{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,30]],"date-time":"2026-04-30T04:06:49Z","timestamp":1777522009836,"version":"3.51.4"},"reference-count":87,"publisher":"SAGE Publications","issue":"4","license":[{"start":{"date-parts":[[2011,8,1]],"date-time":"2011-08-01T00:00:00Z","timestamp":1312156800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Adaptive Behavior"],"published-print":{"date-parts":[[2011,8]]},"abstract":"<jats:p>The natural energy minimization behavior of a dynamical system can be interpreted as a simple optimization process, finding a locally optimal resolution of problem constraints. In human problem solving, high-dimensional problems are often made much easier by inferring a low-dimensional model of the system in which search is more effective. But this is an approach that seems to require top-down domain knowledge\u2014not one amenable to the spontaneous energy minimization behavior of a natural dynamical system. However, in this article we investigate the ability of distributed dynamical systems to improve their constraint resolution ability over time by self-organization. We use a \u2018\u2018self-modeling\u2019\u2019 Hopfield network with a novel type of associative connection to illustrate how slowly changing relationships between system components can result in a transformation into a new system which is a low-dimensional caricature of the original system. The energy minimization behavior of this new system is significantly more effective at globally resolving the original system constraints. This model uses only very simple, and fully distributed, positive feedback mechanisms that are relevant to other \u2018\u2018active linking\u2019\u2019 and adaptive networks. We discuss how this neural network model helps us to understand transformations and emergent collective behavior in various non-neural adaptive networks such as social, genetic and ecological networks.<\/jats:p>","DOI":"10.1177\/1059712311412797","type":"journal-article","created":{"date-parts":[[2011,8,2]],"date-time":"2011-08-02T20:56:33Z","timestamp":1312318593000},"page":"227-249","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":21,"title":["Transformations in the scale of behavior and the global optimization of constraints in adaptive networks"],"prefix":"10.1177","volume":"19","author":[{"given":"Richard A","family":"Watson","sequence":"first","affiliation":[{"name":"Natural Systems Group, School of Electronics and Computer Science, University of Southampton, UK,"}]},{"given":"Rob","family":"Mills","sequence":"additional","affiliation":[{"name":"Natural Systems Group, School of Electronics and Computer Science, University of Southampton, UK"}]},{"given":"C.L.","family":"Buckley","sequence":"additional","affiliation":[{"name":"School of Informatics, Sussex University, UK"}]}],"member":"179","published-online":{"date-parts":[[2011,8,2]]},"reference":[{"key":"atypb1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-81708-3"},{"key":"atypb2","doi-asserted-by":"publisher","DOI":"10.1207\/s15516709cog0901_7"},{"key":"atypb3","volume-title":"Proceedings of the 14th Annual Conference of the Cognitive Science Society","author":"Angeline, P.J."},{"key":"atypb4","volume-title":"Proceedings of the First European Conference on Artificial Life","author":"Bourgine, P."},{"key":"atypb5","volume-title":"Control of large distributed systems using games with pure strategy Nash equilibria. 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