Дослідження тенденцій безпеки глобального суспільства на основі інтелектуального аналізу даних
This article is devoted to applying system analysis and data mining methodology to one of the most pressing problems today: studying the security of a global society in a conflicting world. A set of global threats relevant to the first half of the 21st century is considered. These threats have been...
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| author | Zgurovsky, Michael Pyshnograiev, Ivan |
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| author_institution_txt_mv | [
{
"author": "Michael Zgurovsky",
"institution": "National Technical University of Ukraine \"Igor Sikorsky Kyiv Polytechnic Institute\", Kyiv"
},
{
"author": "Ivan Pyshnograiev",
"institution": "National Technical University of Ukraine \"Igor Sikorsky Kyiv Polytechnic Institute\", Kyiv"
}
] |
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| description | This article is devoted to applying system analysis and data mining methodology to one of the most pressing problems today: studying the security of a global society in a conflicting world. A set of global threats relevant to the first half of the 21st century is considered. These threats have been identified by the United Nations (UN), the World Health Organization (WHO), the World Economic Forum, and other reputable international organizations. As a result of applying the Delphi method to analyze a wide range of threats identified by these organizations, 11 of the most important threats to humanity in the first half of the 21st century were identified. The vulnerabilities of different countries to the impact of the totality of these threats are analyzed. Scenarios for the possible development of a global society during and after the conflict are constructed. |
| doi_str_mv | 10.20535/SRIT.2308-8893.2022.3.01 |
| first_indexed | 2025-07-17T10:28:01Z |
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M. Zgurovsky, I. Pyshnograiev, 2022
Системні дослідження та інформаційні технології, 2022 № 3 7
TIДC
ТЕОРЕТИЧНІ ТА ПРИКЛАДНІ ПРОБЛЕМИ
І МЕТОДИ СИСТЕМНОГО АНАЛІЗУ
UDC 008+303:519.8
DOI: 10.20535/SRIT.2308-8893.2022.3.01
STUDY OF SECURITY TRENDS OF THE GLOBAL SOCIETY
BASED ON INTELLIGENT DATA ANALYSIS
M. ZGUROVSKY, I. PYSHNOGRAIEV
Abstract. This article is devoted to applying system analysis and data mining meth-
odology to one of the most pressing problems today: studying the security of a
global society in a conflicting world. A set of global threats relevant to the first half
of the 21st century is considered. These threats have been identified by the United
Nations (UN), the World Health Organization (WHO), the World Economic Forum,
and other reputable international organizations. As a result of applying the Delphi
method to analyze a wide range of threats identified by these organizations, 11 of
the most important threats to humanity in the first half of the 21st century were iden-
tified. The vulnerabilities of different countries to the impact of the totality of these
threats are analyzed. Scenarios for the possible development of a global society dur-
ing and after the conflict are constructed.
Keywords: global safety, systemic conflicts, global threats, Minkowski norm, vul-
nerability.
INTRODUCTION
Since the beginning of the 21st century, many recognized international organiza-
tions have conducted research to identify the major challenges facing humanity.
Such organizations include the United Nations (UN), the World Health Organiza-
tion (WHO), the World Economic Forum (WEF), Transparency International, the
Global Footprint Network, the International Energy Agency, the World Resources
Institute, the British Petroleum Company and others. Each of these organizations
not only identified challenges for their field of activity, but also tried to assess the
impact of these challenges on other areas of human life.
There is a problem of consolidating these studies and creating a necessary
and sufficient set of global threats to the sustainable development of mankind.
This study is a continuation of studies of the behaviour of complex socio-
economic systems [1], global threats and sustainable development processes [2].
The new study took into account the results of the analysis of global threats to
humanity, performed by the following international organizations:
1. On January 11, 2022, the World Economic Forum presented The Global
Risks Report 2022 [3], in which for the next 10 years it formed the necessary and
sufficient set of threats to the sustainable development of mankind. WEF experts
M. Zgurovsky, I. Pyshnograiev
ISSN 1681–6048 System Research & Information Technologies, 2022 № 3 8
identified a total of 37 global threats in 5 areas of human activity: economic, envi-
ronmental, geopolitical, social and technological.
2. Using the Delphi method, The Millennium Project identified 15 global
challenges in the same areas [4].
3. Due to the fact that this study examines the threats to sustainable devel-
opment, it is also necessary to take into account the 17 UN Sustainable Develop-
ment Goals set out in the “Sustainable Development Agenda 2030” [5, 6].
For further study, we use the variety of threats formulated by the above-
mentioned international organizations.
CHARACTERISTICS OF GLOBAL THREATS TO SUSTAINABLE
DEVELOPMENT
As a result of applying the Delphi method to analyze a wide range of threats iden-
tified by the organizations mentioned above, 11 of the most important threats to
humanity in the first half of the 21st century were identified.
Threat 1. Global decrease in energy security (ES)
The country’s energy independence is an integral and fundamental component of
its sovereignty. It determines the country’s self-sufficiency in energy supply and
energy generation. In the conditions of constant growth of consumption of energy
of the world it is necessary to increase also its production (Fig. 1).
Source: based on data in [7]
At the same time, it is impossible to constantly increase the extraction of
fossil energy resources (Fig. 2), whose reserves are rapidly declining. In addition,
the behaviour of this type of resources in the market is significantly influenced by
world politics. In the Short-term Energy Outlook of the U.S. The Energy Informa-
tion Administration [8] noted that the oil and gas market have great uncertainties,
including due to “Russia’s full-scale invasion of Ukraine”.
On the one hand, the world community needs to find new energy sources,
develop alternative energy [9], and on the other hand in conditional of changing
geopoletics, the is a need to take care of its independence from extremal energy
supplies.
1
2
1 2Fig. 1. World energy consumption and production: 1 — Producttion; 2 — Consumption
Study of security trends of the global society based on intelligent data analysis
Системні дослідження та інформаційні технології, 2022 № 3 9
Source: based on data in [7]
In order to quantitatively estimate the energy security of different countries
of the world the Energy Freedom Index (ES) [10] is used. It aggregates the of
three separate sub-indices, which can be the object of independent analysis:
Sub-index of energy potential – determines the established potential of
the country in terms of access to fuel and energy resources: coal, natural gas and
crude oil reserves (calculated as the value of the total explored reserves of coal,
natural gas and crude oil, determined per capita).
Sub-index of energy balance – reflects the annual balance between total
production and consumption of electricity and heat in the country (calculated as
the ratio of annual production and annual energy consumption in million metric
tons of oil equivalent).
Sub-index of energy development – demonstrates the ability of the coun-
try’s energy system to develop with the possibility of energy transition (calculated
as chain growth rate of the total installed capacity of all electricity generation fa-
cilities in the country).
Threat 2. The imbalance between biological capacity of the Earth and human
needs in biosphere (BB)
In early 2022, the world’s population reached 7.95 billion people living on the
total area 510 072 000 km2 [11]. According to the method of arithmetic extrapola-
tion the Earth population will have been 9.75 billion people by the year 2050. At
the same time, our planet has limited space and resources.
In 2018, the consumption of natural resources exceeded 1.75 times that the
Earth’s biosphere can restore, forming a significant environmental deficit (Fig. 3).
Ecological Footprint adds up all the productive areas for which a population,
a person or a product competes. It measures the ecological assets that a given
population or product requires to produce the natural resources it consumes (in-
cluding plant-based food and fiber products, livestock and fish products, timber
and other forest products, space for urban infrastructure) and to absorb its waste,
especially carbon emissions. The Ecological Footprint tracks the use of produc-
tive surface areas. Typically, these areas are: cropland, grazing land, fishing
grounds, built-up land, forest area, and carbon demand on land. On the supply
side, a city, state or nation’s biocapacity represents the productivity of its ecologi-
2
3
1
321 Fig. 2. Production of fossil resources: 1 — Crude oil; 2 — Natural gas; 3 — Coal and
lignite
M. Zgurovsky, I. Pyshnograiev
ISSN 1681–6048 System Research & Information Technologies, 2022 № 3 10
cal assets (including cropland, grazing land, forest land, fishing grounds, and
built-up land) [13].
