{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T06:10:36Z","timestamp":1784182236418,"version":"3.55.0"},"reference-count":23,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T00:00:00Z","timestamp":1784160000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Syst. Neurosci."],"abstract":"<jats:p>This paper gives a roadmap for filling in a new integrated worldview, from physics to emergent phenomena, presented at Werbos (2010). That worldview starts from the assumption that the cosmos we live in is a dynamical system, which obeys what I call Hard Core Einsteinian realism (HCER), which can be approximated very well in our level of life by quantum electrodynamics as formulated by Everett, Wheeler and Deutsch (EWD) (Deutsch, 1997). In principle all of the patterns which emerge in such a cosmos are fully, precisely specified by knowing the equilibrium probability distribution (equivalent to the entropy function) for that dynamical system. These functions are known precisely for a wide class of EWD and HCER theories. Thus the understanding of life, mind and other emergent phenomena in our cosmos is equivalent to knowing the parameters of the underlying dynamical law (e.g., H, for the case of EWD), and knowing useful ways to approximate what that probability function looks like. As an example, present theories of Darwinian evolution are basically just crude approximation systems for specific types pof state or pattern or object which we usually call \u201clife.\u201d<\/jats:p>","DOI":"10.3389\/fnsys.2026.1803434","type":"journal-article","created":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T05:40:07Z","timestamp":1784180407000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Mathematical foundations of life, mind and all emergent phenomena"],"prefix":"10.3389","volume":"20","author":[{"given":"Paul John","family":"Werbos","sequence":"first","affiliation":[{"name":"National Science Foundation","place":["Alexandria, VA, United States"]},{"name":"Policy and Planning Committees Q2 of Millennium Project, Institute of Electrical and Electronics Engineers-USA","place":["Washington, DC, United States"]},{"name":"Center for Large-Scale Complex Systems & Integrated Optimization Networks, The University of Memphis, Memphis","place":["Grenoble, TN, United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,7,16]]},"reference":[{"key":"ref1","first-page":"19","volume-title":"Arnold-Collected Works: Hydrodynamics, Bifurcation Theory, and Algebraic Geometry 1965\u20131972","author":"Arnold","year":"2013"},{"key":"ref2","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1023\/A:1022650905902","article-title":"Approximation and estimation bounds for artificial neural networks","volume":"14","author":"Barron","year":"1994","journal-title":"Mach. 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