01038nas a2200133   4500008004100000022001400041245006000055210005800115300000700173490000600180520063000186100002300816856006500839       2020                        eng d  a2296-914400aGuiding the Self-Organization of Cyber-Physical Systems0 aGuiding the SelfOrganization of CyberPhysical Systems  a410 v73 aSelf-organization offers a promising approach for designing adaptive systems. Given the inherent complexity of most cyber-physical systems, adaptivity is desired, as predictability is limited. Here I summarize different concepts and approaches that can facilitate self-organization in cyber-physical systems, and thus be exploited for design. Then I mention real-world examples of systems where self-organization has managed to provide solutions that outperform classical approaches, in particular related to urban mobility. Finally, I identify when a centralized, distributed, or self-organizing control is more appropriate.1 aGershenson, Carlos  uhttps://www.frontiersin.org/article/10.3389/frobt.2020.0004102428nas a2200229   4500008004100000022001400041245007400055210006900129300000700198490000600205520173500211100002301946700002101969700001801990700001902008700002302027700002002050700002302070700002902093700002202122856005402144       2016                        eng d  a2193-112700aGeneric temporal features of performance rankings in sports and games0 aGeneric temporal features of performance rankings in sports and   a330 v53 aMany complex phenomena, from trait selection in biological systems to hierarchy formation in social and economic entities, show signs of competition and heterogeneous performance in the temporal evolution of their components, which may eventually lead to stratified structures such as the worldwide wealth distribution. However, it is still unclear whether the road to hierarchical complexity is determined by the particularities of each phenomena, or if there are generic mechanisms of stratification common to many systems. Human sports and games, with their (varied but simple) rules of competition and measures of performance, serve as an ideal test-bed to look for universal features of hierarchy formation. With this goal in mind, we analyse here the behaviour of performance rankings over time of players and teams for several sports and games, and find statistical regularities in the dynamics of ranks. Specifically the rank diversity, a measure of the number of elements occupying a given rank over a length of time, has the same functional form in sports and games as in languages, another system where competition is determined by the use or disuse of grammatical structures. We use a Gaussian random walk model to reproduce the rank diversity of the studied sports and games. We also discuss the relation between rank diversity and the cumulative rank distribution. Our results support the notion that hierarchical phenomena may be driven by the same underlying mechanisms of rank formation, regardless of the nature of their components. Moreover, such regularities can in principle be used to predict lifetimes of rank occupancy, thus increasing our ability to forecast stratification in the presence of competition.1 aMorales, José, A.1 aSánchez, Sergio1 aFlores, Jorge1 aPineda, Carlos1 aGershenson, Carlos1 aCocho, Germinal1 aZizumbo, Jerónimo1 aRodríguez, Rosalío, F.1 aIñiguez, Gerardo  uhttp://dx.doi.org/10.1140/epjds/s13688-016-0096-y01203nas a2200133   4500008004100000245006100041210006000102260001400162300001200176490000800188520081500196100002301011856003501034       2012                        eng d00aGuiding the Self-organization of Random Boolean Networks0 aGuiding the Selforganization of Random Boolean Networks  cSeptember  a181-1910 v1313 aRandom Boolean networks (RBNs) are models of genetic regulatory networks. It is useful to describe RBNs as self-organizing systems to study how changes in the nodes and connections affect the global network dynamics. This article reviews eight different methods for guiding the self-organization of RBNs. In particular, the article is focussed on guiding RBNs towards the critical dynamical regime, which is near the phase transition between the ordered and dynamical phases. The properties and advantages of the critical regime for life, computation, adaptability, evolvability, and robustness are reviewed. The guidance methods of RBNs can be used for engineering systems with the features of the critical regime, as well as for studying how natural selection evolved living systems, which are also critical.1 aGershenson, Carlos  uhttp://arxiv.org/abs/1005.573300340nas a2200097   4500008004100000245006400041210006100105260000900166100002300175856004400198       2006                        eng d00aA General Methodology for Designing Self-Organizing Systems0 aGeneral Methodology for Designing SelfOrganizing Systems  bECCO1 aGershenson, Carlos  uhttp://uk.arxiv.org/abs/nlin.AO/0505009