Summary of Policy Aggregation, by Parand A. Alamdari et al.
Policy Aggregation
by Parand A. Alamdari, Soroush Ebadian, Ariel D. Procaccia
First submitted to arxiv on: 6 Nov 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computer Science and Game Theory (cs.GT); Machine Learning (cs.LG)
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| 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 tackles the problem of aligning artificial intelligence (AI) values with multiple individuals who have different reward functions and optimal policies in a Markov decision process. The authors formalize this challenge as one of policy aggregation, aiming to identify a desirable collective policy. They propose an approach informed by social choice theory, arguing that it is particularly well-suited for this task. The key insight lies in reinterpreting social choice methods by identifying ordinal preferences with volumes of subsets of the state-action occupancy polytope. The authors demonstrate the practical application of various methods, including approval voting, Borda count, the proportional veto core, and quantile fairness, to policy aggregation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper tries to figure out how artificial intelligence (AI) can make decisions that are good for everyone when different people have different ideas about what’s best. This is like trying to decide which movie to watch with friends who all have different tastes. The authors use a special type of math called social choice theory to help find the right decision. They show that certain ways of making choices, like voting systems, can be used to combine individual preferences into a single collective decision. |