Therefore, the increase in the ecological deficit over time can lead to irre-
versible changes in the biosphere, which will directly threaten the existence of
mankind.
For estimation of increasing threats, connected with imbalance between bio-
logical capability of the Earth and human requirements in biosphere, in terms of
demographic structure change of the world we will use the indicator which is ratio
level between biocapacity and ecological footprint consumption for a country
[12]:
– value >1 – the country is an ecological creditor;
– value <1 – the country is an ecological debtor.
Threat 3. Growing inequality between people and countries on the Earth
(GINI)
According to the World Bank, in 2018, 3 billion people live on less than $ 150 a
month [14]. And although most regions, except the Middle East and North Africa,
are showing progress in the fight against poverty, the situation remains threaten-
ing [15].
Political and military conflicts, pandemics, global corruption, terrorism, de-
pletion of resources, etc. complicate humanity’s ability to overcome poverty and
inequality. For example, [16] emphasizss that due to the restrictions imposed by
the proliferation of Covid-19, for the first time since 1993, inequalities between
countries are projected to increase (Fig. 4).
To asses quantitatively the disparity of the distribution of economic and so-
cial benefits for each of the countries under study, we will use the Gini index [17],
which reflects these characteristics.
1 2
2
1
3
4
Fig. 3. Ecological footprint trend: 1 — Ecological Reserve; 2 — Ecological Deficit; 3 —
Biocapasity; 4 — Ecological Footprint
Source: [12]
Study of security trends of the global society based on intelligent data analysis
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Threat 4. The spread of global diseases (GD)
The World Health Organization has identified the top 10 causes of death globally
in 2019, which caused 55% of 55.4 million deaths worldwide (Fig. 5) [18].
At a global level, 7 of the 10 leading causes of deaths in 2019 were non-
communicable diseases. They kill 41 million people each year, equivalent to 71%
of all deaths globally. The main types of NCD are cardiovascular diseases (such
as heart attacks and stroke), cancers, chronic respiratory diseases (such as chronic
obstructive pulmonary disease and asthma) and diabetes [19].
Fig. 4. Degree of inequality between countries
Source: [16]
Fig. 5. Top 10 causes of death globally in 2019.
Source: [18]
M. Zgurovsky, I. Pyshnograiev
ISSN 1681–6048 System Research & Information Technologies, 2022 № 3 12
Also, in the study we consider the communicable (including infectious) dis-
eases, such as: tuberculosis, HIV/AIDS, diarrhea, malaria, hepatitis, etc. They
have lower part of deaths globally, but the threat of new diseases remains. Lower
respiratory infections remained the world’s most deadly communicable disease,
ranked as the 4th leading cause of death. However, the number of deaths has gone
down substantially: in 2019 it claimed 2.6 million lives, 460 000 fewer than in
2000 [20]. Also, nearly half of the world’s population was at risk of malaria in
2020, it is estimated 41 million cases [18]. In 2020, 680 000 (480 000–1.0 mil-
lion) people died from HIV-related causes and 1.5 million (1.0–2.0 million) peo-
ple acquired HIV [21]. Also dangerous for the world community are the pandem-
ics of swine flu (2008–2009), Ebola (2014–2015), SARS-CoV-2 (from 2020),
which also contributed to the deepening of economic crises [22].
The spread of global diseases (GD) is measured in the normalized total
number of people (millions per year) who died from these diseases. For the sub-
sequent simulation, we take data on these diseases from the World Health Organi-
zation [23].
Threat 5. Information gap (IG)
Humanity is constantly generating gigantic volumes of new data and information.
There were 79 zettabytes of data generated worldwide in 2021, 90% of it is repli-
cated [24] (Fig. 6). This raises a number of challenges: how to access this infor-
mation, how to process it, and whether it is trustworthy.
To assess these challenges Information Gap is formed by following determi-
nants of the modern information society:
1. Readiness of the local ICT infrastructure (RLI). This indicator is based on
the ICT Development Index [25], it shows the degree of involvement of the popu-
lation in the consumption / generation of information and the level of exports of
services and goods of the sector, which indicates the availability of an appropriate
base. The following data sets with equal weights are used for this purpose:
Fig. 6. Annual volume of data generated, consumed, copied and stored
Source: [24]
Study of security trends of the global society based on intelligent data analysis
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– individuals using the Internet (% of population) [26];
– mobile cellular subscriptions (per 100 people) [26];
– fixed broadband subscriptions (per 100 people) [26];
– ICT goods exports (% of total goods exports) [27];
– ICT service exports (BoP, current US$) [28].
2. Number of secure Internet servers (per 1 million people) (SIS) [29]. Se-
cure servers are servers using encryption technology in internet transactions. They
provide the infrastructure for the secure exchange of generated data.
3. The vulnerability of one or another country, territory or world to the ac-
tion of cyber-attacks. This component will be measured using the Global Cyber-
security Index (GCI) [30].
4. World Press Freedom index (WPF). It is defined as the ability of journal-
ists as individuals and collectives to select, produce, and disseminate news in the
public interest independent of political, economic, legal, and social interference
and in the absence of threats to their physical and mental safety [31].
The resulting index of IG will be calculated from the formula:
ІG = 0.4RLI + 0.15SIS + 0.3GCI + 0.15WPF.
Threat 6. Corruption perception (CP)
Humanity is constantly generating gigantic volumes of new data and information.
There were 79 zettabytes of data generated worldwide in 2021, 90% of it is repli-
cated [24] (Fig. 6). This raises a number of challenges: how to access this infor-
mation, how to process it, and whether it is trustworthy.
Corruption is the biggest obstacle to the economic and social development of
society. Over last decade world has made no significant progress against corrup-
tion [32].
To estimate the influence of corruption on socio-economical and cultural de-
velopment of different countries of the world we will use the Corruption Percep-
tion Index established by the international organization Transparency Interna-
tional [33].
Corruption is connected with all spheres of society. Countries experiencing
armed conflict or authoritarianism tend to earn the lowest scores, including Vene-
zuela, Afghanistan, North Korea, Yemen, Equatorial Guinea, Libya and Turk-
menistan. Also, last research of Transparency International showed that corrup-
tion level is opposite to the level of human rights [32].
Threat 7. Limited access to drinking water (WA)
According to the data of the World Health Organization (WHO) and the UNICEF
the world is under the threat of reduced the access to drinking (potable) water and
to sanitary facilities. The fourth part of all mankind (2 billion people) does not
have access to drinking water in 2020. At the same time 46% (4.2 billion people)
lack safe sanitation. This situation persists, provided that in 2030 the Agenda for
Sustainable Development agreed to take concrete steps to achieve goals 6.1 and
6.2, i.e. to make access to water “for all” [34].
Meanwhile the world’s population grows, especially in underdeveloped
countries, the struggle for control over the remnants of drinking water resources
M. Zgurovsky, I. Pyshnograiev
ISSN 1681–6048 System Research & Information Technologies, 2022 № 3 14
increases. This phenomenon gives rise to the next, growing in time, threat to hu-
manity.
The limited access to the drinking water will be estimated by the inversed
magnitude to the indicator of the access to drinking water [35].
Threat 8. Impact of climate change and natural disaster (CN)
According to [5], the threat of climate change and the occurrence of natural disas-
ters require increased attention of society and its consolidated efforts to minimize
this factor.
Since the 1940s, the Earth’s surface temperature has been constantly rising
(Fig. 7). That extra heat is driving regional and seasonal temperature extremes,
reducing snow cover and sea ice, intensifying heavy rainfall, increase the number
of natural disasters, and changing habitat ranges for plants and animals –
expanding some and shrinking others [36].
It is necessary to accept that influence of carbon dioxide emissions on the
global temperature changing is much higher than the corresponding influence of
methane. That is why the danger of global warming could be estimated by the
amount of carbon dioxide emissions СО2 in metric tons per capita (CDE) [37].
