When allocating indivisible items to agents, it is known that the only strategyproof mechanisms that satisfy a set of rather mild conditions are constrained serial dictatorships: given a fixed order over agents, at each step the designated agent chooses a given number of items (depending on her position in the sequence). Agents who come earlier in the sequence have a larger choice of items; however, this advantage can be compensated by a higher number of items received by those who come later. How to balance priority in the sequence and number of items received is a nontrivial question. We use a previous model, parameterized by a mapping from ranks to scores, a social welfare functional, and a distribution over preference profiles. For several meaningful choices of parameters, we show that the optimal sequence can be computed exactly in polynomial time or approximated using sampling. Our results hold for several probabilistic models on preference profiles, with an emphasis on the Plackett-Luce model. We conclude with experimental results showing how the optimal sequence is impacted by various parameters.

This paper advocates for the integration of environmental aspects in computing curricula, with a focus on higher education. We created knowledge-based curriculum specifications in order to help teachers who wish to add knowledge foundation on computing impacts. This document lists topics and references that can be integrated into curricula. We implemented it in several higher education institutions. This paper reports on our experience and feedback. We also discuss recommendations to overcome obstacles that, from our experience, are often faced when modifying computing curricula to integrate environmental challenges.

Envy-freeness is a widely studied notion in resource allocation, capturing some aspects of fairness. The notion of envy being inherently subjective though, it might be the case that an agent envies another agent, but that from the other agents' point of view, she has no reason to do so. The difficulty here is to define the notion of objectivity, since no ground-truth can properly serve as a basis of this definition. A natural approach is to consider the judgement of the other agents as a proxy for objectivity. Building on previous work by Parijs (who introduced "unanimous envy") we propose the notion of approval envy: an agent ai experiences approval envy towards aj if she is envious of aj, and sufficiently many agents agree that this should be the case, from their own perspectives. Another thoroughly studied notion in resource allocation is proportionality. The same variant can be studied, opening natural questions regarding the links between these two notions. We exhibit several properties of these notions. Computing the minimal threshold guaranteeing approval envy and approval non-proportionality clearly inherits well-known intractable results from envy-freeness and proportionality, but (i) we identify some tractable cases such as house allocation; and (ii) we provide a general method based on a mixed integer programming encoding of the problem, which proves to be efficient in practice. This allows us in particular to show experimentally that existence of such allocations, with a rather small threshold, is very often observed.

In the context of fair allocation of indivisible items, fairness concepts often compare the satisfaction of an agent to the satisfaction she would have from items that are not allocated to her: in particular, envy-freeness requires that no agent prefers the share of someone else to her own share. We argue that these notions could also be defined relative to the knowledge that an agent has on how the items that she does not receive are distributed among other agents. We define a family of epistemic notions of envy-freeness, parameterized by a social graph, where an agent observes the share of her neighbours but not of her non-neighbours. We also define an intermediate notion between envy-freeness and proportionality, also parameterized by a social graph. These weaker notions of envy-freeness are useful when seeking a fair allocation, since envy-freeness is often too strong. We position these notions with respect to known ones, thus revealing new rich hierarchies of fairness concepts. Finally, we present a very general framework that covers all the existing and many new fairness concepts.

Sequential allocation is a simple allocation mechanism in which agents are given pre-specified turns in which they take one item among those that are still available. It has long been known that sequential allocation is not strategyproof. This raises the question of the complexity of computing a preference report that yields a higher utility than the truthful preference. We show that the problem is NP-complete for one manipulating agent with additive utilities and several non-manipulating agents. In doing so, we correct a wrong claim made in a previous paper. We then give two additional results. First, we present a polynomial-time algorithm for optimal manipulation when the manipulator has additive binary utilities. Second, we consider a stronger notion of manipulation whereby the untruthful outcome yields more utility than the truthful outcome for all utilities consistent with the ordinal preferences; for this notion, we show that a manipulation, if any, can be computed in polynomial time.

We define a new class of low-communication voting rules, tailored for contexts with few voters and possibly many candidates. These rules are defined by a predefined sequence of voters: at each stage, the designated voter eliminates a candidate, and the last remaining candidate wins. We study both deterministic (non-anonymous) variants, and randomized (and anonymous) versions of these rules. We focus on a subfamily of these rules defined by ``non-interleaved'' sequences. We first focus on the axiomatic properties of our rules. Then we focus on the identification of the non-interleaved sequence that gives the best approximation of the Borda score under the impartial culture. Finally, we apply our rules to randomly generated data. Our conclusion is that, in contexts where there are more candidates than voters, elimination-based rules allow for a very low communication complexity (and especially, avoid asking voters to rank alternatives), and yet can be good approximations of common voting rules, while enjoying a number of good properties.

We consider fair allocation of indivisible items under an additional constraint: there is an undirected graph describing the relationship between the items, and each agent's share must form a connected subgraph of this graph. This framework captures, e.g., fair allocation of land plots, where the graph describes the accessibility relation among the plots. We focus on agents that have additive utilities for the items, and consider several common fair division solution concepts, such as proportionality, envy-freeness and maximin share guarantee. While finding good allocations according to these solution concepts is computationally hard in general, we design efficient algorithms for special cases wherethe underlying graph has simple structure, and/or the number of agents---or, less restrictively, the number of agent types---is small. In particular, despite non-existence results in the general case, we prove that for acyclic graphs a maximin share allocation always exists and can be found efficiently.

