Year: 2026
Type: article
Abstract: In this study, we focus on a static multi-resource allocation problem where customers pay to obtain resources from which they derive utility. Resources can be allocated either dynamically, over time, or statically, in a single-shot process. In this study, we explore the trade-off between revenue and fairness in a static setting. We distinguish between fairness-aware decision makers, who prioritize fairness among allocations with equal revenue, and fairness-unaware ones, who randomly select an allocation among all revenue-maximizing options. When the number of customers exceeds the number of available resources, we prove that fairness can be achieved without any revenue loss, but only under fairness-aware decision-making. In contrast, fairness-unaware decision makers are at high risk of selecting allocations with substantially lower fairness, despite achieving the same revenue. We also find that both fairness and revenue are maximized when customers have low price sensitivity and prefer more expensive resources. In contrast, when resources outnumber customers, trade-offs between fairness and revenue become inevitable, especially when customers are highly price-sensitive and prefer cheaper options. In our numerical experiments, we also study the impact of merging resource categories and heterogeneity in customer preferences on the revenue-fairness trade-off. Finally, we complement these analyses with an empirically calibrated case study, showing that our fairness insights remain robust in realistic allocation settings.
Institutions: NEOMA Business School, KU Leuven
Language: English
Field: Decision Sciences
Domain: Social Sciences
SDG: Reduced inequalities
Open Access status: hybrid
Estimated APC paid: $3,290
OpenAlex ID: W7220997397