AI as a mitigator of information asymmetry within platform strategiarchy logic
This paper aims to describe role of AI, deployed on a platform of strategizing, functions as a third party to mitigate information asymmetry within the framework of signaling theory. The entire history of human development can be viewed from the standpoint of the desire to overcome information asymm...
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Інститут економіки промисловості НАН України
2024
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| Cite this: | AI as a mitigator of information asymmetry within platform strategiarchy logic / О. Vyshnevskyi // Економічний вісник Донбасу. — 2024. — № 4 (78). — С. 5-11. — Бібліогр.: 13 назв. — англ. |
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| citation_txt | AI as a mitigator of information asymmetry within platform strategiarchy logic / О. Vyshnevskyi // Економічний вісник Донбасу. — 2024. — № 4 (78). — С. 5-11. — Бібліогр.: 13 назв. — англ. |
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| description | This paper aims to describe role of AI, deployed on a platform of strategizing, functions as a third party to mitigate information asymmetry within the framework of signaling theory.
The entire history of human development can be viewed from the standpoint of the desire to overcome information asymmetry. This takes the significance of the category "information asymmetry" to a new level, demonstrating its universal nature. Thus, information asymmetry is a universal category that is an integral characteristic of the development of nature and society, as well as all possible types of communications between key actors: individuals, organizations, nature and AI.
The four primary categories of actors (persons, organizations, nature, AI) give rise to 10 different types of interaction, which are divided into two groups (peer-level and hierarchical). Actors have different amounts of information, which describes information asymmetry. In turn, information asymmetry generates economic inequality.
The negative effects of information asymmetry can be reduced in two strategic approaches: either by providing additional information to the less informed party, or by redistributing the economic benefits received by the more informed party in favor of the less informed party.
Both of these strategic approaches can be implemented within the framework of the logic of platform strategiarchy using artificial intelligence. This assumes that all actors have formalized public strategies that are taken into account when concluding and implementing smart contracts. This approach can be considered as a further development of the provisions of the signaling theory (by M Spence), where public strategies play role of reliable signals.
Інформаційна симетрія в суспільстві є ідеальною ситуацією, що забезпечує мінімізацію транзакційних витрат. Проте в контексті Четвертої промислової революції відбуваються суперечливі процеси. У той час як інструменти для зменшення інформаційної асиметрії використовуються все частіше, постійно зростаючий обсяг інформації разом із засобами її спотворення фактично загострює проблему. Однак розвиток штучного інтелекту пропонує потенційні рішення для досягнення більшої інформаційної симетрії.
Стаття має на меті описати роль штучного інтелекту, розгорнутого на платформі стратегії, функціонує як третя сторона для пом’якшення інформаційної асиметрії в рамках теорії сигналізації.
Всю історію розвитку людства можна розглядати з позицій прагнення подолати інформаційну асиметрію. Це виводить значущість категорії «асиметрія інформації» на новий рівень, демонструючи її універсальний характер. Інформаційна асиметрія є універсальною категорією, яка є невід’ємною характеристикою розвитку природи та суспільства, а також усіх можливих типів комунікацій між ключовими акторами: індивідами, організаціями, природою та ШІ.
Чотири основні категорії акторів (людини, організації, природа, штучний інтелект) створюють 10 різних типів взаємодії, які поділяються на дві групи (однорівневі та ієрархічні). Актори мають різну кількість інформації, що описує інформаційну асиметрію. У свою чергу, інформаційна асиметрія породжує економічну нерівність.
Негативні наслідки інформаційної асиметрії можна зменшити за допомогою двох стратегічних підходів: або шляхом надання додаткової інформації менш поінформованій стороні, або шляхом перерозподілу економічних благ, отриманих більш поінформованою стороною, на користь менш поінформованої сторони.
Обидва ці стратегічні підходи можуть бути реалізовані в рамках логіки платформної стратегіархії з використанням штучного інтелекту. Це передбачає, що всі суб’єкти мають формалізовані публічні стратегії, які враховуються при укладанні та реалізації смарт-контрактів. Такий підхід можна розглядати як подальший розвиток положень сигнальної теорії (М. Спенса), де публічні стратегії відіграють роль надійних сигналів.
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| first_indexed | 2025-11-26T02:40:49Z |
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O. Vyshnevskyi
5
Економічний вісник Донбасу № 4(78), 2024 ISSN 1817-3772
SCIENTIFIC ARTICLES
DOI: https://doi.org/10.12958/1817-3772-2024-4(78)-5-11
UDC 330.1
O. Vyshnevskyi,
DrHab (Economics), Senior Researcher,
ORCID 0000-0002-2375-6033,
e-mail: vishnevskiy_O@nas.gov.ua,
Institute of Industrial Economics of NAS of Ukraine, Kyiv
AI AS A MITIGATOR OF INFORMATION ASYMMETRY
WITHIN PLATFORM STRATEGIARCHY LOGIC
Formulation of the problem. Information
symmetry in society is an ideal situation, provide to
minimize transaction costs. However, in the context of
the Fourth Industrial Revolution, contradictory
processes occur. While tools for reducing information
asymmetry are increasingly used, the ever-growing
volume of information, coupled with the means to
distort it, actually exacerbates the problem. Indirect
signs of deepening information asymmetry include
disproportions in world trade. The rise of "post-truth"
and "disinformation" is no coincidence and highlights
the importance of research into information asymmetry.
