We propose an empirically relevant measure of voting power that uses the information about real or assumed voting patterns conveyed by a joint probability distribution on the set of voting outcomes, and apply it to the voting data of the Supreme Court of the United States.

Lecture Notes prepared for Summer School, “EU Decision Making : Assessment and Design of Procedures”, San Sebastian, Spain, July 8-11, 2002.

The Double Majority rule in the Treaty is claimed to be simpler, more transparent and more democratic than the existing rule. We examine these questions against the democratic ideal that the votes of all citizens in whatever member country should be of equal value using voting power analysis...

Weighted voting games are ubiquitous mathematical models which are used in economics, political science, neuroscience, threshold logic, reliability theory and distributed systems. They model situations where agents with variable voting weight vote in favour of or against a decision. A coalition...

The constitutions of the Bretton Woods Institutions require decisions to be taken by weighted voting: each member country possesses a number of votes, depending on its quota allocation, all of which must always be cast as a bloc. This leads to a problem of democratic legitimacy since a...

We investigate the applicability of voting power indices, in particular the Penrose index (aka absolute Banzhaf index), in the analysis of voting blocs by means of a hypothetical voting body. We use the power of individual bloc members to study the implications of the formation of blocs and how...

A large number of empirical studies have examined the question of the divorce between ownership and control as to either its extent (following Berle and Means (1932) or its implications for behaviour (following Marris (1964)). These studies have used different samples, different variables and...

There is no consensus on the properties of voting power indices when there are a large number of voters in a weighted voting body. On the one hand, in some real-world cases that have been studied the power indices have been found to be nearly proportional to the weights (eg the EUCM, US...

Much current research in functional MRI employs multivariate machine learning approaches (e.g., support vector machines) to detect fine-scale spatial patterns from the temporal fluctuations of the neural signal. The aim of many studies is not classification, however, but investigation of...

An increasing trend in functional MRI experiments involves discriminating between experimental conditions on the basis of fine-grained spatial patterns extending across many voxels. Typically, these approaches have used randomized resampling to derive inferences. Here, we introduce an analytical...