CC BY
0SUSTAINABLE DEVELOPMENT AND ENGINEERING ECONOMICS 3, 2024
Research article
DOI: https://doi.org/10.48554/SDEE.2024.3.5
The Application of Ontology-Based Game Theory for Decision Support in Sociotechnical Systems
Aleksei Gintciak* , Zhanna Burlutskaya , Darya Zubkova , Mikhail Uspenskiy Peter the Great St. Petersburg Polytechnic University, Saint Petersburg, Russian Federation, [email protected], [email protected], [email protected], [email protected]
*Corresponding author: [email protected]
Abstract
The present paper develops an invariant ontology of strategic interaction in a sociotechnical system using game theory tools. In the course of the research, ontologies are considered tools for modelling sociotechnical systems, including tools for social and technical process integration. The demand for these tools derives from the need to integrate people into technical systems as equivalent and equal elements that exert both external and internal influence on the system. Such sociotechnical models have already been applied to describe enterprise information structures, but they lack a description of decision-making between the system elements within the strategic interaction. As part of the solution to this problem, an ontology-based model of a sociotechnical system describing the interaction of both social and technical elements through game interaction is developed. Each of the participants in the interaction is described in terms of game theory, with the allocation of possible strategies and the corre-sponding winnings. Through the interactive entities within the game theory model, game interaction takes place between the participant and appropriate behaviour strategy selection. The model is a flexible, scalable tool for building simulation models of sociotechnical systems. The results obtained will be tested when real sociotechnical systems are built, and the ontology will be refined according to the results obtained.
Keywords: game theory, digitalisation, ontology, sociotechnical systems Citation: Gintciak, A., Burlutskaya, Z., Zubkova, D., Uspenskiy, M., 2024. The Application of Ontology-Based Game Theory for Decision Support in Sociotechnical Systems. Sustainable Development and Engineering Economics 3, 5. https://doi.org/10.48554/SDEE.2024.3.5
This work is licensed under a CC BY-NC 4.0
© Gintciak, A., Burlutskaya, Z., Zubkova, D., Uspenskiy, M., 2024. Published by Peter the Great St. Petersburg Polytechnic University
65
Management of knowledge and innovation for sustainable development
SUSTAINABLE DEVELOPMENT AND ENGINEERING ECONOMICS 3, 2024
Научная статья
УДК 005.8
DOI: https://doi.org/10.48554/SDEE.2024.3.5
Применение Теории Игр на Основе Онтологии для Поддержки Принятия
Решений в Социотехнических Системах
Алексей Гинцяк* , Жанна Бурлуцкая , Дарья Зубкова , Михаил Успенский
Санкт-Петербургский политехнический университет Петра Великого, Санкт-Петербург, Российская
Федерация, [email protected], [email protected], [email protected], [email protected]
*Автор, ответственный за переписку: [email protected]
Аннотация
В данной работе рассматривается разработка инвариантной онтологии стратегического
взаимодействия в социотехнической системе посредством инструментария теории
игр. В ходе исследования онтологии рассматриваются как инструмент моделирования
социотехнических систем, в том числе инструменты интеграции социальных и технических
процессов. Востребованность использования этих инструментов обусловлена необходимостью
интеграции человека в техническую систему как равноправного и равноправного элемента, оказывающего как внешнее, так и внутреннее воздействие на систему. Такие социотехнические
модели уже применяются для описания информационной структуры предприятий, но в них
отсутствует описание принятия решений между элементами системы в рамках стратегического
взаимодействия. В рамках решения данной проблемы разрабатывается основанная на
онтологии модель социотехнической системы, описывающая взаимодействие как социальных, так и технических элементов посредством игрового взаимодействия. Каждый из участников
взаимодействия описывается в терминах теории игр с выделением возможных стратегий и
соответствующих выигрышей. Посредством интерактивных сущностей модели теории игр
происходит игровое взаимодействие между участником и выбором соответствующих стратегий
поведения. Модель представляет собой гибкий масштабируемый инструмент для построения
имитационных моделей социотехнических систем. Полученные результаты будут проверены
при построении реальных социотехнических систем, а онтология будет дорабатываться в
соответствии с полученными результатами.
