Table of Contents
Entropy, Volume 19, Issue 5 (May 2017)
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Cover Story Any system can be described at a multitude of spatial and temporal scales. One long-standing [...] Read more. Any system can be described at a multitude of spatial and temporal scales. One long-standing reductionist assumption is that the lowest possible microscale should contain the maximum information about the causal structure of any system. While some have argued that higher-level macroscales might be useful because of computational constraints, it has been widely accepted that they are like maps; i.e., at best macroscales are lossless compressions of the underlying territory. Assessing causal structures using the information theory leads to a different conclusion: macroscales can act like error-correcting codes for causal relationships. From this error-correcting ability, macroscales can have more causal influence and contain greater information than their underlying microscales. View this paper