However, the development of artificial intelligence
offers potential solutions for achieving greater
information symmetry.
Literature review. Modern researchers
concentrate their attention on specific issues related to
information asymmetry. The issue of information
asymmetry remains in the focus and is being studied
from all over the world and concerns various aspects.
For example, A. Omar and others “examines the
relationship between information asymmetry and SOP
(say-on-pay) abstention votes, highlighting the role of
transparency and shareholders' decision-making in
executive compensation matters.” [1]. G. Ozparlak
investigates gender sphere and overcome artificial
barriers by women to reduction of asymmetric
information [2, p. 227]. E. Khansalar and others have
analyzed information asymmetry in capital market and
came to the conclusion “that the variables of cash flow
in proportion to accounting interest have more
information content in explanation of capital market
operation.” [3, p. 258]. D. Fasihat and R. Iskandar are
testing “relationship between the variables information
asymmetry, earnings management, and Cost of Equity
Capital” [4, p. 4643] and take result “to suppress profit
management practices and reduce the Cost of Equity
Capital value, companies can suppress information
asymmetry by increasing the transparency of company
information” [4, p. 4643].
Important research made N. Steigenberger, where
he asks question “Why do resource holders not identify
deceptive signals as deceptive?” [5, p. 9] and write
“Deceptive signaling has been a problem for decades,
and has been recently exacerbated by technological
developments in machine learning and generative AI,
which have made and continue to make deceptive
signals both cheaper to produce and more difficult to
detect” [5, p. 2]. But if there is looking to history, we
can see that deceptive signaling take place at list
thousands of years ago. Exemplify, Sun Tzu wrote
“when able to attack, we must seem unable; when using
our forces, we must seem inactive; when we are near,
we must make the enemy believe we are far away; when
far away, we must make him believe we are near” [6].
In terms of signaling theory Sun Tzu recommend to send
deceptive signals. Also, it is necessary to pay attention
that machine learning and generative AI create
opportunity to decrease information [7]. Next
N. Steigenberger discusses deceptive signalling in “four
contexts: (1) young firms’ attempts to acquire resources,
(2) listed firms signaling to investors, (3) firms signaling
to customers and (4) firms using deceptive signals to
disguise their strategic intentions vis-à-vis competitors
to gain a competitive edge and thus a financial
advantage” [5, p. 6]. As we can see wide or general
context, when deceptive signalling touch all aspects
(such as image of future, models behavior etc.) stay out
of focus.
As we can see object of researches have been a
market, or even more direct – specific markets.
Consequently, the understanding of information
asymmetry as a universal phenomenon remains without
due attention.
Purpose of research. Based on the literature
review and existing gap in the previous researches, this
Macroeconomics, Economic Theory and History
© Publisher Institute of Industrial Economy of National Academy of Sciences of Ukraine, 2024
© Publisher State Higher Education Institution "Luhansk Taras Shevchenko National University", 2024
O. Vyshnevskyi
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ISSN 1817-3772 Економічний вісник Донбасу № 4(78), 2024
paper aims to describe role of AI, deployed on a
platform of strategizing, functions as a third party to
mitigate information asymmetry within the framework
of signaling theory.
Presentation of main results. This study follows
a structured logical progression. In the first stage, it
examines the development of human society through the
lens of mitigating information asymmetry. In the second
stage, it investigates key instances where information
asymmetry arose, highlighting its pervasive nature. In
the third stage, it outlines fundamental strategies for
reducing information asymmetry. In the fourth stage, it
demonstrates how platform strategiarchy can be
employed to reduce information asymmetry."
Development of society from the standpoint of
information asymmetry
An examination of information asymmetry through
the lens of historical progression, specifically via
person-to-nature (P2N) and organization-to-nature
(O2N) communication, yields a compelling insight of
this process.
Upon entering the world, an infant possesses
minimal, if any, knowledge. From the earliest moments
of life, the infant endeavors to fill this cognitive void to
effectively interact with the surrounding environment.
Through the accumulation of knowledge, the infant
strives to diminish the discrepancy between their
perception of the world and objective reality. Given the
ephemeral nature of individual human existence, this
process is mirrored at the societal level, facilitated by
the capacity to preserve and transmit knowledge across
generations.
In the era of primitive communal society, the
divergence between the actual world order and societal
perceptions was maximal, bridged by anthropomorphic
totems. Subsequently, during the period of nascent
slave-owning societies, the attempt to reduce this
cognitive gap led to a transition from totems to a
pantheon of deities and early philosophy, whose
interactions aimed to construct a coherent worldview.
The feudal epoch witnessed the ascendancy of
monotheism. The capitalist era marked a shift towards
the dominance of a scientific worldview, which,
according to contemporary understanding, most
accurately represents reality.
"Consider the question, "Why does a stone fall?"
This query would elicit fundamentally different
responses in distinct historical periods. A member of a
primitive communal society might attribute the
phenomenon to the belief in inherent spirits. A
representative of a slave-owning or feudal society might
ascribe it to divine will. Only individuals from the
industrial era, and more specifically, the era of the
scientific and technological revolution coinciding with
the Enlightenment, would understand the influence of
gravitational force. This understanding is a product of
that time period. A similar observation can be made with
the question, "Why does the sun shine?". Individuals
from the first three epochs would not provide an
accurate explanation. Only those with a modern
scientific understanding can explain the process of
thermonuclear fusion of hydrogen into helium."