Ключевые слова: теория игр, цифровизация, онтология, социотехнические системы
Цитирование: Гинцяк, А., Бурлуцкая, Ж., Зубкова, Д., Успенский, М., 2024. Применение Теории Игр
на Основе Онтологии для Поддержки Принятия Решений в Социотехнических Системах. Sustainable Development and Engineering Economics 3, 5. https://doi.org/10.48554/SDEE.2024.3.5
Эта работа распространяется под лицензией CC BY-NC 4.0
© Гинцяк, А., Бурлуцкая, Ж., Зубкова, Д., Успенский, М., 2024. Издатель: Санкт-Петербургский
политехнический университет Петра Великого
Управление знаниями и инновациями в интересах устойчивого развития
66
The application of ontology-based game theory for decision support in sociotechnical systems 1. Introduction
The digitalisation of technical processes has changed approaches to management decision-making (Smirnov, 2021). Emerging technologies for the acquisition and processing of big data enable managers to get all the necessary information about the status, characteristics and functionality of various technical equipment (Dobrinskaya, 2021). The information obtained is processed and then used in digital models to predict the behaviour of technical systems. However, such models do not take into account the impact of the humans who work with technical equipment (Li et al., 2022). Thus, models of isolated technical systems fail to take into account the influence of the external environment. To solve this problem, models of sociotechnical systems are being developed. A sociotechnical system is a complex mixed system incorporating social and technical subsystems and the external environment (Prokopchuk, 2010; Tabachkov et al., 2010). The social system is characterised by human involvement in various processes and interrelations. This system describes the work of various enterprises, corporations, companies and business units (Xue et al., 2023). A technical system is an artificially created system designed to meet a technical need. This system includes various equipment, machines, devices and technologies (Kong et al., 2023).
The scientific community is interested in studying sociotechnical systems since they are pervasive in human societies. A technical system that lacks interaction with a human actor will be unusable since the technical system was initially intended to meet the technical needs of people. Thus, to maintain device or equipment functionality, the involvement of people, that is, a social system, is required (Prokopchuk, 2010).
Simulation modelling is used to model socioeconomic systems since it contains multiple paradigms that satisfy all the properties of these systems. However, the consideration of sociotechnical systems requires the integration of the tools used in the modelling of technical systems, such as mathematical modelling and dynamic prediction, sensitivity analysis, verification, validation and calibration (Gintciak, 2021). Thus, there is a need to develop tools for modelling sociotechnical systems, including tools for the integration of social and technical processes. Integration tools primarily refer to conceptual schemes of interaction between the elements of social and technical systems. Such descriptive models are ontology-based models. Therefore, to model the interaction between technical and social objects, game theory, which allows for the unification of the interaction participants as agents with their own goals and strategies, can be applied. In this interaction, the elements of the social and technical systems are considered equivalent and equal agents striving to achieve maximum efficiency by interacting with each other (Nikitenko et al., 2024).
The purpose of this work is to develop an invariant ontology of strategic interaction in a sociotechnical system in terms of game theory. The study examines game theory tools, taking into account application peculiarities and various types of interactions, and provides an analysis of the application of ontologies in various socioeconomic and sociotechnical systems. As a result of the research, an ontology of a sociotechnical system is developed, taking into account the description of entities and their game interactions. The results of this work can be applied to building models of sociotechnical systems using game theory tools. These solutions provide support for decision-making in sociotechnical systems.
2. Materials and Methods
2.1. Application of Ontologies
An ontology is a formal, explicit specification of a general conceptualisation (Khadir et al., 2021).
In a loose sense, an ontology is a description of knowledge. Thus, an ontology-based model is one that describes knowledge. The following types of ontologies are distinguished: 1. Domain ontologies capture knowledge that is valid for a specific type of domain (e.g. electronic, medical, mechanical, or digital domains).
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2. Generic ontologies are valid in several spheres. Generic ontologies are also called super theories and core ontologies.
3. Application ontologies contain all the necessary knowledge for modelling a specific domain (usually a combination of domain ontologies and method ontologies).
4. Representational ontologies are not tied to any particular subject area. Such ontologies provide representational entities without specifying what should be represented.