In 2021, 432 natural disasters were registered, causing 10.5 thousand deaths
and causing $252 billion as economic damage [38].
For the quantitative estimation of the degree of vulnerability of the world
countries to the natural disasters the index of vulnerability to natural cataclysms
(NDT) is used [39]. It includes the affected from draughts, floods, hurricanes, ex-
treme temperatures, earth-quakes and tsunami.
As a result of the control over decreasing natural resources the struggle not
only between countries but also between separate groups of population can exac-
erbate. This process will cause new global conflicts.
Thus, the CN we calculate as follows:
CN = 0.3CDE + 0.7NDT.
Fig. 7. Yearly surface temperature compared to the 20th-century average from 1880–2020
Source: [36]
Study of security trends of the global society based on intelligent data analysis
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Threat 9. The state fragility (SF)
In conditions of political, social and economic instability, each country faces the
task of preserving its sovereignty and improving its position in all spheres of
functioning. Thus, in Fig. 8 shows the gradual growth of the World Uncertainty
Index and the main reasons for its peaks [40]. It is natural that these events affect
the development of each country individually.
A number of such pressures act on the fragile state. For the quantitative es-
timation of the threat in our study the Fragile States Index produced by The Fund
for Peace (FFP) is used [41]. It is based on a conflict assessment framework –
known as “CAST” – that was developed by FFP nearly a quarter-century ago for
assessing the vulnerability of states to collapse. The CAST framework was origi-
nally designed to measure this vulnerability and assess how it might affect pro-
jects in the field, and continues to be used widely by policy makers, field practi-
tioners, and local community networks. The methodology uses both qualitative
and quantitative indicators, relies on public source data, and produces quantifiable
results.
Threat 10. Increasing proliferation and global terrorism (PT)
This global threat we will consider in the terms of the debarment of the nuclear
war, terrorism and the increasing of total number of weapons. There are three
components for assessing proliferation and global terrorism:
1. The Nonproliferation Index (NPI) [42]. It defines degree of military pro-
liferation and covers four categories of policy: demilitarization or disarmament;
scientific research; state’s development; level of nonproliferation for neighbor
states.
2. The Global Terrorism Index (GTI) [43]. The GTI scores each country on
a scale from 0 to 10; where 0 represents no impact from terrorism and 10 repre-
sents the highest measurable impact of terrorism. It consists of:
Fig. 8. World Uncertainty Index
Source: [40]
M. Zgurovsky, I. Pyshnograiev
ISSN 1681–6048 System Research & Information Technologies, 2022 № 3 16
– total number of terrorist incidents in a given year;
– total number of fatalities caused by terrorists in a given year;
– total number of injuries caused by terrorists in a given year;
– total number of hostages caused by terrorists in a given year.
3. Militarization (MLT) [44]. It is also the subindex of The Global Peace In-
dex. It shows the state of the country armament and considers:
– military expenditure as a percentage of GDP;
– number of armed services personnel per 100.000 people;
– volume of transfers of major conventional weapons as recipient (imports)
per 100.000 people;
– volume of transfers of major conventional weapons as supplier (exports)
per 100.000 people;
– financial contribution to UN peacekeeping missions;
– nuclear and heavy weapons capabilities;
– ease of access to small arms and light weapons.
Thus, we obtained a comprehensive system of indicators, which is embedded
in the integrated formula:
PT = 0.4NPI + 0.2GTI + 0.4MLT.
Threat 11. Conflict intensity increasing (CI)
The number of military and paramilitary conflicts occurring at the national and
international levels has a tendency to increase. But the nature of armed conflicts
changed significantly due to the use of high-tech weapons. It should also be noted
that part of the hostilities was transferred to the digital space [42].
In our research we will consider a conflict between interstate, intrastate, sub-
state, and transstate ones. To assess the conflict intensity in the country we take
into account the two parameters.
The first one is the levels of conflicts intensity (ICB), which were proposed
Heidelberg Institute for International Conflict Research in Conflict Barometer
[45]. They are dispute, non-violent crisis, violent crisis, limited war, and war. In
the methodology of Conflict Barometer, the level of violence and the intensity
class are considered. The last three are violent conflicts, which causes deaths and
distraction of the different level.
The second is the index, which characterized the parameters of the ongoing
domestic and international conflicts (OCI). It is the subindex of The Global Peace
Index, which was introduced by The Institute for Economics and Peace Limited
[44]. It includes:
– number and duration of internal conflicts;
– number of deaths from external/internal organized conflict;
– number, duration and role in external/internal conflicts;
– relations with neighboring countries.
The quantitative value for intensity of conflicts we will take in the form:
CI = 0.4ICB + 0.6OCI.
Study of security trends of the global society based on intelligent data analysis
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This threat acts as a multiplier for the security level. This naturally follows
from the thesis that it is almost impossible to ensure the sustainable development
of the country by participating in the armed conflicts.
Given the above, we obtained a system of indicators that identify 11 threats
identified in the study. Table 1 shows their relevance to the global challenges
listed in the previous section.
T a b l e 1 . The connection between the 11 global treats and global challenges
Global Threat
World Economic
Forum
Millennium
Project
United Nations
ES
The global decrease
in energy security
Natural resource
crises
Energy
GOAL 7: Affordable and
Clean Energy
BB
The imbalance
between biological
capacity of the Earth
and human needs
in biosphere
Biodiversity loss,
Human
environmental
damage
—
GOAL 2:
Zero Hunger
GINI
Growing inequality
between people and
countries on the Earth
Social cohesion
erosion
Population and resources,
Rich-poor gap
GOAL 1: No Poverty,
GOAL 10: Reduced
Inequality
GD
The spread of global
diseases
Infectious
diseases
Health issues
GOAL 3: Good Health
and Well-being
IG Information gap —
Global convergence of IT,
Science and technology
—
CP
Corruption
perception
Social cohesion
erosion
Population
and resources
—
WA
Limited access to
drinking water
— Clean water
GOAL 6: Clean
Water and Sanitation
CN
Impact of climate
change and natural
disaster
Climate action
failure,
Extreme weather
Sustainable
development
and climate change
GOAL 13:
Climate Action
SF The state fragility
Livelihood crises,
Debt crises
Democratization, Global
foresight and decision
making, Status of woman,
Education and learning
GOAL 8: Decent Work
and Economic Growth,
GOAL 16: Peace and
Justice Strong Institutions
PT
Increasing
proliferation
and global terrorism
—
Peace and conflict,
Education and learning
GOAL 16: Peace
and Justice Strong Insti-
tutions
CI
Conflict intensity
increasing
Geoeconomic
confrontation
Peace and conflict,
Transnational organized
crime, Global ethics
GOAL 16: Peace
and Justice Strong Insti-
tutions
MODELING THE TOTAL IMPACT OF THE AGGREGATE OF 11 GLOBAL
THREATS ON DIFFERENT COUNTRIES AND GROUPS OF COUNTRIES
Let’s determine the vulnerability of different countries and groups of countries to
the impact of a set of 11 major threats. Quantitative data on each of the 11 threats
will be obtained from the global databases specified in the description of these
threats in section 2. To determine the groups of countries with close values of
vulnerabilities to the impact of the 11 main threats, we use the partition algorithm
M. Zgurovsky, I. Pyshnograiev
ISSN 1681–6048 System Research & Information Technologies, 2022 № 3 18
of clustering the multivariate time-series with the global alignment kernel dis-
tance [46]. It takes into the account all history of the threats and minimize differ-
ences of their values and behaviour.
Let’s associate each country j with a vector jTr for year i:
i
j
i
j CIPTSFCNWACPIGGDGINIBBES ,, , , , , , , , , Tr ,
elements of which characterize the degree of manifestation of corresponding 11
threats presented in Section 2, 134..1,2021..2005 ji .
