论文标题
客观的社会选择：使用辅助信息来改善投票结果
Objective Social Choice: Using Auxiliary Information to Improve Voting Outcomes
论文作者
论文摘要
一个人应该如何将来自不同来源的嘈杂信息结合起来，以推断客观的基础真理？这个经常反复出现的规范性问题在于统计，机器学习，政策制定和日常生活的核心。它被称为“组合预测”，“荟萃分析”，“结合”，“结合”和“投票的MLE方法”等。过去的研究通常认为嘈杂的投票是相同和独立分布的（i.i.d。），但是这个假设通常是不现实的。取而代之的是，我们假设投票是独立的，但不一定是相同分布的，并且我们的结合算法可以访问与每次投票中噪声的基础模型有关的某些辅助信息。在我们目前的工作中，我们：（1）定义我们的问题，并认为它反映了共同且与社会相关的现实世界情景，（2）提出了一个多臂强盗模型和基于计数的辅助信息集，（3）在我们的噪声模型下，在我们的噪声模型中，（4）提出订单，（4）序列（4），（4）在我们的噪声模型下，（3）得出最大的似然聚合规则，（4），（4）empiratient（4）将我们的规则与普通投票规则和天真的经验加权修改进行比较。我们发现，我们的规则成功地使用辅助信息来超越天真的基线。
How should one combine noisy information from diverse sources to make an inference about an objective ground truth? This frequently recurring, normative question lies at the core of statistics, machine learning, policy-making, and everyday life. It has been called "combining forecasts", "meta-analysis", "ensembling", and the "MLE approach to voting", among other names. Past studies typically assume that noisy votes are identically and independently distributed (i.i.d.), but this assumption is often unrealistic. Instead, we assume that votes are independent but not necessarily identically distributed and that our ensembling algorithm has access to certain auxiliary information related to the underlying model governing the noise in each vote. In our present work, we: (1) define our problem and argue that it reflects common and socially relevant real world scenarios, (2) propose a multi-arm bandit noise model and count-based auxiliary information set, (3) derive maximum likelihood aggregation rules for ranked and cardinal votes under our noise model, (4) propose, alternatively, to learn an aggregation rule using an order-invariant neural network, and (5) empirically compare our rules to common voting rules and naive experience-weighted modifications. We find that our rules successfully use auxiliary information to outperform the naive baselines.
