Summary of Learning Aggregation Rules in Participatory Budgeting: a Data-driven Approach, by Roy Fairstein et al.
Learning Aggregation Rules in Participatory Budgeting: A Data-Driven Approach
by Roy Fairstein, Dan Vilenchik, Kobi Gal
First submitted to arxiv on: 1 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computers and Society (cs.CY); Computer Science and Game Theory (cs.GT)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents a novel machine learning-based approach to address the challenge faced by Participatory Budgeting (PB) organizers in selecting aggregation rules. The method utilizes neural networks trained on PB instances to learn rules that balance social welfare, representation, and other societal goals. The approach is able to generalize from small-scale synthetic examples to large real-world instances, generate new rules that adapt to diverse objectives, and provide a more nuanced compromise-driven solution for PB processes. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Participatory Budgeting (PB) lets communities decide how public money is spent on different projects by voting. When choosing which way to add up the votes, organizers face problems because they might not know about existing rules or want a specific rule that doesn’t exist yet. This paper develops a new way using machine learning to help with this challenge. It trains special computers (neural networks) on PB examples and teaches them to create rules that balance fairness, equality, and other important goals. The approach can use small test cases to learn how to apply the rules in real-life situations. It can also create new rules that fit different community needs, making it a more fair and helpful way for PB processes. |
Keywords
* Artificial intelligence * Machine learning
