将信念更新框架拓展至可同时处理经典与量子概率的条件化推理。

Conditioning and AGM-like belief change in the Desirability-Indifference framework

• 基于偏好与拒绝的抽象概念构建条件化机制
• 统一处理经典与量子概率下的信念更新
• 适合逻辑、哲学及量子认知方向研究者

我们展示了如何将经典的AGM信念更新框架（扩张、修正、收缩）扩展至在所谓效用-无差异框架中处理条件化问题，该框架基于接受与拒绝选项的抽象概念，以及事件的抽象定义。这种高度抽象的设定使我们能够同时处理经典概率与量子概率理论中的信念变化问题。

原文摘要 · Abstract (English)

We show how the AGM framework for belief change (expansion, revision, contraction) can be extended to deal with conditioning in the so-called Desirability-Indifference framework, based on abstract notions of accepting and rejecting options, as well as on abstract notions of events. This level of abstraction allows us to deal simultaneously with classical and quantum probability theory.

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