Democratic Fair Allocation of Indivisible Goods

We study the problem of fairly allocating indivisible goods to groups of agents. Agents in the same group share the same set of goods even though they may have different preferences. Previous work has focused on unanimous fairness, in which all agents in each group must agree that their group's share is fair. Under this strict requirement, fair allocations exist only for small groups. We introduce the concept of democratic fairness, which aims to satisfy a certain fraction of the agents in each group. This concept is better suited to large groups such as cities or countries. We present protocols for democratic fair allocation among two or more arbitrarily large groups of agents with monotonic, additive, or binary valuations. For two groups with arbitrary monotonic valuations, we give an efficient protocol that guarantees envy-freeness up to one good for at least $1/2$ of the agents in each group, and prove that the $1/2$ fraction is optimal. We also present other protocols that make weaker fairness guarantees to more agents in each group, or to more groups. Our protocols combine techniques from different fields, including combinatorial game theory, cake cutting, and voting.Comment: Appears in the 27th International Joint Conference on Artificial Intelligence and the 23rd European Conference on Artificial Intelligence (IJCAI-ECAI), 201

Allocation of Divisible Goods under Lexicographic Preferences

We present a simple and natural non-pricing mechanism for allocating divisible goods among strategic agents having lexicographic preferences. Our mechanism has favorable properties of incentive compatibility (strategy-proofness), Pareto efficiency, envy-freeness, and time efficiency

Almost Group Envy-free Allocation of Indivisible Goods and Chores

We consider a multi-agent resource allocation setting in which an agent's utility may decrease or increase when an item is allocated. We take the group envy-freeness concept that is well-established in the literature and present stronger and relaxed versions that are especially suitable for the allocation of indivisible items. Of particular interest is a concept called group envy-freeness up to one item (GEF1). We then present a clear taxonomy of the fairness concepts. We study which fairness concepts guarantee the existence of a fair allocation under which preference domain. For two natural classes of additive utilities, we design polynomial-time algorithms to compute a GEF1 allocation. We also prove that checking whether a given allocation satisfies GEF1 is coNP-complete when there are either only goods, only chores or both

On incentive issues in practical auction design

Algorithmic mechanism design studies the allocation of resources to selfish agents, who might behave strategically to maximize their own utilities. This thesis studies these incentive issues arsing from four different settings, that are motivated by real- life applications. We model the settings and problems by appropriately extending or generalizing classical economic models. After that we systematically analyze the auction design problems by using methods from both economic theory and computer science. The first problem is the auction design problem for selling online rich media ad- vertisement. In this market, multiple advertisers compete for a set of slots that are arranged in a line, such as a banner on a website. Each buyer desires a particular num- ber of consecutive slots and has a private per-click valuation while each slot is associated with a quality factor. Our goal is to maximize the auctioneer’s expected revenue given buyers’ consecutive demand. This is motivated by modeling buyers who may require these to display a large size ad. Three major pricing mechanisms, the Bayesian pric- ing model, the maximum revenue market equilibrium model and an envy-free solution model are studied in this setting. The second setting is for fund-raising scenarios, where a revenue target is usually specified. We are interested in designing truthful auctions that maximize the probability to achieve this revenue target, rather than in maximizing the expected revenue. We study this topic from the perspective of Bayesian auction design in digital good auctions. We present an algorithm to find the optimal truthful auction for two buyers with independent valuations and show the problem is NP-hard when the number of buyers is arbitrary or the distributions are correlated. We also investigate simple auctions in this setting and provide approximately optimal solutions. Third, we study double auction market design where the trading broker wants to maximize its total revenue by buying low from the sellers and selling high to the buyers in a Bayesian setting. For single-parameter setting, we develop a maximum mechanism for the market maker to maximize its own revenue. For the more general case where each seller’s product may be different, we consider a number of various settings in terms of constraints on supplies and demands. For each of them, we develop a polynomial time computable truthful mechanism for the market maker to achieve a revenue at least a constant factor times the revenue of any other truthful mechanism. Finally, we study the inefficiency of mixed equilibria of all-pay auctions in three different environments – combinatorial, multi-unit and single-item auctions. First, we consider item-bidding combinatorial auctions where m all-pay auctions run in parallel, one for each good. For fractionally subadditive valuations, we strengthen the upper bound by proving some structural properties of mixed Nash equilibria. Next, we design an all-pay mechanism with a randomized allocation rule for the multi-unit auction, which admits a unique, approximately efficient, pure Nash equilibrium. Finally, we analyze single-item all-pay auctions motivated by their connection to crowdsourcing contests and show tight bounds on the PoA of social welfare, revenue and maximum bid