Thus, the entire trajectory of human development
can be conceptualized as an endeavor to mitigate the
information asymmetry between humanity and nature.
A discerning reader might note that the term
"information asymmetry" was initially applied to dyadic
contractual relationships. However, this distinction is
inconsequential, as humans are inherently engaged in an
interaction with their environment, which is contingent
upon their comprehension of it. Consequently, nature
may deviate from human expectations during
interaction (e.g., in resource extraction or space
exploration), which is a direct manifestation of
information asymmetry.
Basic cases of the emergence of information
asymmetry
According to the Nobel Prize in Economics
documents from 2001, the laureates demonstrated that
the phenomenon of information asymmetry can be
understood by augmenting economic theory with the
realistic assumption that one side of a market possesses
superior information [8, p. 1]. However, we can examine
this phenomenon significantly more widely, not limited
to market participants. We can explore this phenomenon
beyond market interactions. For example, we can
examine the relationships between interstate relations,
individual-state interactions, state-state interactions or
human-environment interactions.
It is widely recognized that information asymmetry
plays a pivotal role in the establishment of contractual
relationships, such as seller-buyer relationships [9].
However, a broader perspective can be adopted by del-
ineating four primary categories of actors: (1) persons
(individuals); (2) organizations (enterprises, public
associations, states, international organizations, classes,
legal entities); (3) nature (the environment); (4) artificial
intelligence (AI). Nature's apparent lack of volition may
seem incongruous within this grouping. However, it
does not preclude interactions with the first two
categories, as extensively explored in game theory
(games against nature). The inclusion of AI is somewhat
anticipatory. Nevertheless, given its developmental
strides in recent years, its consideration is pertinent for
both present and future discourse. With the proliferation
of the Internet of Things, AI can be posited as a potential
interface between the technosphere and the biosphere
(humanity).
This yields ten potential contractual scenarios:
(1) person-person (P2P), (2) person-organization (P2O),
(3) person-nature (P2N), (4) person-AI (P2AI),
(5) organization-organization (O2O), (6) organization-
nature (O2N), (7) organization-AI (O2AI), (8) nature-
nature (N2N), (9) nature-AI (N2AI), and (10) AI-AI
(AI2AI).
O. Vyshnevskyi
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Економічний вісник Донбасу № 4(78), 2024 ISSN 1817-3772
The combinations (1), (2), and (5) have received
the most scholarly attention, while (7)-(10) remains the
least explored. A brief overview of each scenario
follows.
1. Person-person (P2P). When two individuals (P1
and P2) interact, such as in a seller-buyer relationship
(or in non-economic contexts like spousal relations or
cooperative hunting), each possesses distinct knowledge
of interaction protocols and anticipated outcomes. Every
interaction constitutes an implicit or explicit contract,
governed by established or tacit rules, and dependent on
the volume of information (hereinafter VI) of each
party. This interaction presents two primary scenarios:
(A) VI (P1) > VI(P2) or VI(P2) > VI(P1), and (B)
VI(P1) ≈ VI(P2). Ultimately, information asymmetry
enables the party with superior information to shape the
future of the less informed party. Mitigation of this
asymmetry (for case VI(P1) > VI(P2)) and its
consequences can be achieved through two general
ways. Firstly, augmentation of P2's information
(knowledge) or reduction of P1's information (as a form
of informational ostracism). Secondly, provision of
compensation by P1.
The former is challenging, as P2 initially lacks
awareness of their knowledge deficit, and subsequent
awareness may be of limited practical utility. However,
AI or societal mechanisms can provide advisory
support. The latter necessitates the involvement of a
higher-level actor, such as a society or state.
2. Person-Organization (P2O). In interactions
between persons and organizations, the latter typically
possesses a greater informational advantage regarding
the subject matter and anticipated consequences. So that
(VI(O)>VI(P)). A standard strategy for mitigating
information asymmetry is the engagement of a third
party. For instance, in person-bank interactions, the
individual (1) may seek legal or financial counsel or
(2) participate in deposit insurance schemes. These
approaches, analogous to risk management, differ
fundamentally: one attempts to equalize information
asymmetry through external expertise, while the other
minimizes risks stemming from this asymmetry.
3. Person-Nature (P2N). This communication
is discussed in the previous section. In this case
VI(P) < VI(N).
4. Person-AI (P2AI). The interaction between AI
and humans has become ubiquitous with the advent of
large language models (LLMs). The disparity in
knowledge between individuals and AI, trained on vast
digitized datasets, is substantial. This interaction is
reciprocal: humans contribute new data to AI, while AI
imparts knowledge to humans. Given AI's simultaneous
interactions with numerous individuals, its knowledge
accrues at a faster rate than one person. Theoretically,
extensive delegation of cognitive tasks to AI could lead
to human cognitive atrophy, then potentially AI will be
degrading through interactions with cognitively
diminished individuals. However, this risk is mitigated
by the adaptability of AI algorithms. The critical issue
remains the development of AI quasi-consciousness,
without which AI remains a tool (in arms of individuals
or organizations) for imposing specific future scenarios
for others individuals or organizations. In any case
VI(P) < VI(AI).
