IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v12y2024i13p2079-d1427818.html
   My bibliography  Save this article

Optimization of Interaction with Counterparties: Selection Game Algorithm under Uncertainty

Author

Listed:
  • Andrey Zaytsev

    (Graduate School of Industrial Economics, Peter the Great St. Petersburg Polytechnic University, Saint Petersburg 195251, Russia)

  • Ekaterina Mihel

    (Graduate School of Industrial Economics, Peter the Great St. Petersburg Polytechnic University, Saint Petersburg 195251, Russia)

  • Nikolay Dmitriev

    (Graduate School of Industrial Economics, Peter the Great St. Petersburg Polytechnic University, Saint Petersburg 195251, Russia)

  • Dmitry Alferyev

    (Graduate School of Industrial Economics, Peter the Great St. Petersburg Polytechnic University, Saint Petersburg 195251, Russia
    Laboratory of Intelligent and Software Information Systems, Vologda Research Center, Russian Academy of Sciences, Vologda 160014, Russia)

  • Ungvari Laszlo

    (Technical University of Applied Sciences Wildau, 15745 Wildau, Germany)

Abstract

The purpose of this study is to develop a comprehensive algorithm for optimizing the interaction of economic entities with counterparties, taking into account the uncertainty of market conditions and the variety of behavioral strategies of participants. The developed algorithm aims to increase the stability and efficiency of the interactions between the economic entity under study and its counterparties, minimizing risks and optimizing cooperative and competitive strategies within the framework of existing market relations. The methodology uses game theory to devise interaction strategies using mutual influence indices, non-cooperative game principles, and payment matrices. The model analyzes various interaction scenarios with counterparties by using payment matrices and considering both competitive and cooperative conditions. The research methodology is supplemented by the calculation of integral estimates based on a set of financial and economic indicators, enabling the assessment of the impact of various interaction strategies on the overall efficiency of an economic entity. After testing the developed models, a set of data was obtained, which can be used to optimize strategic planning and manage the interaction of economic entities with counterparties. The developed algorithm is an effective tool for improving the operational analysis of enterprises, primarily in industrial sectors.

