# Collective discrete optimisation as judgment aggregation

arXiv: https://arxiv.org/abs/2112.00574 · HTML: https://mlsanity.com/paper/2112.00574/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 30 references that are in the map.
- First posted on arXiv 1 Dec 2021.

## Builds on (most cited first, 6 of 6)

- [Participatory Budgeting: Models and Approaches](https://mlsanity.com/paper/2003.00606/) (2020)
- [Knapsack Voting for Participatory Budgeting](https://mlsanity.com/paper/2009.06856/) (2020)
- [A Framework for Approval-based Budgeting Methods](https://mlsanity.com/paper/1809.04382/) (2018)
- [Collective Schedules: Scheduling Meets Computational Social Choice](https://mlsanity.com/paper/1803.07484/) (2018)
- [Egalitarian Judgment Aggregation](https://mlsanity.com/paper/2102.02785/) (2021)
- [A partial taxonomy of judgment aggregation rules, and their properties](https://mlsanity.com/paper/1502.05888/) (2015)

## Abstract (arXiv)

Many important collective decision-making problems can be seen as multi-agent versions of discrete optimisation problems. Participatory budgeting, for instance, is the collective version of the knapsack problem; other examples include collective scheduling, and collective spanning trees. Rather than developing a specific model, as well as specific algorithmic techniques, for each of these problems, we propose to represent and solve them in the unifying framework of judgment aggregation with weighted issues. We provide a modular definition of collective discrete optimisation (CDO) rules based on coupling a set scoring function with an operator, and we show how they generalise several existing procedures developed for specific CDO problems. We also give an implementation based on integer linear programming (ILP) and test it on the problem of collective spanning trees.