5. Organization-Organization (O2O). Interactions
between organizations (first of all a big ones) typically
exhibit lower information asymmetry than P2O, due to
dedicated legal departments and access to professional
experts. So that VI(O1) ≈ VI(O2), if organization sizes
are same. And in general, VI (O1) > VI(O2), if size
company O1 is more than size company O2.
6. Organization-Nature (O2N). This communi-
cation has similar features as case P2N. So that
VI(O) < VI(N).
7. Organization-AI (O2AI). Organizations possess
less information than AI (VI(O)<VI(AI)), but, in current
conditions, AI is developed and owned by specific
organizations, implying AI's alignment with these
entities' interests.
8. Nature-Nature (N2N). Although natural
components do not possess the ability to enter into
contractual arrangements, communication is a
continuous phenomenon within ecological systems.
Human actions demonstrably affecte the exchange of
information between these components, leading to
modifications informational asymmetry between them.
9. AI-Nature (AI2N). Nature, in a universal sense,
is an inexhaustible source of AI training data, so its
knowledge surpasses that of AI and VI(N) > VI(AI).
10. AI-AI (AI2AI). The further development of
various AIs will also determine competition between
them, which will also depend on the level of information
asymmetry. The question of the need to mitigate
information asymmetry for case VI(AI1) > VI(AI2)
remains open.
These communications are categorized into:
(1) peer-level (P2P, O2O, AI2AI) and (2) hierarchical
(all others). Hierarchical actors typically possess
varying information volumes (VI), leading to the
following inequalities:
VI(N) > VI(AI) > VI(O) > VI(P).
These inequalities are justified as follows:
VI(O) > VI(P) because individuals are constituents of
organizations; VI(AI) > VI(O) because AI is trained on
multi-organizational data; and VI(N) > VI(AI) because
AI learns from a subset of natural data.
All actors have opportunity to utilize information
for gain, which assessments as expected results
(hereinafter ER).
Therefore, for persons, if VI(P1) > VI(P2), then
P(ER(P1)) > P(ER(P2)), i. e., P1's vision of the future is
more likely to prevail. This principle, if universalized,
predicts societal stratification into informed "subjects"
(with big VI) and uninformed "objects" (with small VI),
highlighting current knowledge (information) as a
determinant of the future.
O. Vyshnevskyi
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ISSN 1817-3772 Економічний вісник Донбасу № 4(78), 2024
Basic strategies to reduce or mitigate
information asymmetry
As shown in the previous section, there are two
main ways to counteract information asymmetry:
reducing either the causes or the consequences.
The first way involves providing the less informed
party with the necessary amount of additional
information. The second way involves providing
compensatory mechanisms for the party that suffers
losses due to lack of information.
Thus, we can propose 4 options for interaction
between the two parties (Table 1).
As both parties seek to achieve greater information
parity regarding the transaction's details. It promotes to
formation a more efficient market. AI can contribute to
increased efficiency in the information gathering
process for both parties.
Table 1
Intersection of strategies to reduce information asymmetry in the interaction of two parties
Party А
Party B
Strategy #1
Increasing the volume of informa-
tion about the subject of the
transaction (contract, communica-
tion)
Strategy #2
Reducing the risks (consequences)
caused by information asymmetries
Strategy #1
Increasing the volume of informa-
tion about the subject of the
transaction (contract, communica-
tion)
Both parties obtain additional data
regarding the transaction (including
using AI)
The formation of an effective
compensatory mechanism on the
part of B devalues the increase in
volume of information on the part
of A
Strategy #2
Reducing the risks (consequences)
caused by information asymmetries
The formation of an effective
compensatory mechanism on the
part of A devalues the increase in
volume of information on the part
of B
Mutual agreement to implement
compensatory mechanisms guaran-
teed by a third party (often the state)
Source: created by the author.
When one of the parties tries to increase the volume
of information about the subject of the transaction, and
the other party forms an effective compensatory
mechanism, the latter gains an advantage. And this
makes it economically unfeasible for first party to
increase the volume of information.
When both parties aim to reduce risk (the
consequences of asymmetry), the compensatory
mechanism is naturally introduced. Only a third party
can be a guarantor of the fulfillment of obligations.
Platform strategiarchy: a tool for third-party
reduction of information asymmetry
Drawing from signal theory, researchers have
observed that 'third parties can assume a signal
validation role' [5, p. 12]. For instance, a prominent
endorser, acting as a third party, can signal on behalf of
a resource seeker [10], such as when venture capital
investors endorse entrepreneurs [11].
Building upon the ten previously identified
communication types, we now examine the third party's
role. This entity can serve either as an information
source for the less informed party or as a guarantor of
compensatory mechanisms for it, redistributing benefits
to ensure fairness (Table 2).
Within the framework of P2P, P2O, P2N, P2AI,
O2O, and O2N interaction models, the role of third-
party mediation, undertaken by individuals,
organizations, or artificial intelligence, is observed.
