Cake-cutting protocols aim at dividing a cake'' (i.e., a divisible resource) and assigning the resulting portions to several players in a way that each of the players feels to have received a fair'' amount of the cake. An important notion of fairness is envy-freeness: No player wishes to switch the portion of the cake received with another player's portion. Despite intense efforts in the past, it is still an open question whether there is a envy-free cake-cutting protocol for an arbitrary number of players, and even for four players. We introduce the notion of degree of guaranteed envy-freeness (DGEF) as a measure of how good a cake-cutting protocol can approximate the ideal of envy-freeness while keeping the protocol finite bounded (trading being disregarded). We propose a new finite bounded proportional protocol for any number n 3 of players, and show that this protocol has a DGEF of 1 + (n^2) 2 . This is the currently best DGEF among known finite bounded cake-cutting protocols for an arbitrary number of players. We will make the case that improving the DGEF even further is a tough challenge, and determine, for comparison, the DGEF of selected known finite bounded cake-cutting protocols.
More recently, Brams, Jones, and Klamler @cite_6 proposed to minimize envy in terms of the maximum number of players that a player may envy. Their notion of measuring envy differs from our notion of DGEF in various ways, the most fundamental of which is that their notion takes an egalitarian'' approach to reducing the number of envy-relations (namely, via minimizing the most-envious player's envy, in terms of decreasing the number of this single player's envy-relations). In contrast, the DGEF aims at a utilitarian'' approach (namely, via minimizing overall envy, in terms of increasing the total number of guaranteed envy-free-relations among all players). That is to say that, although these notions may seem to be very similar at first glance, the approach presented in @cite_6 is not sensitive to a reduction in the number of envy-relations on the part of any other than the most-envious player, whereas the DGEF does take each single improvement into account and adapts accordingly. The DGEF, thus, is a more specific, more fine-tuned measure. Note also that Brams, Jones, and Klamler @cite_6 focus primarily on presenting a new protocol and less so on introducing a new notion for measuring envy.
@cite_6
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The goal of fair division is to distribute resources among competing players in a "fair" way. Envy-freeness is the most extensively studied fairness notion in fair division. Envy-free allocations do not always exist with indivisible goods, motivating the study of relaxed versions of envy-freeness. We study the envy-freeness up to any good (EFX) property, which states that no player prefers the bundle of another player following the removal of any single good, and prove the first general results about this property. We use the leximin solution to show existence of EFX allocations in several contexts, sometimes in conjunction with Pareto optimality. For two players with valuations obeying a mild assumption, one of these results provides stronger guarantees than the currently deployed algorithm on Spliddit, a popular fair division website. Unfortunately, finding the leximin solution can require exponential time. We show that this is necessary by proving an exponential lower bound on the number of value queries needed to identify an EFX allocation, even for two players with identical valuations. We consider both additive and more general valuations, and our work suggests that there is a rich landscape of problems to explore in the fair division of indivisible goods with different classes of player valuations. We propose a procedure for dividing indivisible items between two players in which each player ranks the items from best to worst. It ensures that each player receives a subset of items that it values more than the other player's complementary subset, given that such an envy-free division is possible. We show that the possibility of one player's undercutting the other's proposal, and implementing the reduced subset for himself or herself, makes the proposer "reasonable" and generally leads to an envy-free division, even when the players rank items exactly the same. Although the undercut procedure is manipulable, each player's maximin strategy is to be truthful. Applications of the undercut procedure are briefly discussed. We consider the problem of fairly dividing a collection of indivisible goods among a set of players. Much of the existing literature on fair division focuses on notions of individual fairness. For instance, envy-freeness requires that no player prefer the set of goods allocated to another player to her own allocation. We observe that an algorithm satisfying such individual fairness notions can still treat groups of players unfairly, with one group desiring the goods allocated to another. Our main contribution is a notion of group fairness, which implies most existing notions of individual fairness. Group fairness (like individual fairness) cannot be satisfied exactly with indivisible goods. Thus, we introduce two “up to one good” style relaxations. We show that, somewhat surprisingly, certain local optima of the Nash welfare function satisfy both relaxations and can be computed in pseudo-polynomial time by local search. Our experiments reveal faster computation and stronger fairness guarantees in practice. We study the problem of fairly allocating a set of indivisible goods to a set of people from an algorithmic perspective. fair division has been a central topic in the economic literature and several concepts of fairness have been suggested. The criterion that we focus on is envy-freeness. In our model, a monotone utility function is associated with every player specifying the value of each subset of the goods for the player. An allocation is envy-free if every player prefers her own share than the share of any other player. When the goods are divisible, envy-free allocations always exist. In the presence of indivisibilities, we show that there exist allocations in which the envy is bounded by the maximum marginal utility, and present a simple algorithm for computing such allocations. We then look at the optimization problem of finding an allocation with minimum possible envy. In the general case the problem is not solvable or approximable in polynomial time unless P = NP. We consider natural special cases (e.g.additive utilities) which are closely related to a class of job scheduling problems. Approximation algorithms as well as inapproximability results are obtained. Finally we investigate the problem of designing truthful mechanisms for producing allocations with bounded envy.