Ayumi Igarashi

Dividing a multilayered cake under nonoverlapping constraints captures several scenarios (e.g., allocating multiple facilities over time where each agent can utilize at most one facility simultaneously). We establish the existence of an envy-free multidivision that is nonoverlapping and contiguous within each layer when the number of agents is a prime power, solving partially an open question by Hosseini et al. [Hosseini H, Igarashi A, Searns A (2020) Fair division of time: Multi-layered cake cutting. Proc. 29th Internat. Joint Conf. Artificial Intelligence (IJCAI), 182–188; Hosseini H, Igarashi A, Searns A (2020) Fair division of time: Multi-layered cake cutting. Preprint, submitted April 28, http://arxiv.org/abs/2004.13397 ]. Our approach follows an idea proposed by Jojić et al. [Jojić D, Panina G, Živaljević R (2021) Splitting necklaces, with constraints. SIAM J. Discrete Math. 35(2):1268–1286] for envy-free divisions, relying on a general fixed-point theorem. We further design a fully polynomial-time approximation scheme for the two-layer, three-agent case, with monotone preferences. All results are actually established for divisions among groups of almost the same size. In the one-layer, three-group case, our algorithm is able to deal with any predetermined sizes, still with monotone preferences. For three groups, this provides an algorithmic version of a recent theorem by Segal-Halevi and Suksompong [Segal-Halevi E, Suksompong W (2021) How to cut a cake fairly: A generalization to groups. Amer. Math. Monthly 128(1):79–83]. Funding: This work was partially supported by the Japan Science and Technology Agency [Grant JPMJPR20C], Fusion Oriented REsearch for disruptive Science and Technology [Grant JPMJFR226O], and Exploratory Research for Advanced Technology [Grant JPMJER2301].

The airport problem is a classical and well-known model of fair cost-sharing for a single facility among multiple agents. This article extends it to a more general setting involving multiple facilities. Specifically, in our model, each agent selects a facility and shares the cost with the other agents using the same facility. This scenario frequently occurs in sharing economies, such as sharing subscription costs for a multi-user license or taxi fares among customers traveling to potentially different destinations along a route. Our model can be viewed as a coalition formation game with size constraints, based on the fair cost-sharing of the airport problem. We refer to our model as a fair ride allocation on a line . We incorporate Nash stability and envy-freeness as criteria for solution concepts in our setting. We show that if a feasible allocation exists, a Nash stable feasible allocation that minimizes the social cost of agents exists and can be computed efficiently. Regarding envy-freeness, we provide several structural properties of envy-free allocations. Based on these properties, we design efficient algorithms for finding an envy-free allocation when either (1) the number of facilities, (2) the number of agent types, or (3) the capacity of facilities, is small. Moreover, we show that a consecutive envy-free allocation can be computed in polynomial time. On the negative front, we show NP-completeness of determining the existence of an allocation under two relaxed envy-free concepts.

In multiwinner approval voting, the goal is to select a k -member committee based on voters’ approval ballots. A well-studied concept of proportionality in this context is the justified representation (JR) axiom, which demands that no large cohesive group of voters remains unrepresented. However, the JR axiom may conflict with other desiderata, such as social welfare (the number of approvals obtained by committee members) or coverage (the number of voters who approve at least one committee member). In this article, we investigate the price of imposing the JR axiom (as well as the more demanding extended justified representation axiom) on social welfare and coverage. Our approach is twofold: We derive worst-case bounds on the loss of welfare/coverage caused by imposing JR and study the computational complexity of finding committees with high welfare that provide JR (obtaining hardness results, approximation algorithms, and an exact algorithm for one-dimensional preferences).

In the classical version of online bipartite matching, there is a given set of offline vertices (aka agents) and another set of vertices (aka items) that arrive online. When each item arrives, its incident edges -- the agents who like the item -- are revealed and the algorithm must irrevocably match the item to such agents. We initiate the study of class fairness in this setting, where agents are partitioned into a set of classes and the matching is required to be fair with respect to the classes. We adopt popular fairness notions from the fair division literature such as envy-freeness (up to one item), proportionality, and maximin share fairness to our setting. Our class versions of these notions demand that all classes, regardless of their sizes, receive a fair treatment. We study deterministic and randomized algorithms for matching indivisible items (leading to integral matchings) and for matching divisible items (leading to fractional matchings). We design and analyze three novel algorithms. For matching indivisible items, we propose an adaptive-priority-based algorithm, MATCH-AND-SHIFT, prove that it achieves 1/2-approximation of both class envy-freeness up to one item and class maximin share fairness, and show that each guarantee is tight. For matching divisible items, we design a water-filling-based algorithm, EQUAL-FILLING, that achieves (1-1/e)-approximation of class envy-freeness and class proportionality; we prove (1-1/e) to be tight for class proportionality and establish a 3/4 upper bound on class envy-freeness. Finally, we build upon EQUAL-FILLING to design a randomized algorithm for matching indivisible items, EQAUL-FILLING-OCS, which achieves 0.593-approximation of class proportionality. The algorithm and its analysis crucially leverage the recently introduced technique of online correlated selection (OCS) [Fahrbach et al., 2020].