Such mediation can be directed towards the diminution
of pre-existing information asymmetry or the
amelioration of its resultant effects. Individuals, in this
role, primarily function as providers of supplementary
information to less informed parties. It is important to
note that the natural world does not qualify as a third
party in these interaction models.
In O2AI and N2N interactions, organizations or AI
may serve as third-party mediators.
In N2AI and AI2AI interactions, only AI can act as
a third party.
The capacity of artificial intelligence to provide
informational consultancy and guarantee contractual
fulfillment through the instrumentality of smart-
contracts is observed.
The potential for using AI as a third party can be
realized within the logic of acting of platform for
coordinating strategies [12-13], that provide basis for
platform strategiarchy [7, p. 61]. Strategiarchy is a
social system in which all persons and organizations
have an actual public strategy. Platform strategiarchy is
a strategiarchy implemented on a digital platform. From
the perspective of signaling theory, a strategy
communicates important information to the
counterparty about the desired future state.
O. Vyshnevskyi
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Економічний вісник Донбасу № 4(78), 2024 ISSN 1817-3772
Table 2
Subjects that can act as a third party to reduce information asymmetry
One party
Second party
Person Organization Nature AI
Person Third party
1. Person (IV*;
G**).
2. Organization
(IV; G).
3. AI (IV; G)
Third party
1. Person (IV).
2. Organization
(IV; G).
3. AI (IV; G)
Third party
1. Person (IV).
2. Organization
(IV; G).
3. AI (IV; G)
Third party
1. Person (IV).
2. Organization
(IV; G).
3. AI (IV; G)
Organization Third party
Analogously to
case person-
organization
Third party
1. Person (IV).
2. Organization
(IV; G).
3. AI (IV; G)
Third party
1. Person (IV).
2. Organization
(IV; G).
3. AI (IV; G)
Third party
1. Organization
(IV; G).
2. AI (IV; G)
Nature Third party
Analogously to
case person-nature
Third party
Analogously to
case organization-
nature
Third party
1. Organization
(IV; G).
2. AI (IV; G)
Third party
1. AI (IV; G)
AI Third party
Analogously to
case person-AI
Third party
Analogously to
case organization-
AI
Third party
Analogously to
case nature-AI
Third party
1. AI (IV; G)
* IV – provides additional VI to the less informed party
** G – guarantees a fair redistribution of benefits obtained as a result of the transaction in favor of the less informed party.
Source: created by the author.
AI deployed on a digital platform of strategizing
[12-13] can organize and compare the public strategies
of individuals and organizations with their actions, the
content of the contracts they plan to conclude or are
currently executing. Thus, AI will ensure effective
moderation of both the conclusion of smart contracts
and their execution. Platform strategiarchy's economic
basis may rely on participant contributions,
governmental funding, or international grants, including
those from the UN.
Conclusions.
1. The entire history of human development can be
viewed from the standpoint of the desire to overcome
information asymmetry. This takes the significance of
the category "information asymmetry" to a new level,
demonstrating its universal nature. Thus, information
asymmetry is a universal category that is an integral
characteristic of the development of nature and society,
as well as all possible types of communications between
key actors: individuals, organizations, nature and AI.
2. The four primary categories of actors (persons,
organizations, nature, AI) give rise to 10 different types
of interaction, which are divided into two groups (peer-
level and hierarchical). Actors have different amounts of
information, which describes information asymmetry.
In turn, information asymmetry generates economic
inequality.
3. The negative effects of information asymmetry
can be reduced in two strategic approaches: either by
providing additional information to the less informed
party, or by redistributing the economic benefits
received by the more informed party in favor of the less
informed party.
4. Both of these strategic approaches can be
implemented within the framework of the logic of
platform strategiarchy using artificial intelligence. This
assumes that all actors have formalized public strategies
that are taken into account when concluding and
implementing smart contracts. This approach can be
considered as a further development of the provisions of
the signaling theory (by M Spence), where public
strategies play role of reliable signals, which determines
the scientific novelty of the research results.
Literature
1. Omar A., Cong Y., Tang A. Information asymmetry and say-on-pay abstention votes. Corporate Governance and
Sustainability Review. 2024. Nо. 8(3). P. 21–35. DOI: https://doi.org/10.22495/cgsrv8i3p2.
2. Ozparlak G. (2024). Does Board Gender Diversity Reduce Information Asymmetry? Evidence From the USA. Pamukkale
University Journal of Social Sciences Institute. 2024. № 64. P. 215-230. DOІ: https://doi.org/10.30794/pausbed.1494040.
3. Khansalar E., Dasht-Bayaz М., & Miri A. Stock Yield and Information Asymmetry. International Journal of Economics and
Finance. 2015. Vol. 7. №11. DOI: https://doi.org/10.5539/ijef.v7n11p250.
4. Fasihat D., Iskandar R. Earnings Management As A Moderator On The Effect Of Information Asymmetry On The Cost Of
Equity Capital. Dinasti International Journal of Economics, Finance & Accounting. 2024. Vol. 5 No. 4. P. 4637-4646. DOI:
https://doi.org/10.38035/dijefa.v5i4.3367.
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ISSN 1817-3772 Економічний вісник Донбасу № 4(78), 2024
5. Steigenberger N. Deceptive signalling: causes, consequences and remedies. International Journal of Management Reviews.
