Fair Knapsack

We study the following multiagent variant of the knapsack problem. We are given a set of items, a set of voters, and a value of the budget; each item is endowed with a cost and each voter assigns to each item a certain value. The goal is to select a subset of items with the total cost not exceeding the budget, in a way that is consistent with the voters' preferences. Since the preferences of the voters over the items can vary significantly, we need a way of aggregating these preferences, in order to select the socially best valid knapsack. We study three approaches to aggregating voters' preferences, which are motivated by the literature on multiwinner elections and fair allocation. This way we introduce the concepts of individually best, diverse, and fair knapsack. We study the computational complexity (including parameterized complexity, and complexity under restricted domains) of the aforementioned multiagent variants of knapsack.Comment: Extended abstract will appear in Proc. of 33rd AAAI 201

Fair assignment of indivisible objects under ordinal preferences

We consider the discrete assignment problem in which agents express ordinal preferences over objects and these objects are allocated to the agents in a fair manner. We use the stochastic dominance relation between fractional or randomized allocations to systematically define varying notions of proportionality and envy-freeness for discrete assignments. The computational complexity of checking whether a fair assignment exists is studied for these fairness notions. We also characterize the conditions under which a fair assignment is guaranteed to exist. For a number of fairness concepts, polynomial-time algorithms are presented to check whether a fair assignment exists. Our algorithmic results also extend to the case of unequal entitlements of agents. Our NP-hardness result, which holds for several variants of envy-freeness, answers an open question posed by Bouveret, Endriss, and Lang (ECAI 2010). We also propose fairness concepts that always suggest a non-empty set of assignments with meaningful fairness properties. Among these concepts, optimal proportionality and optimal weak proportionality appear to be desirable fairness concepts.Comment: extended version of a paper presented at AAMAS 201

Gerechte Zuordnungen: Kollektive Entscheidungsprobleme aus der Perspektive von Mathematik und theoretischer Informatik

