Consensus between Epistemic Agents is Difficult

TL;DR

This paper introduces an influence measure between data streams for epistemic agents, revealing that differences in sampling strategies can prevent consensus, with implications for understanding disagreements on complex topics.

Contribution

It defines a new influence measure related to transfer entropy and demonstrates how sampling strategies affect consensus among epistemic agents.

Findings

Different sampling strategies can lead to contradictory conclusions.

Epistemic disagreements can occur independently of the actual data ontology.

The influence measure helps explain epistemic conflicts in real-world data analysis.

Abstract

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.