The most common example of using ontology is in knowledge-based systems, that is, systems that use artificial intelligence and exchange knowledge. This communication requires agreement on three levels: the representation language format, an agent–communication protocol, and the content specifications of the transmitted data (knowledge) (Studer et al., 1998). Ontologies are used for the third level: content-specific specifications.
Ontologies are also at the core of semantic networks, since they offer a formal method for defining concepts and the semantic relations between them. This allows for reasoning and the extraction of facts (when a certain level of formality is reached) (Gruber, 1993).
The reason that ontologies are so widely used is because they reflect a common understanding of a certain subject area, which can be transmitted between people and computers as a code (Fayoumi and Williams, 2021).
The use of ontologies for modelling sociotechnical and socioeconomic systems allows for the consideration of people as well as software and hardware as equivalent components (Prokopchuk, 2010).
For example, healthcare industry models describe the interaction between patients, the information system and medical staff. Such complex integrated systems, including interaction and information transfer at several levels between agents through technical means, cannot be described by one model and require careful study of the logical bindings within the ontology as the basis of a multi-agent model (Hinkelmann et al., 2016). A comprehensive social engineering ontology is applied in the same way for security analysis in general and social engineering attacks in particular (Li et al., 2022). The ontology is used to describe the connection between social engineering concepts and security concepts. The resulting ontology is formalised via description logic and then used to develop recommendations for technical equipment safety assurance based on behavioural models of social objects. It follows that developing an ontology is the first step in building a digital model of a system by combining artificial intelligence and computing technology (Sahlgren, 2021).
The use of ontologies for modelling sociotechnical systems makes it possible to apply ontologies to describe the integration of social processes and digital technologies. Thus, an ontology is a tool for analysing sociotechnical transitions and environmental sustainability within large socioeconomic and sociotechnical systems (Cuaresma et al., 2022; Rahayu et al., 2022).
The analysed examples of ontology applications consider the interaction of the elements of sociotechnical systems without taking into account their individual goals and characteristics. Thus, the obtained models focus on descriptions of the system structure but do not provide descriptions of the decision-making mechanisms. In this case, finding solutions means selecting strategies for each of the interaction participants, both as living agents and as technical equipment. Thus, game theory is a tool for developing formalised descriptions of strategy selection by various system elements.
2.2. Game Theory
Current game theory methods provide a relevant tool for the interaction of various parties in a game format. They allow for the determination of optimal strategies by game participants, depending on the conditions entered and the initial task. Game theory is applied in economics (Chica et al., 2018), psychology and behavioural sciences (Bhogal et al., 2017; Dixit and Nailbuff, 2015; Wang et al., 2019), computer science (Sergeev. 2006), investment (Chica et al., 2018) and biology (Dixit and Nailbuff, Sustain. Dev. Eng. Econ. 2024, 3, 5. https://doi.org/10.48554/SDEE.2024.3.5
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The application of ontology-based game theory for decision support in sociotechnical systems 2015).
Game theory is a mathematical description of the conflicts between two or more players, each of which pursues its own goals and personal interests. In this case, a conflict is a clash between the interests of several parties. The personal interests of the players determine their objective function, which is the basis of a player’s strategy set. At the same time, a strategy is a possible set of players’ actions. It is important to note that there may be uncertainty in the behaviour of the parties, but the rules of the game are always defined and known to all players. As a result of the interaction, the parties receive their winnings, which determine the outcome of the game (Babakina and Obiremko, 2019; Wang et al., 2019).
Game theory is divided into several types of interactions, as follows: 1. Cooperative and noncooperative (Chalkiadakis, 2011): Cooperative games differ from noncooperative ones in that they offer the possibility of joining coalitions. Another significant difference lies in the objects of research. In noncooperative games, each player acts only in the area of their interests. In this case, the noncooperative game solution is the Nash equilibrium (Williams, 2017). A Nash equilibrium is a set of players’ strategies in which they cannot improve their outcome. In the event that a player has no motivation to change the chosen strategy profile, since the change will not increase the player’s winnings, and the other participants adhere to their chosen strategy, then this profile is a Nash equilibrium (Chica et al., 2018). Thus, in noncooperative games, the player is an individual, while in cooperative games, the player is a coalition (a group of participants).
