客观社会选择：利用辅助信息改善投票结果
Objective Social Choice: Using Auxiliary Information to Improve Voting Outcomes
摘要：
人们应该如何结合来自不同来源的嘈杂信息来推断客观的地面真相？这个经常出现的规范性问题是统计、机器学习、政策制定和日常生活的核心。它被称为“组合预测”，“元分析”，“集成”和“MLE投票方法”等。过去的研究通常假设噪声投票是相同和独立分布的（i.i.d.），但这种假设往往是不切实际的。相反，我们假设投票是独立的，但不一定是相同分布的，并且我们的集成算法可以访问与控制每个投票中的噪声的基础模型相关的某些辅助信息。在我们目前的工作中，我们：（1）定义我们的问题，并认为它反映了常见的和社会相关的现实世界场景，（2）提出多臂强盗噪声模型和基于计数的辅助信息集，（3）在我们的噪声模型下导出用于排名和基数投票的最大似然聚合规则，（4）备选地，提出使用阶不变神经网络来学习聚合规则，和（5）经验比较我们的规则，共同的投票规则和天真的经验加权修改。我们发现，我们的规则成功地使用辅助信息，以超越天真的基线。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
