{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,17]],"date-time":"2026-10-17T11:45:21Z","timestamp":1792237521544,"version":"4.4.0"},"reference-count":37,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2017,4,26]],"date-time":"2017-04-26T00:00:00Z","timestamp":1493164800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>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 may be useful to observers, they are at best a compressed description and at worse leave out critical information and causal relationships. However, recent research applying information theory to causal analysis has shown that the causal structure of some systems can actually come into focus and be more informative at a macroscale. That is, a macroscale description of a system (a map) can be more informative than a fully detailed microscale description of the system (the territory). This has been called \u201ccausal emergence.\u201d While causal emergence may at first seem counterintuitive, this paper grounds the phenomenon in a classic concept from information theory: Shannon\u2019s discovery of the channel capacity. I argue that systems have a particular causal capacity, and that different descriptions of those systems take advantage of that capacity to various degrees. For some systems, only macroscale descriptions use the full causal capacity. These macroscales can either be coarse-grains, or may leave variables and states out of the model (exogenous, or \u201cblack boxed\u201d) in various ways, which can improve the efficacy and informativeness via the same mathematical principles of how error-correcting codes take advantage of an information channel\u2019s capacity. The causal capacity of a system can approach the channel capacity as more and different kinds of macroscales are considered. Ultimately, this provides a general framework for understanding how the causal structure of some systems cannot be fully captured by even the most detailed microscale description.<\/jats:p>","DOI":"10.3390\/e19050188","type":"journal-article","created":{"date-parts":[[2017,4,26]],"date-time":"2017-04-26T13:42:06Z","timestamp":1493214126000},"page":"188","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":100,"title":["When the Map Is Better Than the Territory"],"prefix":"10.3390","volume":"19","author":[{"given":"Erik","family":"Hoel","sequence":"first","affiliation":[{"name":"Department of Biological Sciences, Columbia University, New York, NY 10027, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2017,4,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1007\/BF00485230","article-title":"Special sciences (or: The disunity of science as a working hypothesis)","volume":"28","author":"Fodor","year":"1974","journal-title":"Synthese"},{"key":"ref_2","unstructured":"Kim, J. 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