A Mathematical Model for Optimal Decisions in a Representative Democracy
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Abstract (arXiv)
Direct democracy is a special case of an ensemble of classifiers, where every person (classifier) votes on every issue. This fails when the average voter competence (classifier accuracy) falls below 50%, which can happen in noisy settings where voters have only limited information, or when there are multiple topics and the average voter competence may not be high enough for some topics. Representative democracy, where voters choose representatives to vote, can be an elixir in both these situations. Representative democracy is a specific way to improve the ensemble of classifiers. We introduce a mathematical model for studying representative democracy, in particular understanding the parameters of a representative democracy that gives maximum decision making capability. Our main result states that under general and natural conditions, 1. Representative democracy can make the correct decisions simultaneously for multiple noisy issues. 2. When the cost of voting is fixed, the optimal representative democracy requires that representatives are elected from constant sized groups: the number of representatives should be linear in the number of voters. 3. When the cost and benefit of voting are both polynomial, the optimal group size is close to linear in the number of voters. This work sets the mathematical foundation for studying the quality-quantity tradeoff in a representative democracy-type ensemble (fewer highly qualified representatives versus more less qualified representatives).
Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). First posted on arXiv 17 Jul 2018.
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Citing papers per year: 2021: 1; 2022: 2; 2023: 1; 2024: 1.
5 papers.
- 2024Understanding "Democratization" in NLP and ML Researchcited by 3
- 2022Who Reviews The Reviewers? A Multi-Level Jury Problemcited by 2
- 2021The Optimal Size of an Epistemic Congresscited by 1
- 2022Towards Group Learning: Distributed Weighting of Expertscited by 0
- 2023The Distortion of Binomial Voting Defies Expectationcited by 0
- 2024Understanding "Democratization" in NLP and ML Research17 Jun 2024
- 2023The Distortion of Binomial Voting Defies Expectation27 Jun 2023
- 2022Who Reviews The Reviewers? A Multi-Level Jury Problem15 Nov 2022
- 2022Towards Group Learning: Distributed Weighting of Experts3 Jun 2022
- 2021The Optimal Size of an Epistemic Congress2 Jul 2021