To What Extent Has Game Theory Explained the Pricing Strategies of Ride-Hailing Firms Like Uber and Ola in India (2015–2023)?

This paper investigates the extent to which game theory explains the pricing strategies of Uber and Ola in the Indian ride-hailing market between 2015 and 2023. The study employs a secondary-data-based approach, drawing on company financial reports, market analyses, and industry publications to identify strategic pricing patterns across three distinct phases: (1) Entry and Subsidy Wars (2015–2016), (2) Price War and Customer Retention (2017–2019), and (3) Post-COVID Surge Pricing and Algorithmic Adjustments (2020 onwards). The findings indicate that game theory provides significant explanatory power in the early stages of market competition. The aggressive subsidy-driven strategies during Phase 1 align closely with the Prisoner’s Dilemma and Bertrand competition frameworks, while Phase 2 reflects dynamics consistent with repeated games, as firms engaged in prolonged tit-for-tat price reductions to secure market share. However, the relevance of classical game-theoretic models diminishes in Phase 3, as pricing decisions became increasingly algorithm-driven and influenced by external factors such as regulatory controls, fuel costs, and pandemic-related disruptions. The paper concludes that game theory explains Uber and Ola’s pricing strategies effectively during the initial and intermediate phases but offers limited predictive value in a mature, technology-driven environment. Recommendations for future research include incorporating consumer behavior analysis and assessing the impact of emerging competitors like Rapido and BluSmart.