Game Theory in Ride-Sharing Apps: How Uber and Lyft Use Algorithms to Set Prices
Keywords:
Surge Multipliers, Waiting Time, Dynamic Pricing, Machine Learning, Route congestionAbstract
Ride-sharing companies like Uber and Lyft use game theory-based pricing models to optimize supply and demand. Dynamic pricing models like surge multipliers change prices based on current conditions, optimizing driver availability when demand is high but not charging consumers a lot. The study examines large-scale strategies like "Uniform Pricing (UP)", "Differential Customer Pricing (DCP)", and "Differential Driver Pricing (DDP)" to understand how they influence market equilibrium. The use of machine learning models imposes predictive values on demand variations in order to accommodate precise fare adjustment. Dynamic price schemes also include the waiting time factor, road congestion, and local demand dynamics to optimize effectiveness and user satisfaction. Outcomes are that dynamic pricing, in conjunction with optimized wait times, encourages overall welfare in minimizing idle time for drivers as well as reducing customer waiting time. However, problems like collusion among drivers, price fairness, and regulatory concerns continue to persist. Future research has to focus on developing ethical pricing strategies, improving transparency in algorithmic decision-making, and promoting cross-platform collaboration in order to ensure a sustainable and fair ride-sharing ecosystem.
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