2. Symmetric and asymmetric (Wang et al., 2019): The game is considered symmetric if the players have the same strategies. An asymmetric game is one in which the strategies of the players diverge, so the outcome of the game differs, as well.
3. Zero-sum and non-zero-sum (Bailey and Piliouras, 2019): In zero-sum games, there is no oppor-tunity to increase or decrease the game’s resources. In this case, the winnings are equal to the total loss.
In non-zero-sum games, one player’s win does not necessarily imply another player’s loss. The total result of such a game can be greater or less than zero.
4. Simultaneous and sequential (Brihaye et al., 2017; Wright and Leyton-Brown, 2017): Simultaneous games imply that all players perform their actions instantly, parallel to each other, and without knowing the actions of their opponents. In sequential games, participants can make moves based on their knowledge of their opponents’ previous actions. Actions are performed either in a pre-established or a random order.
5. With complete or incomplete information (Vartanov and Ivin, 2020): In the case of complete information given, the players are informed of the possible strategies of the participants and know all the previous moves of their opponents. Otherwise, the game is called a game with incomplete or partial information.
6. Discrete and continuous (Vartanov and Ivin, 2020; Wang et al., 2019): Discrete games are those with a limited number of events and outcomes, while continuous games last an infinite amount of time.
Depending on the players’ personal characteristics and the game features, a game interaction model is selected. Within the present research, an ontological model is developed. It is a descriptive model of the strategic interaction of sociotechnical system elements via game theory tools. The game interaction within the model will be described in terms of game theory and will be a basic type of game with two participants.
3. Results
The developed ontology of the sociotechnical system in terms of game theory has two main levels: 1. The level of system elements description, at which a real strategic interaction is set.
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2. The level of the game-theoretic model description, at which a game theory model describing the real interaction is formalised.
The model can also be divided into the following entities:
1. System entities that include strategic interaction (the level of strategic interaction description) and a game theory model (the level of game interaction description).
2. Each agent’s individual characteristic entities, including participants (actors) and their goals (the level of strategic interaction description) and these participants’ agents with a description of their objective function, set of strategies, and winnings (the level of game interaction description).
3. Interactive entities of the game theory model, including the profile of the agents’ strategies and the profile of their winnings (all related to the level of interaction description).
Figure 1. Ontology of the Sociotechnical System
Consider the interconnections between elements at the entity level. Since the purpose of the ontology is to describe the interaction between the participants, the main interaction occurs between them through various elements. It is worth noting that in terms of the sociotechnical system description, both people and technical equipment can act as participants. Their strategies will depend on the objective functions specified in the design and the performance characteristics of the software/device according to its functionality, operation specifications, and other features.
The interaction between the participants is described as a strategic interaction in which they both take part. During the formalisation of strategic interaction as game interaction, each of the participants becomes an element of the game theory model and moves to the level of describing interaction as agents.
Within the game theory model, each participant has a set of formalised strategies according to the initial characteristics of the participants. As part of the game interaction, the strategies of both participants Sustain. Dev. Eng. Econ. 2024, 3, 5. https://doi.org/10.48554/SDEE.2024.3.5
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The application of ontology-based game theory for decision support in sociotechnical systems are combined into a strategy profile, which determines the winning profile. Then, the winnings of each participant are distributed, and the participants are evaluated according to the objective function of each agent.
It is also worth considering the relationships within entities that describe the agents’ individual characteristics. Each agent is an element of the game theory model and is described by the participant’s characteristics. Each agent has its own objective function, which is formed according to the purpose of the interaction participant. Each agent has a set of strategies described in terms of game theory. Then, each agent strategy enters the external entity of the model for pairwise formation of game inter-action strategy profiles. According to the interaction results, information about the participant’s winnings is received, which is measured in compliance with the objective function.
Applying game theory as a tool for decision-making in sociotechnical systems allows for a description of the strategic interaction between system elements. Describing the strategic interaction of the sociotechnical system’s elements via game theory provides additional information about the system necessary for informed management decision-making.
