Research on Pricing Model of Car-Hailing Platform Based on Rational Inattention

At present, there are many literature on the pricing of car-hailing platforms under the two-sided markets, but few apply the theory of rational inattention to the pricing strategy of car-hailing platforms. Considering the randomness of the status of car-hailing services and the limited ability for passengers to acquire and process information, we constructs a bi-level optimization model for car-hailing pricing based on the theory of rational inattention. The upper level aims to maximize the platform’s profits, and takes the price of car-hailing services as the independent variable. The lower level aims at optimizing the information processing strategy of rationally inattentive passengers, and establishes a conditional choice probability model in the form of standard MNL. A hybrid heuristic algorithm based on particle swarm algorithm and successive average method is used. The upper level uses PSO to acquire the platform pricing strategy in the price range and is transmitted to the lower level. According to the results of the upper level, the lower level uses MSA to acquire the passengers’ choice probability and then feedback it to the upper level. An example is built to verify the effectiveness of the algorithm. We find that optimal pricing and profit are affected by information cost and prior belief. There is a threshold for information cost and prior belief are not as high as possible. Our findings provide a reference for the formulation of pricing strategies for car-hailing platforms.