Related papers: Learning to Manipulate under Limited Information

Learning to Manipulate under Limited Information

URL: http://arxiv.org/abs/2401.16412v2
Date: Tue, 16 Apr 2024 00:49:53 GMT
Title: Learning to Manipulate under Limited Information
Authors: Wesley H. Holliday, Alexander Kristoffersen, Eric Pacuit,
Abstract summary: We trained over 70,000 neural networks of 26 sizes to manipulate against 8 different voting methods. 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.
Score: 44.99833362998488
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
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 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.

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