2024. P. 1-23. DOI: https://doi.org/10.1111/ijmr.12392.
6. Sun Tzu on the Art of War. Trans. from the Chinese by Lionel Giles. Leicester: Allandale Online Publishing, 2010. 62 p. URL:
https://sites.ualberta.ca/~enoch/Readings/The_Art_Of_War.pdf.
7. Vyshnevskyi O. Platform strategiarchy as a tool for reducing information asymmetry, taking into account the scale, cardinality
and order of the strategy. Економічний вісник Донбасу. 2023. № 4(74). С. 59-66. DOI: https://doi.org/10.12958/1817-3772-2023-
4(74)-59-66.
8. The Nobel prize organization. Markets with Asymmetric Information (October 10, 2001). Advanced Information on The Bank
of Sweden Prize in Economic Sciences in Memory of Alfred Nobel 2001. URL:
https://www.nobelprize.org/uploads/2018/06/advanced-economicsciences2001-1.pdf.
9. Akerlof G. A. The market for lemons. Quality uncertainty and the market mechanism. The Quarterly Journal of Economics.
1970. №84(3). P. 488-500. DOI: https://doi.org/10.2307/1879431.
10. Gomulya D., Jin K., Lee P. M., Pollock T. G. Crossed wires: Endorsement signals and the effects of IPO firm delistings on
venture capitalists’ reputations. Academy of Management Journal. 2019. № 62(3). P. 641–666. DOI:
https://doi.org/10.5465/amj.2016.0796.
11. Plummer L., Allison T., Connelly B. Better Together? Signaling Interactions in New Venture Pursuit of Initial External
Capital. The Academy of Management Journal. 2015. Vol. 59. № 5. P. 1585-1604. DOI: https://doi.org/10.5465/amj.2013.0100.
12. Вишневський О. С. Цифрова платформізація процесу стратегування розвитку національної економіки: монографія.
Київ: Ін-т економіки пром-сті НАН України, 2021. 449 с. URL: https://iie.org.ua/monografiyi/cifrovaplatformizacija-procesu-
strateguvannja-rozvitku-nacionalnoi-ekonomiki/.
13. Вишневський О. С. Цифрова платформізація стратегічного управління економікою України. Економіка
промисловості. 2021. № 3. С. 5-24. DOI: https://doi.org/10.15407/econindustry2021.03.005.
References
1. Omar, A., Cong, Y., & Tang, A. (2024). Information asymmetry and say-on-pay abstention votes. Corporate Governance and
Sustainability Review, 8(3), рр. 21-35. DOI: https://doi.org/10.22495/cgsrv8i3p2
2. Ozparlak, G. (2024). Does Board Gender Diversity Reduce Information Asymmetry? Evidence From the USA. Pamukkale
University Journal of Social Sciences Institute, 64, рр. 215-230. DOI: https://doi.org/10.30794/pausbed.1494040.
3. Khansalar, E., Dasht-Bayaz, М., Miri A. (2015). Stock Yield and Information Asymmetry. International Journal of Economics
and Finance, 7(11). DOI: https://doi.org/10.5539/ijef.v7n11p250.
4. Fasihat, D., & Iskandar, R. (2024). Earnings Management As A Moderator On The Effect Of Information Asymmetry On The
Cost Of Equity Capital. Dinasti International Journal of Economics, Finance & Accounting, 5(4), рр. 4637-4646. DOI:
https://doi.org/10.38035/dijefa.v5i4.3367.
5. Steigenberger, N. (2024). Deceptive signalling: causes, consequences and remedies. International Journal of Management
Reviews, рр. 1–23. DOI: https://doi.org/10.1111/ijmr.12392.
6. Sun Tzu on the Art of War. (2010). Trans. from the Chinese by Lionel Giles. Leicester, Allandale Online Publishing. 62 p.
Retrieved from https://sites.ualberta.ca/~enoch/Readings/The_Art_Of_War.pdf.
7. Vyshnevskyi, O. (2023). Platform strategiarchy as a tool for reducing information asymmetry, taking into account the scale,
cardinality and order of the strategy. Ekonomichnyi visnyk Donbasu – Economic Herald of the Donbas, 4(74), рр. 59-66. DOI:
https://doi.org/10.12958/1817-3772-2023-4(74)-59-66.
8. The Nobel prize organization. (2001, October). Markets with Asymmetric Information. Advanced Information on The Bank
of Sweden Prize in Economic Sciences in Memory of Alfred Nobel 2001. Retrieved from
https://www.nobelprize.org/uploads/2018/06/advanced-economicsciences2001-1.pdf.
9. Akerlof, G. A. (1970). The market for lemons. Quality uncertainty and the market mechanism. The Quarterly Journal of
Economics, 84(3), рр. 488-500. DOI: https://doi.org/10.2307/1879431.
10. Gomulya, D., Jin, K., Lee, P. M., & Pollock, T. G. (2019). Crossed wires: Endorsement signals and the effects of IPO firm
delistings on venture capitalists’ reputations. Academy of Management Journal, 62(3), рр. 641–666. DOI:
https://doi.org/10.5465/amj.2016.0796.
11. Plummer, L., Allison, T. & Connelly, B. (2015). Better Together? Signaling Interactions in New Venture Pursuit of Initial
External Capital. The Academy of Management Journal, 59(5), рр. 1585-1604. DOI: https://doi.org/10.5465/amj.2013.0100.
