=Paper=
{{Paper
|id=Vol-3035/paper14
|storemode=property
|title=Model of Formation of Destructive Horizontal Processes and Counteraction to Them
|pdfUrl=https://ceur-ws.org/Vol-3035/paper14.pdf
|volume=Vol-3035
|authors=Vladimir Minakov,Olga Dudko,Tatyana Minakova,Tatyana Makarchuk,Petr Shepelev
}}
==Model of Formation of Destructive Horizontal Processes and Counteraction to Them==
https://ceur-ws.org/Vol-3035/paper14.pdf
Model of Formation of Destructive Horizontal Processes and
Counteraction to Them
Vladimir Minakov1, Olga Dudko1, Tatyana Minakova2, Tatyana Makarchuk1, Petr Shepelev 1
1
Saint-Petersburg State University of Economics, nab. Griboyedov Canal, 30-32, letter A., St. Petersburg,
191023, Russia
2
Saint-Petersburg Mining University, 21st Line, 2, St Petersburg, 199106, Russia
Abstract
A mathematical model of the formation of avalanche-like agitation processes in socio-
economic systems has been verified. The model is based on the viral dissemination and impact
of information on agents of social and economic processes, as well as the influence of absolute
volumes of information flows on the dynamics of their growth. It has been shown that the
classic Gartner hype cycle is a superposition of simultaneously acting factors of a destructive
and constructive nature. On the basis of the verified model, the field of scenarios for
counteracting destructive hype is generated. The grope of scenario dynamic time series allows
decision makers to evaluate the effectiveness of management of counteracting the hazards of
destructive hype processes, as well as the formation of control actions to achieve management
goals in social and economic systems. The model developed and the results of its use make it
possible to move from qualitative to quantitative methods of monitoring and managing hype
processes.
Keywords1
Model, information hypes, destructive effects of danger, counteraction, management
1. Introduction
Modern digital systems and technologies have become widespread and are developing more
dynamically than other infrastructural solutions of socio-economic systems, not only due to the
simplicity and availability of information, but also due to the impact on economic systems and their
agents [1]. The critical impact of information flows on the formation of the behavior of groups of
citizens in socio-economic systems was manifested in 2016, when the US Senate Intelligence
Committee presented reports on the activity of Russian social media users during the presidential
elections in the United States. The report claimed that accounts from Russia allegedly influenced
Trump's victory. Since then, much attention has been paid to monitoring and countering information
ceilings and even positive and negative assessments of accounts that affect the state of socio-economic
systems. In 2020, during the new presidential elections, on the contrary, the influence of the incumbent
on social networks was blocked, which undoubtedly influenced the election results. In this regard,
methods and models of quantitative assessment of trends formed by information flows with the aim of
supporting them in cases of interest of subjects of management of socio-economic systems, or,
conversely, confronting them, if they are destructive, become relevant [2].
In the scientific literature, destructive actions (cyber-attacks aimed at communication systems, target
servers; unauthorized access, spread of viruses) [3-6] are distinguished, which can be classified as
cybercrimes [7, 8]. At the same time, the precedents of the influence of legitimate information flows,
especially those of an agiotage nature [9], have not been sufficiently studied. In this regard, the present
study aims to develop mathematical methods and models of these processes, both of a constructive and
destructive nature.
BIT-2021: XI International Scientific and Technical Conference on Secure Information Technologies, April 6-7, 2021, Moscow, Russia
EMAIL: m-m-m-m-m@mail.ru (A. 1); shepeleva-olga@list.ru (A. 2); t.e.minakova@mail.ru (A. 3); tmakarchuk@mail.ru (A. 4),
peter_sh@mail.ru (A. 5)
ORCID: 0000-0001-6380-9091 (A. 1); 0000-0003-4555-2020 (A. 2); 0000-0001-5776-1917 (A. 3); 0000-0002-3069-3480 (A.4)
Β© 2021 Copyright for this paper by its authors.
Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR Workshop Proceedings (CEUR-WS.org)
125
It can be considered a textbook precedent for an abnormal growth in the share price of GameStop
Corp in 2021. The company has been losing profits for several years. And in the stock market, its shares
have traditionally been used by investment and hedge funds to make a profit through "short positions",
that is, by selling shares at the current price, after which quotes are reduced due to an excess of stocks
supply, and players buy the same shares but at a lower price. However, another hedge fund shorting in
late 2020 sparked a fierce backlash on the Reddit platform, which is used by nearly three million people.
The post by Elon Musk in support of the Reddit community was of great importance, after which quotes
rose to $ 347.51 per share with an average price for the year of $ 7.138 (the price increased by 4868%
from the annual average). The data visualization (https://ru.investing.com/equities/gamestop-corp-
historical-data) underlying the presented conclusions about the abnormal increase in the GameStop
share price is shown in Fig. 1.
Figure 1: GameStop Corp's hectic stock price cycles
The presented result illustrates the formation of agiotage anomalies [10, 11] under the influence of
information and information flows. The negative effect for short-term actors (investment and hedge
funds) is obvious: their losses amounted to over USD 6 billion. It is important to note that the
information calling for the acquisition of these shares was legitimate.
