Learning to Manipulate under Limited Information

Author:

Wesley H. Holliday, Alexander Kristoffersen, Eric Pacuit

Keyword:

Computer Science, Artificial Intelligence, Artificial Intelligence (cs.AI), Computer Science and Game Theory (cs.GT), Machine Learning (cs.LG), Multiagent Systems (cs.MA), Theoretical Economics (econ.TH)

journal:

date:

2024-01-29 00:00:00

Abstract

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 nearly 40,000 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.

PDF: Learning to Manipulate under Limited Information.pdf