Fiscal gap Fiscal gap The fiscal gap is a measure of a government's total indebtedness proposed by economists Laurence Kotlikoff and Alan Auerbach, who define it as the difference between the present value of all of government's projected financial obligations, including future expenditures, including servicing outstanding official federal debt, and the present value of all projected future tax and other receipts, including income accruing from the government's current ownership of financial assets. According to Kotlikoff and Auerbach, the "fiscal gap" accounting method can be used to calculate the percentage of necessary tax increases or spending reductions needed to close the fiscal gap
Word-sense induction on the identification of hubs in co-occurrence graphs, which have to cope with the need to tune a large number of parameters. To deal with this issue several graph-based algorithms have been proposed, which are based on simple graph patterns, namely Curvature Clustering, Squares, Triangles and Diamonds (SquaT++), and Balanced Maximum Spanning Tree Clustering (B-MST). The patterns aim at identifying meanings using the local structural properties of the co-occurrence graph. A randomized algorithm which partitions the graph vertices by iteratively transferring the mainstream message (i.e. word sense) to neighboring vertices is Chinese Whispers. By applying co-occurrence graphs approaches have been