12. Vyshnevskyi, O. S. (2021). Tsyfrova platformizatsiia protsesu stratehuvannia rozvytku natsionalnoi ekonomiky [Digital
platformization of the process of strategizing the development of the national economy]. Kyiv, IIE of NAS of Ukraine. 449 p. Retrieved
from https://iie.org.ua/monografiyi/cifrovaplatformizacija-procesu-strateguvannja-rozvitku-nacionalnoi-ekonomiki/ [in Ukrainian].
13. Vyshnevskyi, O. S. (2021). Tsyfrova platformizatsiia stratehichnoho upravlinnia ekonomikoiu Ukrainy [Digital
platformization of strategic management of Ukrainian economy]. Econ. promisl., 3, рр. 5-24. DOI:
https://doi.org/10.15407/econindustry2021.03.005 [in Ukrainian].
Вишневський О. Штучний інтелект як агент пом’якшення інформаційної асиметрії за логікою платформної
стратегіархії
Інформаційна симетрія в суспільстві є ідеальною ситуацією, що забезпечує мінімізацію транзакційних витрат. Проте в
контексті Четвертої промислової революції відбуваються суперечливі процеси. У той час як інструменти для зменшення
інформаційної асиметрії використовуються все частіше, постійно зростаючий обсяг інформації разом із засобами її
спотворення фактично загострює проблему. Однак розвиток штучного інтелекту пропонує потенційні рішення для
досягнення більшої інформаційної симетрії.
Стаття має на меті описати роль штучного інтелекту, розгорнутого на платформі стратегії, функціонує як третя сторона
для пом’якшення інформаційної асиметрії в рамках теорії сигналізації.
Всю історію розвитку людства можна розглядати з позицій прагнення подолати інформаційну асиметрію. Це виводить
значущість категорії «асиметрія інформації» на новий рівень, демонструючи її універсальний характер. Інформаційна
O. Vyshnevskyi
11
Економічний вісник Донбасу № 4(78), 2024 ISSN 1817-3772
асиметрія є універсальною категорією, яка є невід’ємною характеристикою розвитку природи та суспільства, а також усіх
можливих типів комунікацій між ключовими акторами: індивідами, організаціями, природою та ШІ.
Чотири основні категорії акторів (людини, організації, природа, штучний інтелект) створюють 10 різних типів взаємодії,
які поділяються на дві групи (однорівневі та ієрархічні). Актори мають різну кількість інформації, що описує інформаційну
асиметрію. У свою чергу, інформаційна асиметрія породжує економічну нерівність.
Негативні наслідки інформаційної асиметрії можна зменшити за допомогою двох стратегічних підходів: або шляхом
надання додаткової інформації менш поінформованій стороні, або шляхом перерозподілу економічних благ, отриманих більш
поінформованою стороною, на користь менш поінформованої сторони.
Обидва ці стратегічні підходи можуть бути реалізовані в рамках логіки платформної стратегіархії з використанням
штучного інтелекту. Це передбачає, що всі суб’єкти мають формалізовані публічні стратегії, які враховуються при укладанні
та реалізації смарт-контрактів. Такий підхід можна розглядати як подальший розвиток положень сигнальної теорії (М.
Спенса), де публічні стратегії відіграють роль надійних сигналів.
Ключові слова: економічна теорія, асиметрія інформації, ШІ, економічні сигнали, теорія сигналів, стратегіархія,
платформна стратегіархія.
Vyshnevskyi О. AI as a Mitigator of Information Asymmetry within Platform Strategiarchy Logic
This paper aims to describe role of AI, deployed on a platform of strategizing, functions as a third party to mitigate information
asymmetry within the framework of signaling theory.
The entire history of human development can be viewed from the standpoint of the desire to overcome information asymmetry.
This takes the significance of the category "information asymmetry" to a new level, demonstrating its universal nature. Thus,
information asymmetry is a universal category that is an integral characteristic of the development of nature and society, as well as all
possible types of communications between key actors: individuals, organizations, nature and AI.
The four primary categories of actors (persons, organizations, nature, AI) give rise to 10 different types of interaction, which are
divided into two groups (peer-level and hierarchical). Actors have different amounts of information, which describes information
asymmetry. In turn, information asymmetry generates economic inequality.
The negative effects of information asymmetry can be reduced in two strategic approaches: either by providing additional
information to the less informed party, or by redistributing the economic benefits received by the more informed party in favor of the
less informed party.
Both of these strategic approaches can be implemented within the framework of the logic of platform strategiarchy using artificial
intelligence. This assumes that all actors have formalized public strategies that are taken into account when concluding and
implementing smart contracts. This approach can be considered as a further development of the provisions of the signaling theory (by
M Spence), where public strategies play role of reliable signals.
Keywords: economic theory, information asymmetry, AI, economics signaling, signaling theory, strategiarchy, platform
strategiarchy.
