Game-theoretic analysis of market competition and pricing strategies

Abstract

This study investigates market competition and pricing strategies through a game-theoretic lens, focusing on how firms make strategic decisions in dynamic and uncertain environments. By modeling interactions among competitors in telecommunications, energy, and cloud service markets, the research examines both static and dynamic pricing frameworks, including evolutionary and repeated games. Simulation results highlight the impact of demand elasticity, competitor behavior, and adaptive learning on equilibrium prices and market stability. Findings demonstrate that firms employing adaptive and evolutionary pricing strategies achieve higher payoffs and maintain competitive advantage, while static strategies are more suitable for stable market conditions. The study provides insights for firms seeking optimal pricing strategies and for policymakers aiming to ensure fair and efficient market outcomes.