Many physical systems can be coherently described in terms of their function and causal structure at multiple different levels. How we can reconcile these seemingly disparate levels of description? This is especially problematic because the lower scales at first appear more fundamental in three ways: in terms of their causal work, in terms of the amount of information they contain, and their theoretical superiority in terms of model choice. However, recent research bringing information theory and causal analysis to bear on modeling systems at different scales significantly reframes the issue, revealing that higher scales can be "causal codes" that allow for the generation of additional information through error correction. This result has significant implications for causal model choice in science and engineering. The findings indicate how emergence and reduction can be identified, measured, and used to design optimally informative experiments.
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