wawasan - Machine Learning - # Strategic Voting with Limited Information

Manipulating Elections under Limited Information: An Empirical Study of Neural Network Strategies

Q: How would the results change if we considered manipulation by a coalition of voters rather than a single voter

Considering manipulation by a coalition of voters rather than a single voter would likely lead to more complex and potentially more impactful manipulation strategies. A coalition of voters could coordinate their actions to strategically influence the outcome of an election by pooling their resources and knowledge. This could involve strategic voting, strategic nomination of candidates, or even strategic control of the voting process itself. The results would change in that the manipulation strategies employed by a coalition would be more sophisticated and potentially more successful in achieving their desired outcomes compared to individual voters acting alone. The coalition could exploit synergies and complement each other's strengths to maximize their manipulative power.

Q: What are the social costs or benefits of the learned manipulation strategies, and how do they compare to the costs/benefits of manipulation by an ideal, fully informed agent

The social costs or benefits of the learned manipulation strategies depend on various factors such as the voting method, the type of information available, and the specific context of the election. In general, the benefits of manipulation strategies lie in the ability to influence the outcome of an election in favor of the manipulator's preferences. This can lead to more favorable policy outcomes, representation, or power for the manipulator or their group. However, the costs of manipulation include undermining the democratic process, eroding trust in the electoral system, and potentially leading to suboptimal or unfair outcomes for society as a whole. Comparing the costs and benefits of manipulation by a learned agent versus an ideal, fully informed agent, the learned agent may be more limited in its ability to accurately predict and manipulate election outcomes. While the learned agent can exploit patterns in the data to develop manipulation strategies, it may not have the same level of strategic insight and foresight as an ideal agent with complete information. This could result in less effective manipulation strategies and potentially lower social costs or benefits compared to manipulation by an ideal agent.

Q: Can a reinforcement learning approach be used to overcome the scalability limitations of the classification approach used in this paper and study manipulation in elections with more candidates

A reinforcement learning approach could potentially overcome the scalability limitations of the classification approach used in the paper and study manipulation in elections with more candidates. By using reinforcement learning, agents can learn to interact with their environment, make decisions, and adapt their strategies based on feedback received. This approach could allow agents to learn more complex and adaptive manipulation strategies in larger-scale elections with multiple candidates. Reinforcement learning could enable agents to explore a wider range of strategies, learn from their experiences, and optimize their decision-making processes over time. By training agents to manipulate elections using reinforcement learning, researchers could potentially study manipulation in more complex scenarios, including elections with a larger number of candidates and voters. This approach could provide insights into the dynamics of manipulation in real-world electoral systems and help understand the implications of strategic behavior in voting processes.

Konsep Inti

Neural networks can learn to profitably manipulate some voting methods, like Borda, even with limited information about voter preferences, while other methods, like Instant Runoff and Condorcet-consistent methods, are more resistant to manipulation.

Abstrak

The paper investigates how difficult it is for a computationally bounded agent to learn to manipulate different voting methods under limited information. The authors train multi-layer perceptron (MLP) neural networks to manipulate 8 different voting methods, including Plurality, Borda, Instant Runoff, and Condorcet-consistent methods, given 6 types of limited information about voter preferences.

The key findings are:

Knowing the majority matrix (which candidate beats which other candidates head-to-head) is often sufficient for MLPs to learn profitable manipulation strategies, even if they don't have full information about voter preferences.
Plurality and Borda are highly manipulable by MLPs, even with limited information. In contrast, Instant Runoff and Condorcet-consistent methods like Minimax and Split Cycle are more resistant to manipulation.
The size and complexity of the MLP required to learn profitable manipulation strategies can serve as a proxy for the difficulty of manipulation. Borda, for example, can be manipulated by relatively small MLPs, while Minimax and Split Cycle require larger, more complex networks.
The authors also find differences in manipulability between the uniform utility model and a 2D spatial model for generating voter preferences, with Condorcet-consistent methods becoming less manipulable under the spatial model.