Considering the fact that all the measured data for components of vector jTr
are presented in different units of measurement, they have different physical
meaning and vary in different ranges, they have been reduced to the normalized
form, so that they vary in the range (0.1). In this case, the value 0 corresponds to
the minimum value of the threat, and the value 1 corresponds to the maximum of
this threat. In the study the logistic normalization is used [2].
The security index Isec of each country with a value ||Tr||
i
j is calculated as
the Minkowski norm of the vector
i
jTr for the jth country, composed of nor-
malized threats. After normalization the security index Isec for each country is
defined as the Minkowski norm:
p
ps
k
i
js
i
j
i
jsec
i
jkSI
1
10
1
,11 )( ,
with parameter 3p , where 134..1,2021..2005 jj , i
jS is the vector of the
normilized threats
i
jTr .
Thus, secI defines the degree of remoteness from the influence of the set
of 11 threats. Based on the calculated norms of the vector of threats ||Tr|| j
for each country j, we obtained an order relation between clusters of countries
(Table 2):
,
)K(
Tr
)K(
Tr
KK
2021
K
2021
pK
j
jj
k
p
jk cardcard
jk
where kK , jK are the pair of obtained clusters.
From Table 2 it follows that Cluster 1 includes the group of countries most
successful from the safety standpoint, for which the degree of remoteness from
the set of 11 global threats is the greatest during 2005–2021. And vice versa,
Cluster 5 includes the most vulnerable countries. For these countries the degree of
remoteness from the set of 11 global threats is minimal.
Based on the data presented in Table 2, Fig. 9 illustrates the safety levels for
different countries and regions of the world.
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T a b l e 2 . Countries degree remoteness from the Set of Threats Based on
Clustering Analysis, 2005‐2021*
Total influence of the set of global threats
on different countries
C
ou
nt
ry
R
an
k
fo
r
20
21
C
ou
nt
ry
, G
D
P
pe
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1 2 3 4 5 6 7 8 9 10 11 12 13 14
Cluster 1 (Very high degree of remoteness during 2005–2021)
1 Denmark
($ 56202.17)
0.607 0.544 27.700 0.537 0.837 88.000 96.731 0.580 18.800 0.773 0.793 1.426
2
New Zealand
($ 40218.39)
0.724 1.667 NA 0.566 0.749 88.000 100.000 0.528 18.400 0.678 0.837 1.417
4
Australia
($ 58029.52)
3.552 1.619 34.300 0.566 0.771 73.000 NA 0.466 21.800 0.582 0.828 1.372
6
Canada
($ 42258.69)
1.846 1.827 33.300 0.544 0.798 74.000 99.039 0.468 21.700 0.636 0.837 1.369
7
Uruguay
($ 15044.64)
0.583 7.405 40.200 0.537 0.673 73.000 NA 0.612 35.900 0.592 0.838 1.367
Cluster 2 (High degree of remoteness during 2005–2021)
3 Austria
($ 43346.43)
0.322 0.441 30.200 0.544 0.784 74.000 98.901 0.557 26.100 0.693 0.836 1.374
5
Malta
($ 25005.76)
0.013 0.089 31.000 0.533 0.738 54.000 100.000 0.625 36.200 0.711 0.955 1.371
10
Belgium
($ 40438.92)
0.261 0.111 27.200 0.544 0.793 73.000 99.914 0.542 31.000 0.671 0.817 1.359
14
Finland
($ 44778.87)
0.489 1.884 27.700 0.546 0.846 88.000 99.638 0.544 70.400 0.696 0.794 1.345
15
Portugal
($ 19771.58)
0.308 0.284 32.800 0.535 0.779 62.000 95.354 0.595 26.800 0.668 0.836 1.328
17
Czech Rep.
($ 18984.64)
0.592 0.407 25.300 0.542 0.784 54.000 97.882 0.468 39.300 0.696 0.836 1.317
18
Slovenia
($ 22899.36)
0.553 0.410 24.400 0.550 0.730 57.000 98.274 0.544 28.200 0.742 0.718 1.292
19
Slovakia
($ 17360.71)
0.403 0.590 23.200 0.547 0.779 52.000 99.238 0.575 39.000 0.681 0.718 1.274
21
Germany ($
41315.31)
0.370 0.320 31.700 0.541 0.859 80.000 99.993 0.536 24.800 0.614 0.652 1.240
22
Italy
($ 29359.93) 0.239 0.191 35.200 0.559 0.748 56.000 95.824 0.585 45.200 0.563 0.836 1.236
23
Latvia
($ 15583.93) 0.557 1.273 34.500 0.555 0.760 59.000 96.289 0.612 44.000 0.656 0.717 1.231
25
Mauritius
($ 9058.21) 0.059 0.207 36.800 0.539 0.673 54.000 NA 0.624 38.100 0.608 0.838 1.216
26
Lithuania
($ 17213.81) 0.134 0.792 35.300 0.555 0.786 61.000 94.924 0.609 38.700 0.502 0.745 1.215
27
Estonia
($ 19767.08) 0.105 1.165 30.800 0.538 0.836 74.000 95.761 0.498 39.500 0.640 0.657 1.207
M. Zgurovsky, I. Pyshnograiev
ISSN 1681–6048 System Research & Information Technologies, 2022 № 3 20
1 2 3 4 5 6 7 8 9 10 11 12 13 14
30
Japan
($ 34813.22) 0.137 0.129 32.900 0.537 0.786 73.000 98.565 0.480 32.200 0.603 0.658 1.183
38
Hungary
($ 14368.69) 0.418 0.665 30.000 0.541 0.758 43.000 92.589 0.593 51.100 0.684 0.657 1.151
41
Oman
($ 15743.22) 2.742 0.227 NA 0.587 0.619 52.000 90.557 0.479 50.400 0.484 0.718 1.125
45
South Korea
($ 31327.41)
0.128 0.102 31.400 0.562 0.784 62.000 99.191 0.490 32.500 0.475 0.552 1.064
Cluster 3 (Medium degree of remoteness during 2005–2021)
8 Norway
($ 75017.16)
5.247 1.219 27.700 0.544 0.800 85.000 98.643 0.559 16.600 0.453 0.705 1.366
9 Ireland
($ 78732.55)
0.283 0.585 30.600 0.561 0.842 74.000 97.329 0.550 22.200 0.570 0.838 1.361
11 Netherlands
($ 46345.35)
0.317 0.137 29.200 0.564 0.871 82.000 99.972 0.534 24.100 0.407 0.836 1.358
12 Switzerland
($ 85685.29)
0.511 0.229 33.100 0.565 0.826 84.000 94.248 0.604 19.900 0.407 0.837 1.356
13 Luxembourg
($ 104879.26)
0.049 0.093 34.200 0.544 0.809 81.000 99.459 0.470 21.100 0.401 0.955 1.348
16 Singapore
($ 58056.81)
0.013 0.010 NA 0.550 0.797 85.000 100.000 0.536 26.600 0.522 0.810 1.320
20 Sweden
($ 51539.56)
0.590 1.427 29.300 0.542 0.834 85.000 99.752 0.619 21.400 0.563 0.609 1.242
24 France
($ 35785.97)
0.539 0.538 32.400 0.563 0.806 71.000 99.249 0.597 32.500 0.307 0.701 1.227
28 Poland
($ 14660.79)