Wir untersuchen verschiedene Fragestellungen der Sozialwahltheorie aus Sicht der Computational Social Choice. Für ein Problem, das in Bezug zu einem Kollektiv von Agenten steht (z.B. Aufteilungen von Ressourcen oder Repräsentantenwahlen), stehen verschiedene Alternativen als Lösung zur Verfügung; ein wesentlicher Aspekt sind dabei die diversen Pr\"aferenzen der Agenten gegenüber den Alternativen. Die Qualität der Lösungen wird anhand von Kriterien aus den Sozialwissenschaften (Fairness), der Spieltheorie (Stabilität) und den Wirtschaftswissenschaften (Effizienz) charakterisiert. In Computational Social Choice werden solche Fragestellungen mit Werkzeugen der Mathematik (z.B. Logik und Kombinatorik) und Informatik (z.B. Komplexitätstheorie und Algorithmik) behandelt. Als roter Faden zieht sich die Frage nach sogenannten "`gerechten Zuordnungen"' durch die Dissertation. Für die Zuordnung von Gütern zu Agenten zeigen wir, wie mithilfe eines dezentralisierten Ansatzes Zuordnungen gefunden werden können, die Ungleichheit minimieren. Wir analysieren das Verhalten dieses Ansatzes für Worst-Case-Instanzen und benutzen dabei eine innovative Beweismethode, die auf impliziten rekursiven Konstruktionen unter Verwendung von Argumenten der Infinitesimalrechnung beruht. Bei der Zuordnung von Agenten zu Aktivitäten betrachten wir das vereinfachte Szenario, in dem die Agenten Präferenzen bezüglich der Aktivitäten haben und die Menge der zulässigen Zuordnungen Beschränkungen bezüglich der Teilnehmerzahlen pro Aktivität unterliegt. Wir führen verschiedene Lösungskonzepte ein und erläutern die Zusammenhänge und Unterschiede dieser Konzepte. Die zugehörigen Entscheidungsprobleme zur Existenz und Maximalität entsprechender Zuordnungen unterziehen wir einer ausführlichen Komplexitätsanalyse. Zuordnungsprobleme können auch als Auktionen aufgefasst werden. Wir betrachten ein Szenario, in dem die Agenten Gebote auf Transformationen von Gütermengen abgeben. In unserem Modell sind diese durch die Existenz von Gütern charakterisiert, die durch die Transformationen nicht verbraucht werden. Von Interesse sind die Kombinationen von Transformationen, die den Gesamtnutzen maximieren. Wir legen eine (parametrisierte) Komplexitätsanalyse dieses Modells vor. Etwas abseits der Grundfragestellung liegen unsere Untersuchungen zu kombinierten Wettkämpfen. Diese interpretieren wir als Wahlproblem, d.h. als Aggregation von Ordnungen. Wir untersuchen die Anfälligkeit für Manipulationen durch die Athleten.We investigate questions from social choice theory from the viewpoint of computational social choice. We consider the setting that a group of agents faces a collective decision problem (e.g., resource allocation or the choice of a representative): they have to choose among various alternatives. A crucial aspect are the agents' individual preferences over these alternatives. The quality of the solutions is measured by various criteria from the fields of social sciences (fairness), game theory (stability) and economics (efficiency). In computational social choice, such problems are analyzed and accessed via methods of mathematics (e.g., logic and combinatoric) and theoretical computer science (e.g. complexity theory and algorithms). The question of so called `fair assignments' runs like a common thread through most parts of this dissertation. Regarding allocations of goods to agents, we show how to achieve allocations with minimal inequality by means of a distributed approach. We analyze the behavior of this approach for worst case instances; therefor we use an innovative proof technique which relies on implicit recursive constructions and insights from basic calculus. For assignments of agents to activities, we consider a simplified scenario where the agents express preferences over activities and the set of feasible assignments is restricted by the number of agents which can participate in a (specific) activity. We introduce several solution concepts and elucidate the connections and differences between these concepts. Furthermore, we provide an elaborated complexity analysis of the associated decision problems addressing existence and maximality of the corresponding solution concepts. Assignment problems can also be seen as auctions. We consider a scenario where the agents bid on transformations of goods. In our model, each transformation requires the existence of a `tool good' which is not consumed by the transformation. We are interested in combinations of transformations which maximize the total utility. We study the computational complexity of this model in great detail, using methods from both classical and parameterized complexity theory. Slightly off topic are our investigations on combined competitions. We interpret these as a voting problem, i.e., as the aggregation of orders. We investigate the susceptibility of these competitions to manipulation by the athletes

Mechanism design for distributed task and resource allocation among self-interested agents in virtual organizations

The aggregate power of all resources on the Internet is enormous. The Internet can be viewed as a massive virtual organization that holds tremendous amounts of information and resources with different ownerships. However, little is known about how to run this organization efficiently. This dissertation studies the problems of distributed task and resource allocation among self-interested agents in virtual organizations. The developed solutions are not allocation mechanisms that can be imposed by a centralized designer, but decentralized interaction mechanisms that provide incentives to self-interested agents to behave cooperatively. These mechanisms also take computational tractability into consideration due to the inherent complexity of distributed task and resource allocation problems. Targeted allocation mechanisms can achieve global task allocation efficiency in a virtual organization and establish stable resource-sharing communities based on agentsâÃÂàown decisions about whether or not to behave cooperatively. This high level goal requires solving the following problems: synthetic task allocation, decentralized coalition formation and automated multiparty negotiation. For synthetic task allocation, in which each task needs to be accomplished by a virtual team composed of self-interested agents from different real organizations, my approach is to formalize the synthetic task allocation problem as an algorithmic mechanism design optimization problem. I have developed two approximation mechanisms that I prove are incentive compatible for a synthetic task allocation problem. This dissertation also develops a decentralized coalition formation mechanism, which is based on explicit negotiation among self-interested agents. Each agent makes its own decisions about whether or not to join a candidate coalition. The resulting coalitions are stable in the core in terms of coalition rationality. I have applied this mechanism to form resource sharing coalitions in computational grids and buyer coalitions in electronic markets. The developed negotiation mechanism in the decentralized coalition formation mechanism realizes automated multilateral negotiation among self-interested agents who have symmetric authority (i.e., no mediator exists and agents are peers). In combination, the decentralized allocation mechanisms presented in this dissertation lay a foundation for realizing automated resource management in open and scalable virtual organizations