概率推理与推理系统中的选言效应
Probability Reversal and the Disjunction Effect in Reasoning Systems
摘要：
基于数据的判断进入人工智能应用，但当提供看似不必要的额外数据时，它们会发生矛盾的逆转。这方面的例子是辛普森的逆转和分离效应，一旦数据以不同的方式呈现或聚合，对数据的信念就会发生变化。有时，可以使用Pearson卡方检验或Fisher精确检验等统计检验来评估差异的显着性，但这对于在不完整信息下运行的基于阈值的决策系统可能没有帮助。为了降低决策中使用算法的风险，我们考虑了信念建模的问题。我们认为，证据支持的信念是不是经典的统计变量，他们应该在一般情况下，被视为不相交或极性结果的叠加态。我们从信念作为量子矢量的角度分析了析取效应。
英文摘要：
Data based judgments go into artificial intelligence applications but they undergo paradoxical reversal when seemingly unnecessary additional data is provided. Examples of this are Simpson's reversal and the disjunction effect where the beliefs about the data change once it is presented or aggregated differently. Sometimes the significance of the difference can be evaluated using statistical tests such as Pearson's chi-squared or Fisher's exact test, but this may not be helpful in threshold-based decision systems that operate with incomplete information. To mitigate risks in the use of algorithms in decision-making, we consider the question of modeling of beliefs. We argue that evidence supports that beliefs are not classical statistical variables and they should, in the general case, be considered as superposition states of disjoint or polar outcomes. We analyze the disjunction effect from the perspective of the belief as a quantum vector.
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。