0.630 0.396 30.200 0.536 0.755 56.000 98.325 0.540 43.100 0.574 0.746 1.204
29 Barbados
($ 13595.03)
0.119 0.047 NA 0.555 0.494 65.000 NA 0.605 47.000 0.527 0.955 1.196
32 Spain
($ 24939.19)
0.279 0.350 34.300 0.555 0.775 61.000 99.587 0.584 44.800 0.528 0.703 1.177
36 United Kingdom
($ 43020.2)
0.660 0.246 35.100 0.536 0.823 78.000 99.822 0.585 41.500 0.332 0.648 1.160
46 Cypru s
($ 26372.65)
0.046 0.053 31.200 0.544 0.762 53.000 99.765 0.575 57.400 0.528 0.606 1.064
52 Chile
($ 12954.41)
0.256 0.777 44.900 0.555 0.640 67.000 98.771 0.494 44.100 0.414 0.613 1.021
58 United States
($ 58203.38)
1.103 0.417 41.500 0.548 0.847 67.000 97.326 0.289 44.600 0.284 0.468 0.936
Cluster 4 (Low degree of remoteness during 2005–2021)
31
Fiji
($ 4911.08)
0.140 0.878 30.100 0.549 0.464 55.000 NA 0.228 16.200 0.528 0.884 1.178
33
Croatia
($ 12984.7)
0.429 0.730 28.900 0.564 0.701 47.000 NA 0.607 49.800 0.610 0.707 1.172
34
Romania
($ 10844.53)
0.538 0.887 34.800 0.545 0.709 45.000 81.989 0.613 51.000 0.559 0.808 1.168
35
Costa Rica
($ 12105.93)
0.428 0.614 49.300 0.602 0.657 58.000 80.516 0.558 42.500 0.530 0.795 1.163
37
Malaysia
($ 10631.51)
1.154 0.507 41.100 0.557 0.713 48.000 93.818 0.486 56.900 0.637 0.807 1.154
42
Cape Verde
($ 2935.32)
0.051 0.259 42.400 0.581 0.420 58.000 NA 0.652 64.200 0.473 0.955 1.114
43
Bhutan
($ 2879.64)
1.044 0.965 37.400 0.525 0.394 68.000 36.648 0.639 68.300 0.673 0.745 1.085
47
Argentina
($ 11344.41)
0.916 1.866 42.300 0.568 0.570 38.000 NA 0.588 50.100 0.482 0.767 1.062
48
Guyana
($ 9250.3)
0.005 21.338 NA 0.555 0.430 39.000 NA 0.490 66.100 0.642 0.606 1.061
49
Botswana
($ 6299.21)
0.589 1.394 53.300 0.421 0.560 55.000 NA 0.609 57.000 0.570 0.838 1.058
Study of security trends of the global society based on intelligent data analysis
Системні дослідження та інформаційні технології, 2022 № 3 21
1 2 3 4 5 6 7 8 9 10 11 12 13 14
51
Kazakhstan
($ 10974.96)
2.603 0.722 27.800 0.568 0.607 37.000 89.335 0.496 61.200 0.495 0,606 1,029
53
Serbia
($ 6549.35)
0.877 0.547 34.500 0.558 0.671 38.000 75.038 0.549 67.400 0.630 0.606 0.989
54
Moldova
($ 3250.31)
0.023 0.667 26.000 0.540 0.583 36.000 74.071 0.611 67.000 0.598 0.552 0.964
55
North Macedonia
($ 5115.92) 0.395 0.482 33.000 0.480 0.622 39.000 76.833 0.619 64.500 0.645 0.564 0.950
56
Montenegro
($ 6512.62)
0.665 0.594 36.800 0.562 0.592 46.000 85.072 0.608 58.500 0.515 0.564 0.949
57
Dominican
Republic
($ 7677.71)
0.060 0.378 39.600 0.563 0.567 30.000 NA 0.448 64.700 0.603 0.718 0.942
59
Viet Nam
($ 2655.77)
0.734 0.413 35.700 0.537 0.677 39.000 NA 0.389 63.300 0.505 0.658 0.931
60
Jamaica
($ 4539)
0.039 0.256 45.500 0.561 0.498 44.000 NA 0.570 61.200 0.625 0.613 0.920
61
Ghana
($ 2018.62)
1.479 0.648 43.500 0.451 0.588 43.000 41.410 0.596 63.900 0.577 0.656 0.919
62
Jordan
($ 4028.96)
0.055 0.086 33.700 0.539 0.487 49.000 85.701 0.637 76.800 0.582 0.543 0.911
64
Greece
($ 17323.82)
0.280 0.285 33.100 0.537 0.700 49.000 100.000 0.567 54.500 0.459 0.459 0.896
65
Albania
($ 4389.9)
0.395 0.526 30.800 0.564 0.549 35.000 70.675 0.363 59.000 0.549 0.564 0.887
66
Benin
($ 1214.66)
0.000 0.572 37.800 0.431 0.476 42.000 NA 0.637 72.800 0.501 0.690 0.886
67
Cote D`Ivoire
($ 2313.79)
0.877 1.281 37.200 0.433 0.523 36.000 35.205 0.669 90.700 0.602 0.596 0.885
68
Bosnia and
Herzegovina
($ 5433.15)
0.609 0.514 33.000 0.531 0.499 35.000 88.869 0.464 72.900 0.548 0.611 0.885
71
Senegal
($ 1364.84)
0.048 0.785 38.100 0.531 0.432 43.000 NA 0.594 73.400 0.626 0.561 0.863
72
Tanzania
($ 1061.17)
0.415 0.839 40.500 0.420 0.501 39.000 NA 0.533 79.300 0.604 0.698 0.860
74
Paraguay
($ 5670.75)
1.007 3.278 43.500 0.593 0.516 30.000 64.084 0.323 66.400 0.449 0.564 0.855
75
Indonesia
($ 3756.91)
1.971 0.722 37.300 0.519 0.595 38.000 NA 0.602 67.600 0.480 0.505 0.854
76
Tunisia
($ 3780.6)
0.303 0.339 32.800 0.537 0.626 44.000 79.286 0.636 69.200 0.497 0.455 0.849
77
Armenia
($ 4021.05)
0.291 0.324 25.200 0.599 0.537 49.000 86.911 0.611 69.800 0.454 0.388 0.844
78
Azerbaijan
($ 5083.38)
4.071 0.382 26.600 0.565 0.575 30.000 88.323 0.621 75.100 0.460 0.352 0.839
79
Ecuador
($ 5317.68)
1.862 1.108 47.300 0.576 0.427 36.000 66.827 0.536 71.200 0.525 0.560 0.839
80
Guinea
($ 984.01)
0.136 1.116 29.600 0.447 0.348 25.000 NA 0.667 97.400 0.506 0.558 0.838
81
Georgia
($ 4447.66)
0.300 0.545 34.500 0.530 0.641 55.000 66.355 0.611 72.600 0.476 0.459 0.835
82
Sierra Leone
($ 623.89)
0.102 0.880 35.700 0.462 0.361 34.000 10.621 0.661 83.400 0.631 0.562 0.835
84
Kyrgyzstan
($ 1100.04)
0.659 0.779 29.000 0.582 0.488 27.000 70.090 0.644 76.400 0.748 0.365 0.832
85
Belarus
($ 6234.82)
0.081 0.649 24.400 0.556 0.565 41.000 94.611 0.561 68.000 0.526 0.362 0.805
86
Algeria
($ 3834.44)
2.702 0.243 27.600 0.571 0.415 33.000 72.381 0.603 73.600 0.413 0.389 0.794
M. Zgurovsky, I. Pyshnograiev
ISSN 1681–6048 System Research & Information Technologies, 2022 № 3 22
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88 Belize
($ 3968.49)
0.536 0.446 53.300 0.571 0.447 NA NA 0.609 64.200 0.436 0.500 0.789
89
Brazil
($ 8228.78)
0.945 3.327 48.900 0.551 0.627 38.000 85.766 0.545 75.800 0.605 0.367 0.783
90
El Salvador
($ 3632.45)
0.219 0.290 38.800 0.587 0.434 34.000 NA 0.425 71.600 0.464 0.563 0.769
91
Morocco
($ 2818.77)
0.069 0.450 39.500 0.561 0.589 39.000 79.950 0.530 71.500 0.511 0.443 0.766
94
Gambia
($ 692.21)
0.003 0.496 35.900 0.518 0.400 37.000 44.715 0.562 80.500 0.507 0.563 0.752
97
Ukraine
($ 2344.36)
0.774 1.130 25.600 0.523 0.622 32.000 89.020 0.607 69.800 0.447 0.273 0.716
98
Egypt
($ 4028.42)
