论文标题
根据估计权重进行加权多数投票的稳定性
Stability of Weighted Majority Voting under Estimated Weights
论文作者
论文摘要
考虑到来源提供准确的信息（可信度）的可能性，加权多数投票（WMV）是集体决策制定的众所周知的最佳决策规则。但是，实际上，可信赖性并不是决策者的已知数量 - 他们必须依靠称为信任的估计。计算信任的（机器学习）算法在具有系统地高估或低估可信赖性的属性时称为无偏见。为了正式分析决策过程的不确定性，我们介绍和分析了这种公正信任值的两个重要特性：正确性和最佳稳定性的稳定性。正确性的稳定性意味着决策者认为自己所获得的决策准确性等于实际准确性。我们证明了正确性的稳定性。最优性的稳定性意味着基于信任做出的决定与基于可信赖的情况一样好。最优性的稳定性不达到。我们分析两者之间的差异及其界限。我们还概述了对信任和可信度变化的敏感决策正确性的敏感性。
Weighted Majority Voting (WMV) is a well-known optimal decision rule for collective decision making, given the probability of sources to provide accurate information (trustworthiness). However, in reality, the trustworthiness is not a known quantity to the decision maker - they have to rely on an estimate called trust. A (machine learning) algorithm that computes trust is called unbiased when it has the property that it does not systematically overestimate or underestimate the trustworthiness. To formally analyse the uncertainty to the decision process, we introduce and analyse two important properties of such unbiased trust values: stability of correctness and stability of optimality. Stability of correctness means that the decision accuracy that the decision maker believes they achieved is equal to the actual accuracy. We prove stability of correctness holds. Stability of optimality means that the decisions made based on trust, are equally good as they would have been if they were based on trustworthiness. Stability of optimality does not hold. We analyse the difference between the two, and bounds thereon. We also present an overview of how sensitive decision correctness is to changes in trust and trustworthiness.
