To allow society to treat unequal alternatives distinctly we propose a natural extension of Approval Voting [7] by relaxing the assumption of neutrality. According to this extension, every alternative receives ex-ante a non-negative and finite weight. These weights may differ across alternatives. Given the voting decisions of every individual (individuals are allowed to vote for, or approve of,...

We describe an ensemble of classifiers based algorithm for incremental learning in nonstationary environments. In this formulation, we assume that the learner is presented with a series of training datasets, each of which is drawn from a different snapshot of a distribution that is drifting at an unknown rate. Furthermore, we assume that the algorithm must learn the new environment in an increm...

When wise people take critical decisions, they usually take into account the opinions from many experts. Now whether to give equal importance to each expert’s opinion or weigh them according to their expertise, their experience is another good logical problem to think on. The main focus of this paper is to closely compare the performance of equally weighted voting (democracy) schemes in Machine...

Mobile and loosely-coupled environments call for decentralized optimistic replication protocols that provide highly available access to shared objects. A fundamental property of most optimistic protocols is to guarantee an eventual consensus on a commit order among the set of tentatively issued updates. In this paper we propose a replicated object protocol that employs a novel epidemic weighted...

Weighted voting games allow for studying the distribution of power between agents in situations of collective decision making. While the conventional version of these games assumes that any agent is always ready to cooperate with all others, recently, more involved models have been proposed, where cooperation is subject to restrictions. Following Myerson [1977], such restrictions are typically ...

Multiple Species Weighted Voting (MSWV) is a genetics-based machine learning (GBML) system with relatively few parameters that combines N two-class classifiers into an N -class classifier. MSWV uses two levels of speciation, one manual (a separate species is assigned to each two-class classifier) and one automatic, to reduce the size of the search space and also increase the accuracy of the dec...

We show that a number of election-related problems with prices (such as, for example, bribery) are fixed-parameter tractable (in FPT) when parameterized by the number of candidates. For bribery, this resolves a nearly 10-year old family of open problems. Our results follow by a general technique that formulates voting problems as covering problems and extends the classic approach of using integ...

We propose a new power index based on the minimum sum representation (MSR) of a weighted voting game. The MSR offers a redesign of a voting game, such that voting power as measured by the MSR index becomes proportional to voting weight. The MSR index is a coherent measure of power that is ordinally equivalent to the Banzhaf, Shapley-Shubik and Johnston indices. We provide a characterization for...

Symmetry is an important composition feature by investigating similar sides inside an image plane. It has a crucial effect to recognize man-made or nature objects within the universe. Recent symmetry detection approaches used a smoothing kernel over different voting maps in the polar coordinate system to detect symmetry peaks, which split the regions of symmetry axis candidates in inefficient w...

We initiate the study of dynamic cooperative games—cooperative games where the characteristic function may change over time. We introduce two types of algorithmic problems for such games: computing a given solution concept at time t, and checking that a certain function of the game (e.g., the Shapley value of a given player or the value of the least core) remains within given bounds during time...