Determining the Optimal Pricing Strategy for Online Cinemas Using an Agent-Based Model on the Example of IVI and Kinopoisk
- Authors: Moiseenko A.M.1, Grineva N.V.1,2
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Affiliations:
- Russian Academy of National Economy and Public Administration under the President of the Russian Federation
- Financial University under the Government of the Russian Federation
- Issue: Vol 19, No 2 (2023)
- Pages: 197-208
- Section: Mathematical, Statistical and Instrumental Methods in Economics
- URL: https://journals.eco-vector.com/2541-8025/article/view/568527
- ID: 568527
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Abstract
The relevance of the study is justified by the phenomenon of the ever-increasing demand for online cinema services in the presence of free «pirate» resources, the heterogeneity of consumers of online cinemas that form the demand for their services, hence the low predictability of demand. The purpose of the study is to determine the optimal pricing policy in the conditions of competition of online cinemas. The constructed model predicts the demand for the services of the two leaders of the online cinema market with a reliability of more than 99%. Results: in a static game with full information, the price of a Kinopoisk subscription roughly corresponds to the best answer to the Ivi strategy to set the price at the current level; the Ivi subscription price corresponds to one of the Pareto optimal outcomes of the game, characterized by the symmetry of the strategy profile, the Nash equilibrium has not been achieved in reality.
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About the authors
Alexander M. Moiseenko
Russian Academy of National Economy and Public Administration under the President of the Russian Federation
Author for correspondence.
Email: alex7and7er@gmail.com
ORCID iD: 0009-0001-0380-1693
Russian Federation, Moscow
Natalia V. Grineva
Russian Academy of National Economy and Public Administration under the President of the Russian Federation; Financial University under the Government of the Russian Federation
Email: ngrineva@fa.ru
ORCID iD: 0000-0001-7647-5967
Cand. Sci. (Econ.), Associate Professor; Associate Professor of the Department of data analysis and machine learning, Financial University under the Government of the Russian Federation; Associate Professor, Department of System Analysis, Russian Academy of National Economy and Public Administration under the President of the Russian Federation
Russian Federation, Moscow; MoscowReferences
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