An Empirical Study of the Manipulability of Single Transferable Voting

Voting is a simple mechanism to combine together the preferences of multiple agents. Agents may try to manipulate the result of voting by mis-reporting their preferences. One barrier that might exist to such manipulation is computational complexity. In particular, it has been shown that it is NP-hard to compute how to manipulate a number of different voting rules. However, NP-hardness only bounds the worst-case complexity. Recent theoretical results suggest that manipulation may often be easy in practice. In this paper, we study empirically the manipulability of single transferable voting (STV) to determine if computational complexity is really a barrier to manipulation. STV was one of the first voting rules shown to be NP-hard. It also appears one of the harder voting rules to manipulate. We sample a number of distributions of votes including uniform and real world elections. In almost every election in our experiments, it was easy to compute how a single agent could manipulate the election or to prove that manipulation by a single agent was impossible.Comment: To appear in Proceedings of the 19th European Conference on Artificial Intelligence (ECAI 2010

Complexity of and Algorithms for Borda Manipulation

We prove that it is NP-hard for a coalition of two manipulators to compute how to manipulate the Borda voting rule. This resolves one of the last open problems in the computational complexity of manipulating common voting rules. Because of this NP-hardness, we treat computing a manipulation as an approximation problem where we try to minimize the number of manipulators. Based on ideas from bin packing and multiprocessor scheduling, we propose two new approximation methods to compute manipulations of the Borda rule. Experiments show that these methods significantly outperform the previous best known %existing approximation method. We are able to find optimal manipulations in almost all the randomly generated elections tested. Our results suggest that, whilst computing a manipulation of the Borda rule by a coalition is NP-hard, computational complexity may provide only a weak barrier against manipulation in practice

Allocation in Practice

How do we allocate scarcere sources? How do we fairly allocate costs? These are two pressing challenges facing society today. I discuss two recent projects at NICTA concerning resource and cost allocation. In the first, we have been working with FoodBank Local, a social startup working in collaboration with food bank charities around the world to optimise the logistics of collecting and distributing donated food. Before we can distribute this food, we must decide how to allocate it to different charities and food kitchens. This gives rise to a fair division problem with several new dimensions, rarely considered in the literature. In the second, we have been looking at cost allocation within the distribution network of a large multinational company. This also has several new dimensions rarely considered in the literature.Comment: To appear in Proc. of 37th edition of the German Conference on Artificial Intelligence (KI 2014), Springer LNC

Élection d'un chemin dans un réseau : étude de la manipulabilité

International audienceInternet est devenu un écosystème économique où interviennent de nombreux acteurs concurrents. Pour les décisions faisant intervenir plusieurs acteurs, il faut un mécanisme équitable qui évite si possible la manipulation du processus de prise de décision par certains. Dans cet article, nous illustrons comment les systèmes de vote peuvent être appliqués sur le cas du choix d'un chemin dans le réseau qui fait intervenir plusieurs opérateurs, et nous montrons que le choix du système de vote a un fort impact sur la manipulabilité du résultat et l'efficacité globale du processus de décision. Une version étendue de cet article a été publiée à ICQT 2013

Designing a Multiagent System for Course-Offering Determination

I attended a doctoral symposium of the conference. It was very good to know about how to guide PhD students to conduct high-quality research and complete PhD program. I attended all the keynote sessions of the conference. The presentation on Computational Disaster Management by Professor Pascal Van Hentenryck was very insightful and encouraging. The talk on “Agents might not be people” by Professor Nigel Gilbert was illuminating. The talk on “Satisfiability to Linear Algebra” by Professor Fangzhen Lin was revealing. I attended almost all sections of PRIMA 2013 and some presentations of AI 2013. They reflect the advancement of the field. The discussions with the people on-site were very interesting and helpful to my future research. Also, it was great to talk to active researchers in the field and exchanged ideas of our research and explored the possibility of collaboration.This paper describes the design of a multiagent system that facilitates course-offering decision making for a program in an institution. We first model course offering determination for upcoming semester as a multi-winner election with exogenous constraints which is a problem for computational social choice in multiagent systems, which has rarely been considered. Then, the paper describes the architecture and models of the multiagent system for course offering determination with Gaia role model methodology, TROPOS strategic actor diagram, Agent Unified Modeling Language (AUML) sequence diagram for a multi-agent negotiation interaction protocol, and Pseudo-code algorithms for generating fractional votes and course election protocol. A novel course selection preference model for students has been proposed and described formally. The effectiveness of the approach and the implemented system has been showed with the initial experimental results