
TL;DR
This paper explores how macro-level causal models can sometimes be more informative than detailed micro-level models by leveraging information theory concepts like channel capacity, revealing limits of detailed modeling.
Contribution
It introduces a framework linking causal emergence to Shannon's channel capacity, explaining why macro models can outperform detailed models in capturing causal structure.
Findings
Macroscale models can utilize full causal capacity.
Causal emergence explained via information theory principles.
Model choice affects the utilization of a system's causal capacity.
Abstract
The causal structure of any system can be analyzed at a multitude of spatial and temporal scales. It has long been thought that while higher scale (macro) descriptions of causal structure may be useful to observers, they are at best a compressed description and at worse leave out critical information. However, recent research applying information theory to causal analysis has shown that the causal structure of some systems can actually come into focus (be more informative) at a macroscale (Hoel et al. 2013). That is, a macro model of a system (a map) can be more informative than a fully detailed model of the system (the territory). This has been called causal emergence. While causal emergence may at first glance seem counterintuitive, this paper grounds the phenomenon in a classic concept from information theory: Shannon's discovery of the channel capacity. I argue that systems have a…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
