Abstract. When agents are acting together, they may need a simple mechanism to decide on joint actions. One possibility is to have the agents express their preferences in the form of a ballot and use a voting rule to decide the winning action(s). Unfortunately, agents may try to manipulate such an election by misreporting their preferences. Fortunately, it has been shown that it is NP-hard to compute how to manipulate a number of different voting rules. However, NPhardness only bounds the worst-case complexity. Recent theoretical results suggest that manipulation may often be easy in practice. To address this issue, I suggest studying empirically if computational complexity is in practice a barrier to manipulation. The basic tool used in my investigations is the identification of computational “phase transitions”. Such an approach has been fruitful in identifying hard instances of propositional satisfiability and other NP-hard problems. I show that phase transition behaviour gives insight into the hardness of manipulating voting rules, increasing concern that computational complexity is indeed any sort of barrier. Finally, I look at the problem of computing manipulation of other, related problems like stable marriage and tournament problems. 1

This comparative study examines the impact of backbone guided heuristics on the performance of dynamic local search methods. We study alternatives to the backbone membership estimation problem, discuss how our proposed estimation phase addresses it, and discuss how this information is integrated in the host methods. Backbone guidance results in significantly faster dynamic local search on the large problems tested, but it is of questionable use for small problem domains where the cost of backbone estimation alone represents a significant part of the total search cost.