Overall, the paper demonstrates that machine learning can be a useful tool for studying the strategic manipulability of different voting methods, providing insights that go beyond previous complexity-theoretic analyses.

Kustomisasi Ringkasan

Tulis Ulang dengan AI

Buat Sitasi

Terjemahkan Sumber

Ke Bahasa Lain

Buat Peta Pikiran

dari konten sumber

Kunjungi Sumber

arxiv.org

Statistik

"By classic results in social choice theory, any reasonable preferential voting method sometimes gives individuals an incentive to report an insincere preference."
"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."

Kutipan

"Sufficiently large MLPs learned to profitably manipulate all voting methods we studied on the basis of knowing only the majority matrix, though the profitability of such manipulation varied dramatically across methods."
"IRV and IRV-PUT were quite resistant to manipulation on the basis of limited information (with the exception of the manipulability of IRV for 3 candidates and 10 voters), despite the fact that these methods are more manipulable than some others by an ideal manipulator."
"Minimax, Nanson, and Split Cycle became less profitably manipulable (roughly by one half) under the spatial model compared to the uniform utility model."

Wawasan Utama Disaring Dari

Learning to Manipulate under Limited Information

by Wesley H. Ho... pada arxiv.org 04-17-2024

https://arxiv.org/pdf/2401.16412.pdf

Learning to Manipulate under Limited Information

Pertanyaan yang Lebih Dalam

How would the results change if we considered manipulation by a coalition of voters rather than a single voter

Considering manipulation by a coalition of voters rather than a single voter would likely lead to more complex and potentially more impactful manipulation strategies. A coalition of voters could coordinate their actions to strategically influence the outcome of an election by pooling their resources and knowledge. This could involve strategic voting, strategic nomination of candidates, or even strategic control of the voting process itself. The results would change in that the manipulation strategies employed by a coalition would be more sophisticated and potentially more successful in achieving their desired outcomes compared to individual voters acting alone. The coalition could exploit synergies and complement each other's strengths to maximize their manipulative power.

What are the social costs or benefits of the learned manipulation strategies, and how do they compare to the costs/benefits of manipulation by an ideal, fully informed agent

The social costs or benefits of the learned manipulation strategies depend on various factors such as the voting method, the type of information available, and the specific context of the election. In general, the benefits of manipulation strategies lie in the ability to influence the outcome of an election in favor of the manipulator's preferences. This can lead to more favorable policy outcomes, representation, or power for the manipulator or their group. However, the costs of manipulation include undermining the democratic process, eroding trust in the electoral system, and potentially leading to suboptimal or unfair outcomes for society as a whole.
Comparing the costs and benefits of manipulation by a learned agent versus an ideal, fully informed agent, the learned agent may be more limited in its ability to accurately predict and manipulate election outcomes. While the learned agent can exploit patterns in the data to develop manipulation strategies, it may not have the same level of strategic insight and foresight as an ideal agent with complete information. This could result in less effective manipulation strategies and potentially lower social costs or benefits compared to manipulation by an ideal agent.

Can a reinforcement learning approach be used to overcome the scalability limitations of the classification approach used in this paper and study manipulation in elections with more candidates

A reinforcement learning approach could potentially overcome the scalability limitations of the classification approach used in the paper and study manipulation in elections with more candidates. By using reinforcement learning, agents can learn to interact with their environment, make decisions, and adapt their strategies based on feedback received. This approach could allow agents to learn more complex and adaptive manipulation strategies in larger-scale elections with multiple candidates.
Reinforcement learning could enable agents to explore a wider range of strategies, learn from their experiences, and optimize their decision-making processes over time. By training agents to manipulate elections using reinforcement learning, researchers could potentially study manipulation in more complex scenarios, including elections with a larger number of candidates and voters. This approach could provide insights into the dynamics of manipulation in real-world electoral systems and help understand the implications of strategic behavior in voting processes.