تقرير
Learning to Manipulate under Limited Information
| العنوان: | Learning to Manipulate under Limited Information |
|---|---|
| المؤلفون: | Holliday, Wesley H., Kristoffersen, Alexander, Pacuit, Eric |
| سنة النشر: | 2024 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Artificial Intelligence, Computer Science - Computer Science and Game Theory, Computer Science - Machine Learning, Computer Science - Multiagent Systems, Economics - Theoretical Economics, 91B12, 91B14, 91B10, 68T07, I.2.6, I.2.11 |
| الوصف: | By classic results in social choice theory, any reasonable preferential voting method sometimes gives individuals an incentive to report an insincere preference. The extent to which different voting methods are more or less resistant to such strategic manipulation has become a key consideration for comparing voting methods. Here we measure resistance to manipulation by whether neural networks of varying sizes can learn to profitably manipulate a given voting method in expectation, given different types of limited information about how other voters will vote. We trained over 70,000 neural networks of 26 sizes to manipulate against 8 different voting methods, under 6 types of limited information, in committee-sized elections with 5-21 voters and 3-6 candidates. We find that some voting methods, such as Borda, are highly manipulable by networks with limited information, while others, such as Instant Runoff, are not, despite being quite profitably manipulated by an ideal manipulator with full information. For the two probability models for elections that we use, the overall least manipulable of the 8 methods we study are Condorcet methods, namely Minimax and Split Cycle. Comment: Appears at the 1st Workshop on Social Choice and Learning Algorithms (SCaLA 2024) held at the 23rd International Conference on Autonomous Agents and Multiagent Systems, organized by B. Armstrong, R. Fairstein, N. Mattei, and Z. Terzopoulou, May 6-7, 2024, Auckland, New Zealand |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2401.16412 |
| رقم الأكسشن: | edsarx.2401.16412 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |