Institutul de Matematică şi Informatică "Vladimir Andrunachievici"
Universitatea de Stat din MoldovaRO EN
Degree of favoring in apportionments
Authors: Ion Bolun
Keywords: apportionment method, comparative analysis, computer simulation, criteria, favoring large beneficiaries, favoring small beneficiaries.
– 0.36 Mb
Keywords: apportionment method, comparative analysis, computer simulation, criteria, favoring large beneficiaries, favoring small beneficiaries.
Abstract
To quantitatively estimate the degree of favoring the beneficiaries in proportional apportionments of entities of the same kind (seats, PCs, etc.), five quantitative criteria were defined. By computer simulation, the degree of favoring the large or small beneficiaries by 6 apportionment methods is identified. Thus, favoring large beneficiaries by the d’Hondt method can overpass 10.7-12.1 entities (entities in excess) and that of small beneficiaries by the Huntington-Hill method -- 2.7-11.0 entities, and by the Adapted Sainte-Lagu\"{e} method -- 1.7-9.7 entities. The Huntington-Hill method favors small beneficiaries up to 5.70 times stronger than the Adapted Sainte-Laguë one does. Also, the d’Hondt method favors beneficiaries (the large ones) much stronger than the Adapted Sainte-Laguë one does (the small ones) -- for very many cases the respective ratio exceeds 10 times.Technical Universty of Moldova
Studentilor, 9/7, Chisinau, Moldova
Phone: +373 22509908
E-mail:
Studentilor, 9/7, Chisinau, Moldova
Phone: +373 22509908
E-mail:
DOI
https://doi.org/10.56415/csjm.v30.11Fulltext
– 0.36 Mb
Contents
- On Elimination of Erasing Rules from E0S Grammars
- Vehicle Detection from Unmanned Aerial Images with Deep Mask R-CNN
- Identities and generalized derivatives of quasigroups
- Degree of favoring in apportionments
- Quadruplet loss and SqueezeNets for Covid-19 detection from Chest-X ray
- The Domination Parameters on a kind of the regular honeycomb structure
- Split logarithm problem and a candidate for a post-quantum signature scheme
- A Comparative study on classification performance of Emphysema with transfer learning methods in deep convolutional neural networks
- Svetlana Cojocaru - 70th anniversary