Received by the editors: 26.11.2024
Reviewed: 06.12.2024
|
| id | nasplib_isofts_kiev_ua-123456789-203372 |
| institution | Digital Library of Periodicals of National Academy of Sciences of Ukraine |
| issn | 1817-3772 |
| language | English |
| last_indexed | 2025-11-26T02:40:49Z |
| publishDate | 2024 |
| publisher | Інститут економіки промисловості НАН України |
| record_format | dspace |
| spelling | Vyshnevskyi, O. 2025-05-23T09:29:17Z 2024 AI as a mitigator of information asymmetry within platform strategiarchy logic / О. Vyshnevskyi // Економічний вісник Донбасу. — 2024. — № 4 (78). — С. 5-11. — Бібліогр.: 13 назв. — англ. 1817-3772 https://nasplib.isofts.kiev.ua/handle/123456789/203372 330.1 https://doi.org/10.12958/1817-3772-2024-4(78)-5-11 This paper aims to describe role of AI, deployed on a platform of strategizing, functions as a third party to mitigate information asymmetry within the framework of signaling theory. The entire history of human development can be viewed from the standpoint of the desire to overcome information asymmetry. This takes the significance of the category "information asymmetry" to a new level, demonstrating its universal nature. Thus, information asymmetry is a universal category that is an integral characteristic of the development of nature and society, as well as all possible types of communications between key actors: individuals, organizations, nature and AI. The four primary categories of actors (persons, organizations, nature, AI) give rise to 10 different types of interaction, which are divided into two groups (peer-level and hierarchical). Actors have different amounts of information, which describes information asymmetry. In turn, information asymmetry generates economic inequality. The negative effects of information asymmetry can be reduced in two strategic approaches: either by providing additional information to the less informed party, or by redistributing the economic benefits received by the more informed party in favor of the less informed party. Both of these strategic approaches can be implemented within the framework of the logic of platform strategiarchy using artificial intelligence. This assumes that all actors have formalized public strategies that are taken into account when concluding and implementing smart contracts. This approach can be considered as a further development of the provisions of the signaling theory (by M Spence), where public strategies play role of reliable signals. Інформаційна симетрія в суспільстві є ідеальною ситуацією, що забезпечує мінімізацію транзакційних витрат. Проте в контексті Четвертої промислової революції відбуваються суперечливі процеси. У той час як інструменти для зменшення інформаційної асиметрії використовуються все частіше, постійно зростаючий обсяг інформації разом із засобами її спотворення фактично загострює проблему. Однак розвиток штучного інтелекту пропонує потенційні рішення для досягнення більшої інформаційної симетрії. Стаття має на меті описати роль штучного інтелекту, розгорнутого на платформі стратегії, функціонує як третя сторона для пом’якшення інформаційної асиметрії в рамках теорії сигналізації. Всю історію розвитку людства можна розглядати з позицій прагнення подолати інформаційну асиметрію. Це виводить значущість категорії «асиметрія інформації» на новий рівень, демонструючи її універсальний характер. Інформаційна асиметрія є універсальною категорією, яка є невід’ємною характеристикою розвитку природи та суспільства, а також усіх можливих типів комунікацій між ключовими акторами: індивідами, організаціями, природою та ШІ. Чотири основні категорії акторів (людини, організації, природа, штучний інтелект) створюють 10 різних типів взаємодії, які поділяються на дві групи (однорівневі та ієрархічні). Актори мають різну кількість інформації, що описує інформаційну асиметрію. У свою чергу, інформаційна асиметрія породжує економічну нерівність. Негативні наслідки інформаційної асиметрії можна зменшити за допомогою двох стратегічних підходів: або шляхом надання додаткової інформації менш поінформованій стороні, або шляхом перерозподілу економічних благ, отриманих більш поінформованою стороною, на користь менш поінформованої сторони. Обидва ці стратегічні підходи можуть бути реалізовані в рамках логіки платформної стратегіархії з використанням штучного інтелекту. Це передбачає, що всі суб’єкти мають формалізовані публічні стратегії, які враховуються при укладанні та реалізації смарт-контрактів. Такий підхід можна розглядати як подальший розвиток положень сигнальної теорії (М. Спенса), де публічні стратегії відіграють роль надійних сигналів. en Інститут економіки промисловості НАН України Економічний вісник Донбасу Macroeconomics, economic theory and history AI as a mitigator of information asymmetry within platform strategiarchy logic Штучний інтелект як агент пом’якшення інформаційної асиметрії за логікою платформної стратегіархії Article published earlier |
| spellingShingle | AI as a mitigator of information asymmetry within platform strategiarchy logic Vyshnevskyi, O. Macroeconomics, economic theory and history |
| title | AI as a mitigator of information asymmetry within platform strategiarchy logic |
| title_alt | Штучний інтелект як агент пом’якшення інформаційної асиметрії за логікою платформної стратегіархії |
| title_full | AI as a mitigator of information asymmetry within platform strategiarchy logic |
| title_fullStr | AI as a mitigator of information asymmetry within platform strategiarchy logic |
| title_full_unstemmed | AI as a mitigator of information asymmetry within platform strategiarchy logic |
| title_short | AI as a mitigator of information asymmetry within platform strategiarchy logic |
| title_sort | ai as a mitigator of information asymmetry within platform strategiarchy logic |
| topic | Macroeconomics, economic theory and history |
| topic_facet | Macroeconomics, economic theory and history |
| url | https://nasplib.isofts.kiev.ua/handle/123456789/203372 |
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