0.898 0.199 31.500 0.576 0.558 33.000 NA 0.637 85.000 0.400 0.322 0.712
101
Tajikistan
($ 1199.06)
0.844 0.422 34.000 0.589 0.323 25.000 55.237 0.652 75.100 0.628 0.302 0.705
105
Colombia
($ 5892.48)
2.825 1.852 54.200 0.574 0.548 39.000 73.009 0.601 79.300 0.269 0.291 0.685
108
Israel
($ 37488.45)
0.379 0.039 38.600 0.539 0.770 59.000 99.321 0.550 75.100 0.249 0.237 0.676
109
Nicaragua
($ 1922.35)
0.244 1.537 46.200 0.575 0.336 20.000 55.516 0.404 77.100 0.625 0.404 0.675
111
Russian
($ 9666.81)
2.449 1.265 35.300 0.529 0.689 29.000 76.104 0.503 73.600 0.217 0.264 0.655
113
Iran
($ 4883.6)
1.604 0.223 40.900 0.582 0.589 25.000 93.984 0.244 84.500 0.318 0.285 0.632
114
Saudi Arabia
($ 18691.25)
3.415 0.082 NA 0.606 0.620 53.000 NA 0.478 69.700 0.369 0.167 0.630
116
Venezuela
($ NA)
2.310 1.154 44.800 0.576 0.373 14.000 NA 0.594 92.600 0.386 0.292 0.619
117
Mexico
($ 8909.68)
0.683 0.485 45.400 0.644 0.612 31.000 43.026 0.572 69.900 0.450 0.197 0.601
118
Lebanon
($ 5382.34)
0.010 0.085 31.800 0.564 0.413 24.000 47.700 0.496 89.000 0.262 0.299 0.565
121
Turkey
($ 12038.63)
0.306 0.390 41.900 0.566 0.616 38.000 NA 0.591 79.700 0.404 0.145 0.530
129
Burkina Faso
($ 731.52)
0.034 0.792 47.300 0.473 0.416 42.000 NA 0.535 87.100 0.542 0.185 0.472
130
Cameroon
($ 1419.68)
1.585 1.313 46.600 0.416 0.393 27.000 NA 0.659 97.200 0.448 0.113 0.464
132
Syria
($ NA)
0.422 0.364 37.500 0.592 0.344 13.000 NA 0.607 110.700 0.325 0.059 0.440
133
Yemen
($ NA)
0.803 0.737 36.700 0.579 0.234 16.000 NA 0.595 111.700 0.280 0.066 0.438
Cluster 5 (Very low degree of remoteness during 2005–2021)
39 Mongolia
($ 4126.7)
5.942 1.944 32.700 0.539 0.461 35.000 30.061 0.208 52.300 0.574 0.795 1.137
50
Congo
($ 1608.78)
9.043 8.538 48.900 0.411 0.303 21.000 45.897 0.432 92.400 0.479 0.661 1.045
63
Zambia
($ 1273.88)
0.681 1.373 57.100 0.403 0.455 33.000 NA 0.645 84.900 0.621 0.694 0.905
69
Angola
($ 3168.25)
9.381 2.131 51.300 0.403 0.307 29.000 NA 0.534 89.000 0.563 0.506 0.873
70 Bolivia.
($ 2983.03) 2.081 4.583 43.600 0.560 0.392 30.000 NA 0.375 74.900 0.515 0.564 0.873
73 Namibia
($ 4047.86) 0.134 2.519 59.100 0.407 0.395 49.000 NA 0.307 64.300 0.531 0.795 0.859
83 Madagascar
($ 442.19) 0.160 2.324 42.600 0.492 0.328 26.000 20.539 0.389 79.500 0.643 0.739 0.832
Study of security trends of the global society based on intelligent data analysis
Системні дослідження та інформаційні технології, 2022 № 3 23
1 2 3 4 5 6 7 8 9 10 11 12 13 14
87 Peru
($ 5792.19) 0.980 1.525 43.800 0.578 0.529 36.000 51.264 0.471 71.400 0.402 0.560 0.792
92 Thailand
($ 6198.41) 0.456 0.540 35.000 0.566 0.668 35.000 NA 0.303 70.900 0.501 0.430 0.763
93 Nepal
($ 1028.46) 0.252 0.519 32.800 0.559 0.453 33.000 17.576 0.416 82.200 0.499 0.549 0.754
95 China
($ 10370.36) 0.812 0.243 38.200 0.558 0.684 45.000 NA 0.273 68.900 0.390 0.427 0.745
96 Laos
($ 2554.43) 1.191 0.922 38.800 0.573 0.297 30.000 17.682 0.258 76.000 0.475 0.564 0.732
99 Togo
($ 626.62) 0.002 0.569 42.400 0.447 0.369 30.000 19.561 0.661 85.100 0.418 0.549 0.707
100 Malawi
($ 394) 0.455 0.767 38.500 0.439 0.353 35.000 NA 0.345 83.200 0.501 0.694 0.707
102 Rwanda
($ 834.39) 0.168 0.516 43.700 0.413 0.449 53.000 12.103 0.663 85.000 0.452 0.445 0.701
103 Guatemala
($ 4126.21) 0.263 0.530 48.300 0.598 0.371 25.000 55.834 0.283 79.400 0.471 0.511 0.691
104 Sri Lanka
($ 4052.75) 0.157 0.300 39.300 0.597 0.487 37.000 NA 0.267 80.500 0.421 0.446 0.686
106 Honduras
($ 2223.45) 0.283 1.103 48.200 0.593 0.290 23.000 NA 0.404 79.400 0.456 0.503 0.682
107 South Africa
($ 5659.21) 1.025 0.262 63.000 0.355 0.642 44.000 NA 0.451 70.000 0.534 0.416 0.682
110 Lesotho
($ 982.97) 0.279 0.516 44.900 0.375 0.339 38.000 28.906 0.243 77.900 0.567 0.641 0.673
112 Bangladesh
($ 1643.67) 0.694 0.459 32.400 0.513 0.492 26.000 58.512 0.315 85.000 0.515 0.388 0.636
115 Cambodia
($ 1376.41) 0.204 0.796 NA 0.491 0.354 23.000 27.758 0.375 80.600 0.488 0.604 0.626
119 Uganda
($ 891.3) 0.343 0.441 42.700 0.378 0.422 27.000 16.648 0.639 92.900 0.513 0.316 0.564
120 India
($ 1811.68) 0.523 0.368 35.700 0.487 0.590 40.000 NA 0.358 77.000 0.382 0.250 0.534
122 Mozambique
($ 574.6) 3.192 1.962 54.000 0.292 0.312 26.000 NA 0.315 93.900 0.481 0.256 0.520
123 Nigeria
($ 2396.04) 3.467 0.632 35.100 0.368 0.497 24.000 21.669 0.619 98.000 0.543 0.108 0.517
124 Pakistan
($ 1446.81) 0.507 0.433 29.600 0.479 0.424 28.000 35.839 0.508 90.500 0.239 0.233 0.504
125 Kenya
($ 1559.55) 0.238 0.468 40.800 0.434 0.514 30.000 NA 0.335 89.200 0.391 0.342 0.492
126 CAR
($ 414.4) 0.174 6.237 56.200 0.369 0.239 24.000 6.183 0.648 107.000 0.467 0.094 0.491
127 Philippines
($ 3269.67) 0.315 0.351 42.300 0.476 0.628 33.000 47.465 0.205 82.400 0.390 0.230 0.482
128 Niger
($ 522.56) 0.785 0.843 37.300 0.482 0.302 31.000 NA 0.343 96.000 0.374 0.318 0.482
31 Mali
($ 781.73) 0.222 1.151 36.100 0.444 0.353 29.000 NA 0.616 96.600 0.408 0.126 0.448
134 Ethiopia
($ 826.95) 0.376 0.547 35.000 0.474 0.332 39.000 12.577 0.523 99.000 0.417 0.111 0.403
NA – data not available; critical threats are indicated by red color;
(<) – a lower value corresponds to a higher threat. (>) – a higher value corresponds to a higher threat;
*latest available data;
** Data Source: [47].
As presented in Table 3, the common trait of the ten leaders is high Isec, and
low level of threats. E.g., the group leaders, Denmark, New Zealand, and Austria,
have the best indicators among all the group countries. However, half of the list
have a relatively low level of energy security (ES), this threat is critical for them.
