A simple game (N, v) is given by a set N of n players and a partition of (2^N ) into a set ( L ) of losing coalitions L with value (v(L)=0 ) that is closed under taking subsets and a set ( W ) of winning coalitions W with (v(W)=1 ). Simple games with ( = _ p 0 _ W W ,L L p(L) p(W) 0 ).
We show that the following problem is NP-hard, and hence computationally intractable: "Given d weighted majority games, decide whether the dimension of their intersection exactly equals d". Our result indicates that the dimension of simple monotonic games is a combinatorially complicated concept.  This paper contributes to the program of numerical characterization and classification of simple games outlined in the classic monograph of von Neumann and Morgenstern. We suggest three possible ways to classify simple games beyond the classes of weighted and roughly weighted games. To this end we introduce three hierarchies of games and prove some relationships between their classes. We prove that our hierarchies are true (i.e., infinite) hierarchies. In particular, they are strict in the sense that more of the key “resource” (which may, for example, be the size or structure of the “tie-breaking” region where the weights of the different coalitions are considered so close that we are allowed to specify either winningness or nonwinningness of the coalition) yields the flexibility to capture strictly more games. Voting is a commonly applied method for the aggregation of the preferences of multiple agents into a joint decision. If preferences are binary, i.e., "yes" and "no", every voting system can be described by a (monotone) Boolean function @math . However, its naive encoding needs @math bits. The subclass of threshold functions, which is sufficient for homogeneous agents, allows a more succinct representation using @math weights and one threshold. For heterogeneous agents, one can represent @math as an intersection of @math threshold functions. Taylor and Zwicker have constructed a sequence of examples requiring @math and provided a construction guaranteeing @math . The magnitude of the worst-case situation was thought to be determined by in 2008, but the analysis unfortunately turned out to be wrong. Here we uncover a relation to coding theory that allows the determination of the minimum number @math for a subclass of voting systems. As an application, we give a construction for @math , i.e., there is no gain from a representation complexity point of view.