论文标题
认知因素之间的共识很困难
Consensus between Epistemic Agents is Difficult
论文作者
论文摘要
我们在两个数据流之间介绍了一项认知信息措施，即我们称为$ tagrment $。与转移熵密切相关的措施必须通过采样可访问的数据流来估计具有有限记忆资源的认知药物。我们表明，即使在理想的条件下，使用略有不同采样策略的认知药物也可能无法在其哪些数据流影响哪些数据的结论中达成共识。作为例证，我们研究了一个现实世界的数据流，其中不同的抽样策略得出了矛盾的结论，解释了为什么由于纯粹的认识论原因，无论世界上的实际本体论，为什么可能存在一些政治上充满的话题。
We introduce an epistemic information measure between two data streams, that we term $influence$. Closely related to transfer entropy, the measure must be estimated by epistemic agents with finite memory resources via sampling accessible data streams. We show that even under ideal conditions, epistemic agents using slightly different sampling strategies might not achieve consensus in their conclusions about which data stream is influencing which. As an illustration, we examine a real world data stream where different sampling strategies result in contradictory conclusions, explaining why some politically charged topics might exist due to purely epistemic reasons irrespective of the actual ontology of the world.