It is also necessary to pay attention to the low biological balance (BB) of Belgium
and Malta, and the high level of inequality (GINI) of Uruguay.
M. Zgurovsky, I. Pyshnograiev
ISSN 1681–6048 System Research & Information Technologies, 2022 № 3 24
* For each country critical values of the threats indicators are highlighted by red color
The G7 countries are characterized by a high and medium level of national
security and therefore a low vulnerability to the impact of 11 global threats (Ta-
ble 4). In some sense, an exception is the United States, for which the threats of
inequality (GINI), global warming and natural disasters (CN), conflicts (CI), and
the prolifiration (PT) are very critical. In Japan, there are clearly threats to disrupt
the biological balance (BB) and energy security (ES), which is natural in connec-
tion with the geographic location and the great density of the population.
T a b l e 4 . The level of national security of the G-7 countries*
Rank
Isec
ISO Country Isec ES BB GINI GD IG CP WA CN SF PT CI
6 CAN Canada 1.369 1.846 1.827 33.300 0.544 0.798 74.000 99.039 0.468 21.700 0.636 0.837
21 DEU Germany 1.240 0.370 0.320 31.700 0.541 0.859 80.000 99.993 0.536 24.800 0.614 0.652
22 ITA Italy 1.236 0.239 0.191 35.200 0.559 0.748 56.000 95.824 0.585 45.200 0.563 0.836
24 FRA France 1.227 0.539 0.538 32.400 0.563 0.806 71.000 99.249 0.597 32.500 0.307 0.701
30 JPN Japan 1.183 0.137 0.129 32.900 0.537 0.786 73.000 98.565 0.480 32.200 0.603 0.658
36 GBR United
Kingdom 1.160 0.660 0.246 35.100 0.536 0.823 78.000 99.822 0.585 41.500 0.332 0.648
58 USA United
States 0.936 1.103 0.417 41.500 0.548 0.847 67.000 97.326 0.289 44.600 0.284 0.468
* For each country critical values of the threats indicators are highlighted by red color
T a b l e 3 . Top 10 countries with the highest level of national security*
Rank
Isec
ISO Country Isec ES BB GINI GD IG CP WA CN SF PT CI
1 DNK Denmark 1.426 0.607 0.544 27.700 0.537 0.837 88.000 96.731 0.580 18.800 0.773 0.793
2 NZL New
Zealand 1.417 0.724 1.667 NA 0.566 0.749 88.000 100.000 0.528 18.400 0.678 0.837
3 AUT Austria 1.374 0.322 0.441 30.200 0.544 0.784 74.000 98.901 0.557 26.100 0.693 0.836
4 AUS Australia 1.372 3.552 1.619 34.300 0.566 0.771 73.000 NA 0.466 21.800 0.582 0.828
5 MLT Malta 1.371 0.013 0.089 31.000 0.533 0.738 54.000 100.000 0.625 36.200 0.711 0.955
6 CAN Canada 1.369 1.846 1.827 33.300 0.544 0.798 74.000 99.039 0.468 21.700 0.636 0.837
7 URY Uruguay 1.367 0.583 7.405 40.200 0.537 0.673 73.000 NA 0.612 35.900 0.592 0.838
8 NOR Norway 1.366 5.247 1.219 27.700 0.544 0.800 85.000 98.643 0.559 16.600 0.453 0.705
9 IRL Ireland 1.361 0.283 0.585 30.600 0.561 0.842 74.000 97.329 0.550 22.200 0.570 0.838
10 BEL Belgium 1.359 0.261 0.111 27.200 0.544 0.793 73.000 99.914 0.542 31.000 0.671 0.817
Fig. 9. Countries safety levels – degree of remoteness from the Set of Threats (Based on
Clustering Analysis)
Study of security trends of the global society based on intelligent data analysis
Системні дослідження та інформаційні технології, 2022 № 3 25
The last 10 countries by the Isec are characterized by a very high vulnerabil-
ity to the impact of 11 global threats (Table 5). These are countries with hostilities
on their territory (CI), low stability of the state (SF) and a high level of
proliferation (PT).
Table 5. Last 10 countries with the lowest level of national security*
Rank
Isec ISO Country Isec ES BB GINI GD IG CP WA CN SF PT CI
125 KEN Kenya 0,492 0,238 0,468 40,800 0,434 0,514 30,000 NA 0,335 89,200 0,391 0,342
126 CAF
Central
African
Republic
0.491 0.174 6.237 56.200 0.369 0.239 24.000 6.183 0.648 107.000 0.467 0.094
127 PHL Philippines 0.482 0.315 0.351 42.300 0.476 0.628 33.000 47.465 0.205 82.400 0.390 0.230
128 NER Niger 0.482 0.785 0.843 37.300 0.482 0.302 31.000 NA 0.343 96.000 0.374 0.318
129 BFA Burkina
Faso 0.472 0.034 0.792 47.300 0.473 0.416 42.000 NA 0.535 87.100 0.542 0.185
130 CMR Cameroon 0.464 1.585 1.313 46.600 0.416 0.393 27.000 NA 0.659 97.200 0.448 0.113
131 MLI Mali 0.448 0.222 1.151 36.100 0.444 0.353 29.000 NA 0.616 96.600 0.408 0.126
132 SYR Syria 0.440 0.422 0.364 37.500 0.592 0.344 13.000 NA 0.607 110.700 0.325 0.059
133 YEM Yemen 0.438 0.803 0.737 36.700 0.579 0.234 16.000 NA 0.595 111.700 0.280 0.066
134 ETH Ethiopia 0,403 0,376 0,547 35,000 0,474 0,332 39,000 12,577 0,523 99,000 0,417 0,111
* For each country critical values of the threats indicators are highlighted by red color
As for Ukraine, the most critical for it is the threat of increasing armed con-
flicts (CI), which prevents the sustainable development of its territory (Table 6).
Among the neighboring countries, Ukraine ranks second to last (the last is Russia).
* For each country critical values of the threats indicators are highlighted by red color
POSSIBLE SCENARIOS OF WORLD DEVELOPMENT DURING “CONFLICT XXI”
According to the results of the above-mentioned studies, the following scenarios
of the development of global society during the conflict of the 21st century and
after its end can be assumed:
T a b l e 6 . Ukraine in the European context*
Rank
Isec ISO Country Isec ES BB GINI GD IG CP WA CN SF PT CI
19 SVK Slovakia 1,274 0,403 0,590 23,200 0,547 0,779 52,000 99,238 0,575 39,000 0,681 0,718
21 DEU Germany 1,240 0,370 0,320 31,700 0,541 0,859 80,000 99,993 0,536 24,800 0,614 0,652
22 ITA Italy 1,236 0,239 0,191 35,200 0,559 0,748 56,000 95,824 0,585 45,200 0,563 0,836
24 FRA France 1,227 0,539 0,538 32,400 0,563 0,806 71,000 99,249 0,597 32,500 0,307 0,701
28 POL Poland 1,204 0.630 0.396 30.200 0.536 0.755 56.000 98.325 0.540 43.100 0.574 0.746
34 ROU Romania 1.168 0.538 0.887 34.800 0.545 0.709 45.000 81.989 0.613 51.000 0.559 0.808
36 GBR United
Kingdom 1.160 0.660 0.246 35.100 0.536 0.823 78.000 99.822 0.585 41.500 0.332 0.648
38 HUN Hungary 1.151 0.418 0.665 30.000 0.541 0.758 43.000 92.589 0.593 51.100 0.684 0.657
54 MDA Moldova,
Republic of 0.964 0.023 0.667 26.000 0.540 0.583 36.000 74.071 0.611 67.000 0.598 0.552
85 BLR Belarus 0.805 0.081 0.649 24.400 0.556 0.565 41.000 94.611 0.561 68.000 0.526 0.362
97 UKR Ukraine 0.716 0.774 1.130 25.600 0.523 0.622 32.000 89.020 0.607 69.800 0.447 0.273
111 RUS Russian
Federation 0.655 2.449 1.265 35.300 0.529 0.689 29.000 76.104 0.503 73.600 0.217 0.264
M. Zgurovsky, I. Pyshnograiev
ISSN 1681–6048 System Research & Information Technologies, 2022 № 3 26
Pessimistic scenario. As a result of the conducted research, the question
arises: what does the 21st century have in store for civilization? What is the na-
ture of the final state of civilization as a system? What should happen to world
civilization, in particular, in the 22nd century? Perhaps the final cycle of some
global evolutionary chain of human development is beginning?
