(t)]

[

σ(t) + δ

σ(t)]

[ ˙s(t) + δ

s(t)]

[˙z(t) + δ

z(t)]

(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

–

the rate of decrease of the STP index due to the obso-

lescence of advanced production technologies, δ

> 0;

,β

– static parameters, 0 ≤ β

≤ 1, 0 ≤ β

≤

1,0 ≤ β

≤ 1, 0 ≤ β

≤ 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)]

, 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[·]

≥ 0 or F

t+1

[·] ≥ F

[·], (10)

where

[·] = F[K(t),L(t),H(t), N(t),

Φ(t), S(t), I

(t),C

(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[·]

≥ 0 , N = N

+ N

and

≥ 0, (12)

or N

t+1

≥ N

, N = 1...4

where N

– critical part of natural capital, and N

–

natural capital, which can be replaced by artiﬁcial.

For example, given critical natural capital N

, 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:

(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

(t),C

(t),~c) ≤

F(K(t + 1), L(t + 1),H(t + 1), N(t + 1), Φ(t + 1),

S(t + 1), I

(t + 1),C

(t + 1),~c) (14)

And it also requires compliance with the condi-

tion of not decreasing in time the value of N

, i.e.

= N

+ N

, as well as the condition of par-

tial replacement of natural capital N by artiﬁcial N

(or non-renewable resource for renewable resource):

= N

+ N

The integrated level of sustainable development

for all capital (resources) can be deﬁned, for exam-

ple, in the case of linear dependence as:

sdl

(t) = c

K(t) + c

L(t) + c

H(t) + c

N(t)+

Φ(t) + c

S(t) + c

(t) + c

(t), (15)

where c

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.

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