Spatial crowdsourcing platforms (SCP) are systems that allow someone to publish spatial tasks in order to find a suitable workforce to achieve it. These tasks require people, often using mobile devices, to be at a given location in order to accomplish them. SCPs have been source of much interest for academy and industry. For this reason, Doan et al. [4] argued in 2011 that the race was now on "toward building general crowdsourcing platforms that can be used to develop such systems quickly". Since then, little has been done in this matter. Besides, what has been proposed does not take into account real commercial SCPs and its requirements. We propose GENIUS-C, a framework to support the development of SCPs. It is based on a generic architecture proposed by Fonteles et al. [7] designed to reduce the gap between academy and industry. GENIUS-C is meant to decrease development cost and effort and increase overall quality of SCPs. A case study SCP has been created using GENIUS-C to demonstrate its benefits and how it can be used in the developments of SCPs.

Spatial Crowdsourcing Platforms (SCP) are systems that act as brokers that help people requesting location dependent tasks to find suitable workforce to accomplish it. This kind of system has been source of much interest for both academy and industry. However, little has been done to define a crowdsourcing platform and its technical details precisely. Furthermore, much of the research works on this subject have focused specifically on crowdsensing platforms, a special case of SCP. There is also a need to contrast what has been proposed, so far, in this matter with real world SCPs in order to refine and validate the current state of the art results. In this paper, we discuss a list of requirements for SCPs. This list has been identified and validated by an analysis of scientific related works and several commercial SCPs. Based on this list, we also propose a generic architecture for SCPs that is meant to help the understanding of SCPs and the design of better spatial crowdsourcing solutions.

Crowdsourcing systems (CS) are platforms that enable a system or a user to publish tasks in order to be accomplished by others. Typically, a CS is a system where users, called workers, perform tasks using desktop computers. Recently, some CS have appeared with spatiotemporal tasks. Such tasks require a worker to be in a given location within a specific time-window to be accomplished. We propose and study here the usage of five heuristics for solving the NP-hard trajectory recommendation problem (TRP). In a TRP, the system recommends a trajectory to a worker that allows him to accomplish spatiotemporal tasks he has skill and/or affinity with, without exceeding his available time. Our experiments show that some of our heuristics are efficient alternatives for a heavy optimal approach providing trajectories with an average utility of about 60% of the optimal ones.

We define a family of rules for dividing m indivisible goods among agents, parameterized by a scoring vector and a social welfare aggregation function. We assume that agents' preferences over sets of goods are additive, but that the input is ordinal: each agent simply ranks single goods. Similarly to (positional) scoring rules in voting, a scoring vector s = (s1,...,sm) consists of m nonincreasing nonnegative weights, where siis the score of a good assigned to an agent who ranks it in position i. The global score of an allocation for an agent is the sum of the scores of the goods assigned to her. The social welfare of an allocation is the aggregation of the scores of all agents, for some aggregation function ★ such as, typically, + or min. The rule associated with s and ★ maps a profile to (one of) the allocation(s) maximizing social welfare. After defining this family of rules, and focusing on some key examples, we investigate some of the social-choice-theoretic properties of this family of rules, such as various kinds of monotonicity, separability, envy-freeness, and Pareto efficiency.

Picking sequences are a natural way of allocating indivisible items to agents in a decentralized manner: at each stage, a designated agent chooses an item among those that remain available. We address the computational issues of the manipulation of picking sequences by an agent or a coalition of agents. We show that a single agent can compute an optimal manipulation in polynomial time. Then we consider several notions of coalitional manipulation; for one of these notions, we show that computing an optimal manipulation is easy. We temper these results by giving a nontrivial upper bound on the impact of manipulation on the loss of social welfare.

We study the problem of fairly dividing a set of goods amongst a group of agents, when those agents have preferences that are ordinal relations over alternative bundles of goods (rather than utility functions) and when our knowledge of those preferences is incomplete. The incompleteness of the preferences stems from the fact that each agent reports their preferences by means of an expression of bounded size in a compact preference representation language. Specifically, we assume that each agent only provides a ranking of individual goods (rather than of bundles). In this context, we consider the algorithmic problem of deciding whether there exists an allocation that is possibly (or necessarily) envy-free, given the incomplete preference information available, if in addition some mild economic efficiency criteria need to be satisfied. We provide simple characterisations, giving rise to simple algorithms, for some instances of the problem, and computational complexity results, establishing the intractability of the problem, for others.

We consider the problem of allocating fairly a set of indivisible goods among agents from the point of view of compact representation and computational complexity. We start by assuming that agents have dichotomous preferences expressed by propositional formulae. We express efficiency and envy-freeness in a logical setting, which reveals unexpected connections to nonmonotonic reasoning. Then we identify the complexity of determining whether there exists an efficient and envy-free allocation, for several notions of efficiency, when preferences are represented in a succinct way (as well as restrictions of this problem). We first study the problem under the assumption that preferences are dichotomous, and then in the general case.