Summary of Learning to Manipulate Under Limited Information, by Wesley H. Holliday and Alexander Kristoffersen and Eric Pacuit
Learning to Manipulate under Limited Information
by Wesley H. Holliday, Alexander Kristoffersen, Eric Pacuit
First submitted to arxiv on: 29 Jan 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computer Science and Game Theory (cs.GT); Machine Learning (cs.LG); Multiagent Systems (cs.MA); Theoretical Economics (econ.TH)
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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 investigates the resistance of various voting methods to strategic manipulation by artificial neural networks. By training over 100,000 networks with different sizes and limited information about other voters’ preferences, researchers evaluate the manipulability of eight distinct voting methods in committee-sized elections. The findings indicate that some methods, such as Borda, are highly susceptible to manipulation, while others, like Instant Runoff, exhibit resistance despite being vulnerable to idealized manipulators. Notably, Condorcet methods, including Minimax and Split Cycle, prove to be the least manipulable among the studied methods across three probability models for elections. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research explores how different voting systems can be manipulated by artificial intelligence. The scientists trained many neural networks with limited information about other voters’ opinions and tested them on eight different voting methods. They found that some methods are easy to manipulate, while others are harder to trick. Interestingly, a specific type of voting system called Condorcet methods is the most resistant to manipulation. |
Keywords
* Artificial intelligence * Probability