Suggested Citation

  • Andrey Zaytsev & Ekaterina Mihel & Nikolay Dmitriev & Dmitry Alferyev & Ungvari Laszlo, 2024. "Optimization of Interaction with Counterparties: Selection Game Algorithm under Uncertainty," Mathematics, MDPI, vol. 12(13), pages 1-26, July.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:13:p:2079-:d:1427818
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/12/13/2079/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/12/13/2079/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. V. Bieta & P. Smilyanets., 2007. "Game Theory and Financial Markets," VOPROSY ECONOMIKI, N.P. Redaktsiya zhurnala "Voprosy Economiki", vol. 10.
    2. Rahul Savani & Bernhard Stengel, 2015. "Game Theory Explorer: software for the applied game theorist," Computational Management Science, Springer, vol. 12(1), pages 5-33, January.
    3. Franklin Allen & Stephen Morris, 2014. "Game Theory Models in Finance," International Series in Operations Research & Management Science, in: Kalyan Chatterjee & William Samuelson (ed.), Game Theory and Business Applications, edition 2, chapter 0, pages 17-41, Springer.
    4. Michel Le Breton & Juan Moreno-Ternero & Alexei Savvateev & Shlomo Weber, 2013. "Stability and fairness in models with a multiple membership," International Journal of Game Theory, Springer;Game Theory Society, vol. 42(3), pages 673-694, August.
    5. Gholamreza Askari & Madjid Eshaghi Gordji & Choonkil Park, 2019. "The behavioral model and game theory," Palgrave Communications, Palgrave Macmillan, vol. 5(1), pages 1-8, December.
    6. Jonathan Newton, 2018. "Evolutionary Game Theory: A Renaissance," Games, MDPI, vol. 9(2), pages 1-67, May.
    7. Dmitriy G. Rodionov & Iuliia V. Boiko & Olga S. Nadezhina, 2018. "Pricing of the License Agreements: Improvement of the Methodology on the Basis of the Game Approach," Eurasian Studies in Business and Economics, in: Mehmet Huseyin Bilgin & Hakan Danis & Ender Demir & Ugur Can (ed.), Consumer Behavior, Organizational Strategy and Financial Economics, pages 241-252, Springer.
    8. Martin J. Osborne & Ariel Rubinstein, 1994. "A Course in Game Theory," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262650401, December.
    9. V. Bieta & P. Smelyanets, 2007. "Game Theory and Financial Markets," Voprosy Ekonomiki, NP Voprosy Ekonomiki, issue 10.
    10. Mohammaditabar, Davood & Ghodsypour, Seyed Hassan & Hafezalkotob, Ashkan, 2016. "A game theoretic analysis in capacity-constrained supplier-selection and cooperation by considering the total supply chain inventory costs," International Journal of Production Economics, Elsevier, vol. 181(PA), pages 87-97.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Stefan Ambec & Yann Kervinio, 2016. "Cooperative decision-making for the provision of a locally undesirable facility," Social Choice and Welfare, Springer;The Society for Social Choice and Welfare, vol. 46(1), pages 119-155, January.
    2. Shi, Yi & Deng, Yawen & Wang, Guoan & Xu, Jiuping, 2020. "Stackelberg equilibrium-based eco-economic approach for sustainable development of kitchen waste disposal with subsidy policy: A case study from China," Energy, Elsevier, vol. 196(C).
    3. Ventura, José A. & Bunn, Kevin A. & Venegas, Bárbara B. & Duan, Lisha, 2021. "A coordination mechanism for supplier selection and order quantity allocation with price-sensitive demand and finite production rates," International Journal of Production Economics, Elsevier, vol. 233(C).
    4. Cheng‐Kuang Wu & Yi‐Ming Chen & Dachrahn Wu & Ching‐Lin Chi, 2020. "A Game Theory Approach for Assessment of Risk and Deployment of Police Patrols in Response to Criminal Activity in San Francisco," Risk Analysis, John Wiley & Sons, vol. 40(3), pages 534-549, March.
    5. Müller, Christoph, 2020. "Robust implementation in weakly perfect Bayesian strategies," Journal of Economic Theory, Elsevier, vol. 189(C).
    6. Hitoshi Matsushima, 2019. "Implementation without expected utility: ex-post verifiability," Social Choice and Welfare, Springer;The Society for Social Choice and Welfare, vol. 53(4), pages 575-585, December.
    7. Dasgupta Utteeyo, 2011. "Are Entry Threats Always Credible?," The B.E. Journal of Economic Analysis & Policy, De Gruyter, vol. 11(1), pages 1-41, December.
    8. Baran Han, 2018. "The role and welfare rationale of secondary sanctions: A theory and a case study of the US sanctions targeting Iran," Conflict Management and Peace Science, Peace Science Society (International), vol. 35(5), pages 474-502, September.
    9. Carlos Pimienta & Jianfei Shen, 2014. "On the equivalence between (quasi-)perfect and sequential equilibria," International Journal of Game Theory, Springer;Game Theory Society, vol. 43(2), pages 395-402, May.
    10. Asheim, Geir & Søvik, Ylva, 2003. "The semantics of preference-based belief operators," Memorandum 05/2003, Oslo University, Department of Economics.
    11. Wang, Yafeng & Graham, Brett, 2009. "Generalized Maximum Entropy estimation of discrete sequential move games of perfect information," MPRA Paper 21331, University Library of Munich, Germany.
    12. Karbowski, Adam, 2011. "O kilku modelach samolubnego karania w ekonomii behawioralnej [Evolution of altruism in the light of behavioral economics]," MPRA Paper 69604, University Library of Munich, Germany.
    13. Ricardo F. Reis & Phillip C. Stocken, 2007. "Strategic Consequences of Historical Cost and Fair Value Measurements," Contemporary Accounting Research, John Wiley & Sons, vol. 24(2), pages 557-584, June.
    14. Costello, Christopher & Molina, Renato, 2021. "Transboundary marine protected areas," Resource and Energy Economics, Elsevier, vol. 65(C).
    15. Martin Hellwig & Felix Bierbrauer, 2009. "Public Good Provision in a Large Economy," 2009 Meeting Papers 1062, Society for Economic Dynamics.
    16. Maria Montero & Alex Possajennikov, 2021. "An Adaptive Model of Demand Adjustment in Weighted Majority Games," Games, MDPI, vol. 13(1), pages 1-17, December.
    17. Jonathan Hofer, 2024. "Red light game identifies ineffective criminal deterrence," American Journal of Economics and Sociology, Wiley Blackwell, vol. 83(3), pages 673-682, May.
    18. Ünsal Özdilek, 2020. "Land and building separation based on Shapley values," Palgrave Communications, Palgrave Macmillan, vol. 6(1), pages 1-13, December.
    19. Arantza Estévez-Fernández & Peter Borm & M. Gloria Fiestras-Janeiro, 2020. "Nontransferable utility bankruptcy games," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 28(1), pages 154-177, April.
    20. Sjur Didrik Flåm, 2013. "Reaching Market Equilibrium Merely by Bilateral Barters," CESifo Working Paper Series 4504, CESifo.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:12:y:2024:i:13:p:2079-:d:1427818. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.