Pricing the Unseen: Revenue Management under Rationally Inattentive Travellers

Abstract

Ongoing research in transport modelling is gradually shifting from the traditional Rational Expectations framework, premised on the assumption of fully informed, optimizing, and selfinterested decision-makers, to a more nuanced understanding of traveller behaviour under Bounded Rationality. However, the focus is typically targeted on developing advanced choice models to forecast travellers’ behaviour, with less emphasis on the subsequent response of competing suppliers. Recognizing the cognitive cost of acquiring and processing information (e.g., price, travel time) this paper explores the implications of Rational Inattention in the context of revenue management from the perspective of the transport service provider. Through a series of numerical experiments, we compare optimal pricing strategies in a duopolistic taxi/ride-hailing market, contrasting the outcomes derived from neoclassical models with those incorporating information capacity constraints and the formulation of prior, unconditional probabilities of choice. Our findings suggest that traditional Random Utility models (e.g., MNL) might overestimate the market share capture of minor, economical market players, failing to account for the influence of prior perceptions, and the scarcity of consumers’ attentional resources. This can lead to suboptimal pricing strategies, especially when introducing heterogeneity in the population. Through the analysis of varying levels of information acquisition cost, we observe a distinct advantage accruing to established providers, which is magnified as information costs increase, when other profitable opportunities are easily missed. Interestingly, beyond a certain threshold, minor competitors are also incentivized to obfuscate their pricing, leading to mutually profitable obfuscation pricing strategies and underscoring the need for market regulation. By incorporating discrete choice models of Rational Inattention into revenue management, this paper contributes to an alternative, potentially more realistic portrayal of travel demand and supply interaction, highlighting biases in conventional forecasts and identifying risks related to the exploitation of travellers’ cognitive capacity constraints. .