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Fairness has emerged as an important concern in automated decision-making inrecent years, especially when these decisions affect human welfare. In thiswork, we study fairness in temporally extended decision-making settings,specifically those formulated as Markov Decision Processes (MDPs). Our proposednotion of fairness ensures that each state's long-term visitation frequency isat least a specified fraction. This quota-based notion of fairness is naturalin many resource-allocation settings where the dynamics of a single resourcebeing allocated is governed by an MDP and the distribution of the sharedresource is captured by its state-visitation frequency. In an average-rewardMDP (AMDP) setting, we formulate the problem as a bilinear saddle point programand, for a generative model, solve it using a Stochastic Mirror Descent (SMD)based algorithm. The proposed solution guarantees a simultaneous approximationon the expected average-reward and fairness requirement. We give samplecomplexity bounds for the proposed algorithm and validate our theoreticalresults with experiments on simulated data.

Long-Term Resource Allocation Fairness in Average Markov Decision Process (AMDP) Environment