The answer to this question can be found in the research of two outstanding
scientists of the last century: Vernadskyi [48] and Moiseev [49]. Independently of
each other, they formulated a very close idea: if humanity on a planetary scale
does not radically change its behaviour (using its mind and its work to self-
destruct), then in the middle of the 21st century conditions may arise in which
people will not be able to exist. Such conclusions were made for the paradigm
constant throughout the history of mankind: “unlimited and growing consump-
tion” and for the technosphere (a set of technological ways of life), unfriendly to
human habitation, which developed in the 19th and early 21st centuries.
An optimistic scenario. If humanity can change the paradigm of its behav-
iour on a planetary scale, for example, to “harmonious coexistence” and radically
transform the technosphere into a “nature-like” one (a human-friendly environ-
ment based on the convergence of nano-, bio-informational, cognitive and socio-
humanitarian technologies [50]) , then the regularity revealed for the previous
paradigm of the development of systemic world conflicts is not justified for the
new paradigm. And this, in turn, will allow humanity to continue its creative mis-
sion on planet Earth.
CONCLUSIONS
The study analyzed the global challenges and problems of humanity. According
to the results of the analysis, the set of global threats to sustainable development
have been defined.
Based on the provided global modelling, 5 clusters of countries were identi-
fied according to their remoteness from these threats during 2005–2021. Using
the obtained results, critical threats were identified for each country. Groups of
countries (Top10, Last10, G7, Ukraine in the European context) were analyzed
and their features were identified.
Assumptions are made about possible pessimistic and optimistic scenarios
for the development of the world during the “Conflict XXI” and after it.
These results can be used to study and model the degree of cultural and civi-
lizational gaps in the world.
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Received 22.07.2022
INFORMATION ON THE ARTICLE
Ivan O. Pyshnograiev, ORCID: 0000-0002-3346-8318, National Technical University of
Ukraine “Igor Sikorsky Kyiv Polytechnic Institute”, Ukraine, e-mail: pyshno-
graiev@gmail.com
Michael Z. Zgurovsky, ORCID: 0000-0001-5896-7466, National Technical University
of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute”, Ukraine, e-mail: zgu-
rovsm@hotmail.com
ДОСЛІДЖЕННЯ ТЕНДЕНЦІЙ БЕЗПЕКИ ГЛОБАЛЬНОГО СУСПІЛЬСТВА
НА ОСНОВІ ІНТЕЛЕКТУАЛЬНОГО АНАЛІЗУ ДАНИХ / М.З. Згуровський,
І.О. Пишнограєв
Анотація. Присвячено застосуванню методології системного аналізу та
інтелектуального аналізу даних до однієї з найактуальніших проблем
сучасності: дослідження безпеки глобального суспільства в конфліктному
світі. Розглянуто комплекс глобальних загроз, актуальних для першої полови-
ни ХХІ ст. Ці загрози були визначені Організацією Об’єднаних Націй,
Всесвітньою організацією охорони здоров’я, Всесвітнім економічним фору-
мом та іншими авторитетними міжнародними організаціями. У результаті за-
стосування методу Delphi для аналізу широкого спектру загроз, виявлених ци-
ми організаціями, виявлено 11 найважливіших загроз людству в першій
половині ХХІ ст. Проаналізовано вразливість різних країн до впливу
сукупності цих загроз. Побудовано сценарії можливого розвитку глобального
суспільства під час та після конфлікту.
Ключові слова: глобальна безпека, системні конфлікти, глобальні загрози,
норма Мінковського, вразливість.
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| institution | System research and information technologies |
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| language | English |
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| publisher | The National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute" |
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| spelling | journaliasakpiua-article-2691882022-12-21T22:15:21Z Study of security trends of the global society based on intelligent data analysis Дослідження тенденцій безпеки глобального суспільства на основі інтелектуального аналізу даних Zgurovsky, Michael Pyshnograiev, Ivan глобальна безпека системні конфлікти глобальні загрози норма Мінковського вразливість global safety systemic conflicts global threats Minkowski norm vulnerability This article is devoted to applying system analysis and data mining methodology to one of the most pressing problems today: studying the security of a global society in a conflicting world. A set of global threats relevant to the first half of the 21st century is considered. These threats have been identified by the United Nations (UN), the World Health Organization (WHO), the World Economic Forum, and other reputable international organizations. As a result of applying the Delphi method to analyze a wide range of threats identified by these organizations, 11 of the most important threats to humanity in the first half of the 21st century were identified. The vulnerabilities of different countries to the impact of the totality of these threats are analyzed. Scenarios for the possible development of a global society during and after the conflict are constructed. Присвячено застосуванню методології системного аналізу та інтелектуального аналізу даних до однієї з найактуальніших проблем сучасності: дослідження безпеки глобального суспільства в конфліктному світі. Розглянуто комплекс глобальних загроз, актуальних для першої половини ХХІ ст. Ці загрози були визначені Організацією Об’єднаних Націй, Всесвітньою організацією охорони здоров’я, Всесвітнім економічним форумом та іншими авторитетними міжнародними організаціями. У результаті застосування методу Delphi для аналізу широкого спектру загроз, виявлених цими організаціями, виявлено 11 найважливіших загроз людству в першій половині ХХІ ст. Проаналізовано вразливість різних країн до впливу сукупності цих загроз. Побудовано сценарії можливого розвитку глобального суспільства під час та після конфлікту. The National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute" 2022-10-30 Article Article application/pdf https://journal.iasa.kpi.ua/article/view/269188 10.20535/SRIT.2308-8893.2022.3.01 System research and information technologies; No. 3 (2022); 7-29 Системные исследования и информационные технологии; № 3 (2022); 7-29 Системні дослідження та інформаційні технології; № 3 (2022); 7-29 2308-8893 1681-6048 en https://journal.iasa.kpi.ua/article/view/269188/264678 |
| spellingShingle | глобальна безпека системні конфлікти глобальні загрози норма Мінковського вразливість Zgurovsky, Michael Pyshnograiev, Ivan Дослідження тенденцій безпеки глобального суспільства на основі інтелектуального аналізу даних |
| title | Дослідження тенденцій безпеки глобального суспільства на основі інтелектуального аналізу даних |
| title_alt | Study of security trends of the global society based on intelligent data analysis |
| title_full | Дослідження тенденцій безпеки глобального суспільства на основі інтелектуального аналізу даних |
| title_fullStr | Дослідження тенденцій безпеки глобального суспільства на основі інтелектуального аналізу даних |
| title_full_unstemmed | Дослідження тенденцій безпеки глобального суспільства на основі інтелектуального аналізу даних |
| title_short | Дослідження тенденцій безпеки глобального суспільства на основі інтелектуального аналізу даних |
| title_sort | дослідження тенденцій безпеки глобального суспільства на основі інтелектуального аналізу даних |
| topic | глобальна безпека системні конфлікти глобальні загрози норма Мінковського вразливість |
| topic_facet | глобальна безпека системні конфлікти глобальні загрози норма Мінковського вразливість global safety systemic conflicts global threats Minkowski norm vulnerability |
| url | https://journal.iasa.kpi.ua/article/view/269188 |
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