2. Model of the influence of digital resources on the consciousness and
behavior of actors in socio-economic systems
The influence of information and information and communication technologies (ICT) on the
behavior of citizens has been proven by scientific research [12, 13] and has been repeatedly confirmed
by practice. There was even a whole generation Y (born 1980 - 1999, plus or minus 3 years for the left
and right boundaries of the range), called the network. The expansion and strengthening of the influence
of digital technologies on socio-economic systems and actors is manifested in the last two decades,
when generation Z was formed (born in 2000 - 2019, plus or minus 3 years for the left and right
boundaries of the range), called digital. These generations are characterized not only by the permanent
use of world information resources [14], but also by such dependence on them that their social and
economic activity is manifested to a greater extent in the network space [15], and in real time (online)
than in real life and "live" social interaction "offline". The specified dependence on the information
126
resources of economic entities [1], mobile gadgets is a distinctive feature that characterizes and
predetermines the features of social and economic behavior of generations Y and Z.
Although other generations of the world's population are not subject to such an irresistible
dependence on digital resources, the influence of virtualization has grown so much [16, 17, 18] that the
annual cash flows of marketers to bloggers, providers, and authoritative participants in social networks
are 40 billion US dollars. It is important to note that this impact of information and information flows
continues to grow rapidly [19]. This growth was especially pronounced during the coronavirus
pandemic and the resulting restrictions on the physical interaction of people in the real world. The
capitalization of telecommunications companies and companies that develop and provide video
conferencing services grew faster than others.
The basis of the mathematical description of the processes of the formation and spread of excitement,
we put the following empirical provisions. First, citizens are always characterized by heterogeneity
(heterogeneity and opposition) of opinions (pluralism) on any aspect of social and economic processes
[20, 21, 22]. Consequently, any new statement that appears in the ICT space, a priori, has its own
audience of both approval and support, and potential opposition. Second: most of the actors decide to
support one or another alternative based on comparative analysis, ratings and rankings for the most
important indicator for the subject [17, 19]. This requires data generated by other entities to build a
sequence of alternatives on their basis and give preference to one of them, or to the TOP lists of
alternatives. Consequently, such groups of actors inevitably become followers. Some of them only need
information about the opinion of authoritative actors. This practice is becoming widespread in the ICT
space. Consequently, the growth rate of followers is proportional to the number of predecessors who
substantiated and presented their judgment. Third: the limiting number of participants in the rush
process (HYIP) is, of course, how finite is the resources, demand, or supply for which is subject to a
rush cycle [22]. The above can be formalized mathematically [23] by the differential equation:
ππππ/ππππ = π£π£ β
ππ β (1 β ππ/ππππ ) (1)
where:
Q - is the current value of the investigated indicator of the social or economic process;
Qm - is the limiting value of the indicator under study (for example, the volume of demand), that is,
the level of saturation;
v - is an indicator of the rate of propagation of a reaction, for example, to an information flow (the
degree of influence of information on the acceptance of its content).
Equation (1) has a solution in the form of a sigmoid:
ππππ (2)
ππ = .
1 + ππ π’π’βπ£π£β
π‘π‘
where:
u - is a constant of integration, determined by the initial conditions (time reference);
e - is a constant (2.71828459).
The growth sigmoid is graphically shown in Fig. 2.
Obviously, information countermeasures are subject to the same distribution patterns that form the
basis of equations (1) and (2). Consequently, the opposing processes are described by similar equations,
but with the opposite sign:
ππππ_/ππππ = π£π£_ β
ππ_ β (1 β ππ_/ππππ_ ) (3)
ππππ_ (4)
ππ_ = β
1 + ππ π’π’_βπ£π£_β
π‘π‘
The decay sigmoid (4) is also shown in Fig. 2.
127
Figure 2: Sigmoids of growth and decline
Now the equation of the joint influence of information and information flows on the sigmoidal
processes of growth and decline of agiotage processes can be represented by a superposition
of (2) and (4):
ππππ πππ΄π΄ππ_ (5)
πππ π π π π π = π’π’βπ£π£β
π‘π‘
β
1 + ππ 1 + ππ π’π’_βπ£π£_β
π‘π‘
The dynamics of changes in parameters in accordance with (5) is illustrated in Figure 3. Note that
the solution obtained has a character of change consistent with the Gartner rush cycle, which is widely
used not only by the company to analyze the state of the modern market, but also by enterprises to
manage the market positions of their products.
128
Figure 3: The nature of the rush cycle
The resulting solution makes it possible to assess and compare the nature of the rush dynamics, for
example, depending on the weight or volume of information flows in socio-economic systems [18],
which form the phase of rise (k +) and decline (k_), and, accordingly, the risks of their destructive
impact [24, 25, 26].
ππππ ππππ_ (6)
πππ π π π π π = ππ+ β β ππ_ β
1 + ππ π’π’βπ£π£β
π‘π‘ 1 + ππ π’π’_βπ£π£_β
π‘π‘
Depending on their ratio
ππ = ππβ /ππ+ (7)
we get the result of managing agiotage processes, visualized in Fig. 4.
129
Figure 4: Scenarios to counteract rush processes
The obtained result of modeling rush cycles with varying degrees of resistance, for example, to
anomalous processes, makes it possible to quantitatively estimate the resources necessary for such
resistance based on the ratio of information flows of a destructive and constructive nature.
The mathematical model of agiotage socio-economic processes has been verified. The regularities
of agiotage growth and decline are established, their unity, identity, but the opposite direction of
temporal dynamics is shown. A parameter has been introduced that makes it possible to assess and
compare the impact of both anomalous and countermeasures, for example, in the form of generated
information flows [27].
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