Dynamic capacities and priorities in stable matching

Cette thèse aborde les facettes dynamiques des principes fondamentaux du problème de l'appariement stable plusieurs-à-un. Nous menons notre étude dans le contexte du choix de l'école et de l'appariement entre les hôpitaux et les résidents. Dans la première étude, en utilisant le modèle résident-hôpital, nous étudions la complexité de calcul de l'optimisation des variations de capacité des hôpitaux afin de maximiser les résultats pour les résidents, tout en respectant les contraintes de stabilité et de budget. Nos résultats révèlent que le problème de décision est NP-complet et que le problème d'optimisation est inapproximable, même dans le cas de préférences strictes et d'allocations de capacités disjointes. Ces résultats posent des défis importants aux décideurs qui cherchent des solutions efficaces aux problèmes urgents du monde réel. Dans la seconde étude, en utilisant le modèle du choix de l'école, nous explorons l'optimisation conjointe de l'augmentation des capacités scolaires et de la réalisation d'appariements stables optimaux pour les étudiants au sein d'un marché élargi. Nous concevons une formulation innovante de programmation mathématique qui modélise la stabilité et l'expansion des capacités, et nous développons une méthode efficace de plan de coupe pour la résoudre. Des données réelles issues du système chilien de choix d'école valident l'impact potentiel de la planification de la capacité dans des conditions de stabilité. Dans la troisième étude, nous nous penchons sur la stabilité de l'appariement dans le cadre de priorités dynamiques, en nous concentrant principalement sur le choix de l'école. Nous introduisons un modèle qui tient compte des priorités des frères et sœurs, ce qui nécessite de nouveaux concepts de stabilité. Notre recherche identifie des scénarios où des appariements stables existent, accompagnés de mécanismes en temps polynomial pour leur découverte. Cependant, dans certains cas, nous prouvons également que la recherche d'un appariement stable de cardinalité maximale est NP-difficile sous des priorités dynamiques, ce qui met en lumière les défis liés à ces problèmes d'appariement. Collectivement, cette recherche contribue à une meilleure compréhension des capacités et des priorités dynamiques dans les scénarios d'appariement stable et ouvre de nouvelles questions et de nouvelles voies pour relever les défis d'allocation complexes dans le monde réel.This research addresses the dynamic facets in the fundamentals of the many-to-one stable matching problem. We conduct our study in the context of school choice and hospital-resident matching. In the first study, using the resident-hospital model, we investigate the computational complexity of optimizing hospital capacity variations to maximize resident outcomes, while respecting stability and budget constraints. Our findings reveal the NP-completeness of the decision problem and the inapproximability of the optimization problem, even under strict preferences and disjoint capacity allocations. These results pose significant challenges for policymakers seeking efficient solutions to pressing real-world issues. In the second study, using the school choice model, we explore the joint optimization of increasing school capacities and achieving student-optimal stable matchings within an expanded market. We devise an innovative mathematical programming formulation that models stability and capacity expansion, and we develop an effective cutting-plane method to solve it. Real-world data from the Chilean school choice system validates the potential impact of capacity planning under stability conditions. In the third study, we delve into stable matching under dynamic priorities, primarily focusing on school choice. We introduce a model that accounts for sibling priorities, necessitating novel stability concepts. Our research identifies scenarios where stable matchings exist, accompanied by polynomial-time mechanisms for their discovery. However, in some cases, we also prove the NP-hardness of finding a maximum cardinality stable matching under dynamic priorities, shedding light on challenges related to these matching problems. Collectively, this research contributes to a deeper understanding of dynamic capacities and priorities within stable matching scenarios and opens new questions and new avenues for tackling complex allocation challenges in real-world settings

The Shapley Value in the Knaster Gain Game

In Briata, Dall'Aglio and Fragnelli (2012), the authors introduce a coopera- tive game with transferable utility for allocating the gain of a collusion among completely risk-averse agents involved in the fair division procedure introduced by Knaster (1946). In this paper we analyze the Shapley value (Shapley, 1953) of the game and propose its use as a measure of the players' attitude towards collusion. Furthermore, we relate the sign of the Shapley value with the ranking order of the players' evaluation, and show that some players in a given ranking will always deter collusion. Finally, we characterize the coalitions that maximize the gain from collusion, and suggest an ad-hoc coalition formation mechanism