TY - BOOK ID - 85646028 TI - Votes from seats : logical models of electoral systems AU - Shugart, Matthew Soberg AU - Taagepera, Rein PY - 2017 SN - 1108265731 1108261124 1108271197 1108417027 110840426X PB - Cambridge : Cambridge University Press, DB - UniCat KW - Elections KW - Election districts KW - Legislative bodies KW - Representative government and representation KW - Parliamentary government KW - Political representation KW - Representation KW - Self-government KW - Constitutional history KW - Constitutional law KW - Political science KW - Democracy KW - Republics KW - Suffrage KW - Bicameralism KW - Legislatures KW - Parliaments KW - Unicameral legislatures KW - Estates (Social orders) KW - Aldermanic districts KW - Legislative districts KW - Precincts (Political science) KW - Voting precincts KW - Wards (Political science) KW - Administrative and political divisions KW - Apportionment (Election law) KW - Electoral politics KW - Franchise KW - Polls KW - Politics, Practical KW - Plebiscite KW - Political campaigns KW - Mathematical models. UR - https://www.unicat.be/uniCat?func=search&query=sysid:85646028 AB - Take the number of seats in a representative assembly and the number of seats in districts through which this assembly is elected. From just these two numbers, the authors of Votes from Seats show that it is possible to deduce the number of parties in the assembly and in the electorate, as well as the size of the largest party. Inside parties, the vote distributions of individual candidates likewise follow predictable patterns. Four laws of party seats and votes are constructed by logic and tested, using scientific approaches rare in social sciences. Both complex and simple electoral systems are covered, and the book offers a set of 'best practices' for electoral system design. The ability to predict so much from so little, and to apply to countries worldwide, is an advance in the systematic analysis of a core institutional feature found in any democracy, and points the way towards making social sciences more predictive. ER -