Modeling Voters in Multi-Winner Approval Voting
Modeling Voters in Multi-Winner Approval Voting
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DOI：
10.1609/aaai.v35i6.16716
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发表时间：
2020-12
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影响因子：
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通讯作者：
J. Scheuerman;J. Harman;Nicholas Mattei;K. Venable
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文献类型：
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作者：
J. Scheuerman;J. Harman;Nicholas Mattei;K. Venable
在许多现实世界的情况下，集体决策是通过投票做出的，在委员会或董事会选举等情况下，采用了产生多个赢家的投票规则。在多赢家批准投票(AV)中，代理人提交一张选票，其中包括对他们希望的任意多个候选人的批准，然后通过统计选票并选择获得最多批准的前k名候选人来选出获胜者。在许多情况下，代理人可能会操纵他们提交的选票，以不反映他们真实偏好的方式投票，以获得更好的结果。在复杂和不确定的情况下，代理人可能会使用启发式方法，而不是招致计算最有利于他们的操作所需的额外工作。在本文中，我们使用从机械土耳其人那里获得的行为数据，检验了具有不同程度不确定性的单赢家和多赢家批准投票场景中的投票行为。我们发现，人们普遍操纵自己的选票以获得更好的结果，但往往不确定最优的操纵方式。在社会选择和心理学文献中，有许多关于代理人行为的预测模型，这些模型基于认知上可信的启发式策略。我们表明，现有的方法不能对我们的真实数据进行充分的建模。我们提出了一种新的模型，该模型考虑了获胜集合的大小和人类的认知约束，并证明了该模型在多赢家投票场景中更有效地捕捉真实世界的行为。
In many real world situations, collective decisions are made using voting and, in scenarios such as committee or board elections, employing voting rules that return multiple winners. In multi-winner approval voting (AV), an agent submits a ballot consisting of approvals for as many candidates as they wish, and winners are chosen by tallying up the votes and choosing the top-k candidates receiving the most approvals. In many scenarios, an agent may manipulate the ballot they submit in order to achieve a better outcome by voting in a way that does not reflect their true preferences. In complex and uncertain situations, agents may use heuristics instead of incurring the additional effort required to compute the manipulation which most favors them. In this paper, we examine voting behavior in single-winner and multi-winner approval voting scenarios with varying degrees of uncertainty using behavioral data obtained from Mechanical Turk. We find that people generally manipulate their vote to obtain a better outcome, but often do not identify the optimal manipulation. There are a number of predictive models of agent behavior in the social choice and psychology literature that are based on cognitively plausible heuristic strategies. We show that the existing approaches do not adequately model our real-world data. We propose a novel model that takes into account the size of the winning set and human cognitive constraints; and demonstrate that this model is more effective at capturing real-world behaviors in multi-winner approval voting scenarios.