April 01 2011 Applications of Automata Theory and Algebra via the Mathematical Theory of Complexity to Biology, Physics, Psychology, Philosophy, and Games. John Rhodes. Chrystopher L. Nehaniv (Ed.). Foreword by Morris W. Hirsch. (2009, World Scientific Books.) ISBN: 978-981-283-696-0, US$65 (hardcover); ISBN: 978-981-283-697-7, US$39 (paperback). In Special Collection: CogNet Attila Egri-Nagy Attila Egri-Nagy Eszterházy Károly College Search for other works by this author on: This Site Google Scholar Author and Article Information Attila Egri-Nagy Eszterházy Károly College *Department of Computing Science, Institute of Mathematics and Informatics, Eszterházy Károly College, Eger, Leányka u. 4. H-3300, Hungary. E-mail: [email protected] Online ISSN: 1530-9185 Print ISSN: 1064-5462 © 2011 Massachusetts Institute of Technology2011MIT Press Artificial Life (2011) 17 (2): 141–143. https://doi.org/10.1162/artl_r_00025 Cite Icon Cite Permissions Share Icon Share Facebook Twitter LinkedIn Email Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Search Site Citation Attila Egri-Nagy; Applications of Automata Theory and Algebra via the Mathematical Theory of Complexity to Biology, Physics, Psychology, Philosophy, and Games. John Rhodes. Chrystopher L. Nehaniv (Ed.). Foreword by Morris W. Hirsch. (2009, World Scientific Books.) ISBN: 978-981-283-696-0, US$65 (hardcover); ISBN: 978-981-283-697-7, US$39 (paperback).. Artif Life 2011; 17 (2): 141–143. doi: https://doi.org/10.1162/artl_r_00025 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAll JournalsArtificial Life Search Advanced Search This content is only available as a PDF. © 2011 Massachusetts Institute of Technology2011MIT Press Article PDF first page preview Close Modal You do not currently have access to this content.

Beyond complexity measures, sometimes it is worthwhile in addition to investigate how complexity changes structurally, especially in artificial systems where we have complete knowledge about the evolutionary process. Hierarchical decomposition is a useful way of assessing structural complexity changes of organisms modeled as automata, and we show how recently developed computational tools can be used for this purpose, by computing holonomy decompositions and holonomy complexity. To gain insight into the evolution of complexity, we investigate the smoothness of the landscape structure of complexity under minimal transitions. As a proof of concept, we illustrate how the hierarchical complexity analysis reveals symmetries and irreversible structure in biological networks by applying the methods to the lac operon mechanism in the genetic regulatory network of Escherichia coli.