Stochastic Apportionment

By Geoffrey Grimmett. 2002

23 citations recorded by the source corpus.

The problem of how to allocate to states the seats in the US House of Representatives is the most studied instance of what is termed the `apportionment problem'. We propose a new method of apportionment which is stochastic, which meets the quota condition, and which is fair in the sense of expectations. Two sources of systematic unfairness are identified, firstly the lower bound condition (every state shall receive at least one seat), and secondly the lower quota condition (every state shall receive at least the integer part of its quota).

Subfields: Gender Studies

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In Gender Studies

1. Michael Hay
2. Gerome Miklau
3. David Pujol
4. Ashwin Machanavajjhala
5. Ryan McKenna
6. Satya Kuppam

Citations in the corpus

• Fair decision making using privacy-protected data (2019, methodology): “We note that an expected deviation from quota of zero was used as a fairness criterion by Grimmett in the context of a randomized (but non-private) method for apportionment [20].”