4. Discussion and Conclusions
Ontology-based models of sociotechnical systems have been successfully applied in enterprise information architecture design, where they describe the interconnections between social and technical systems (Rahayu et al., 2022; Sahlgren, 2021). In this case, ontologies represent an important stage in building digital models of enterprises or socioeconomic and sociotechnical systems by combining artificial intelligence with computing technology (Rahayu et al., 2022).
In the present research, an invariant ontology of strategic interaction in a sociotechnical system is developed using game theory. Ontology has been considered a tool for modelling sociotechnical systems, including tools to integrate social and technical processes based on existing examples of ontology applications. In the current study, game theory is used as a tool for the mathematical formalisation of various agents’ strategic interactions, taking into account their individual characteristics and goals. The resulting ontology-based model considers the interaction of two agents through system and interactive entities of the game theory model. The ontology describes in detail the individual characteristic entities of each agent from the description of participants and their goals to a set of strategies and winnings. The model is a flexible and scalable tool for building simulation models of sociotechnical systems. The results obtained will be tested when real sociotechnical systems are built, and the ontology will be refined according to the results obtained.
Acknowledgements
The research is funded by the Ministry of Science and Higher Education of the Russian Federation (contract No. 075-03-2024-004 dated 17.01.2024).
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3(36), 549–555. https://doi.org/10.1007/s12144-016-9443-1
Brihaye, T. et al., 2017. Dynamics and coalitions in sequential games. Electron. Proc. Theor. Comput. Sci. 256, 136–150. https://doi.
org/10.48550/arXiv.1709.02100
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Chica, M. et al., 2018. A networked-player trust game and its evolutionary dynamics. IEEE Trans. Evol. Comput. 22(6), 866–878. https://
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The article was submitted 12.09.2024, approved after reviewing 25.09.2024, accepted for publication 07.10.2024.
Статья поступила в редакцию 12.09.2024, одобрена после рецензирования 25.09.2024, принята к
публикации 07.10.2024.
About authors:
1. Aleksei Gintciak, Ph.D in technology, head of Laboratory of Digital modeling of Industrial systems, Peter the Great St. Petersburg Polytechnic University, Saint Petersburg, Russian Federation. [email protected], https://orcid.org/0000-0002-9703-5079.
2. Zhanna Burlutskaya, Junior Researcher of Laboratory of Digital modeling of Industrial systems, Peter the Great St. Petersburg Polytechnic University, Saint Petersburg, Russian Federation. [email protected], https://orcid.org/0000-0002-5680-1937
3. Darya Zubkova, Junior researcher in Laboratory of Digital modeling of Industrial systems, Peter the Great St. Petersburg Polytechnic University, Saint Petersburg, Russian Federation. [email protected], https://orcid.org/0000-0003-1106-5080
4. Mikhail Uspenskiy, Ph.D in technology, Senior Researcher in Laboratory of Digital modeling of Industrial systems, Peter the Great St. Petersburg Polytechnic University, Russian Federation. [email protected], https://orcid.org/0000-0003-3417-7376
Информация об авторах:
1. Алексей Гинцяк, кандидат технических наук, заведующий лабораторией «Цифровое моделирование
индустриальных систем», Санкт-Петербургский политехнический университет Петра Великого, Санкт-Петербург, Российская Федерация. [email protected], https://orcid.org/0000-0002-9703-5079
2. Жанна Бурлуцкая, младший научный сотрудник лаборатории «Цифровое моделирование индустриальных
систем», Санкт-Петербургский политехнический университет Петра Великого, Санкт-Петербург, Российская Федерация. [email protected], https://orcid.org/0000-0002-5680-1937
3. Дарья Зубкова, младший научный сотрудник лаборатории «Цифровое моделирование индустриальных
систем», Санкт-Петербургский политехнический университет Петра Великого, Санкт-Петербург, Российская Федерация. [email protected], https://orcid.org/0000-0003-1106-5080
4. Михаил Успенский, кандидат технических наук, ведущий научный сотрудник лаборатории «Цифровое
моделирование индустриальных систем», Санкт-Петербургский политехнический университет Петра
Великого, Санкт-Петербург, Российская Федерация. [email protected], https://orcid.org/0000-0003-3417-7376
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