01617nas a2200253   4500008004100000022001400041245006100055210006000116300001000176490000600186520088300192653001801075653001501093653002401108653001501132100002001147700003001167700002301197700002601220700002201246700002701268700002901295856003901324       2020                        eng d  a2167-835900aEcosystem antifragility: beyond integrity and resilience0 aEcosystem antifragility beyond integrity and resilience  ae85330 v83 aWe review the concept of ecosystem resilience in its relation to ecosystem integrity from an information theory approach. We summarize the literature on the subject identifying three main narratives: ecosystem properties that enable them to be more resilient; ecosystem response to perturbations; and complexity. We also include original ideas with theoretical and quantitative developments with application examples. The main contribution is a new way to rethink resilience, that is mathematically formal and easy to evaluate heuristically in real-world applications: ecosystem antifragility. An ecosystem is antifragile if it benefits from environmental variability. Antifragility therefore goes beyond robustness or resilience because while resilient/robust systems are merely perturbation-resistant, antifragile structures not only withstand stress but also benefit from it.10aAntifragility10aComplexity10aEcosystem integrity10aResilience1 aEquihua, Miguel1 aAldama, Mariana, Espinosa1 aGershenson, Carlos1 aLópez-Corona, Oliver1 aMunguía, Mariana1 aPérez-Maqueo, Octavio1 aRamírez-Carrillo, Elvia  uhttps://doi.org/10.7717/peerj.853301685nas a2200181   4500008004100000020001400041245006200055210006000117300000900177520113900186653003301325653001501358653001401373653002301387100002201410700002301432856004801455       2015                        eng d  a1936-644200aMeasuring the complexity of adaptive peer-to-peer systems0 aMeasuring the complexity of adaptive peertopeer systems  a1-163 aTo improve the efficiency of peer-to-peer (P2P) systems while adapting to changing environmental conditions, static peer-to-peer protocols can be replaced by adaptive plans. The resulting systems are inherently complex, which makes their development and characterization a challenge for traditional methods. Here we propose the design and analysis of adaptive P2P systems using measures of complexity, emergence, self-organization, and homeostasis based on information theory. These measures allow the evaluation of adaptive P2P systems and thus can be used to guide their design. We evaluate the proposal with a P2P computing system provided with adaptation mechanisms. We show the evolution of the system with static and also changing workload, using different fitness functions. When the adaptive plan forces the system to converge to a predefined performance level, the nodes may result in highly unstable configurations, which correspond to a high variance in time of the measured complexity. Conversely, if the adaptive plan is less ``aggressive'', the system may be more stable, but the optimal performance may not be achieved.10aAdaptive peer-to-peer system10aComplexity10aEvolution10aInformation theory1 aAmoretti, Michele1 aGershenson, Carlos  uhttp://dx.doi.org/10.1007/s12083-015-0385-401115nas a2200193   4500008004100000022001400041245002600055210002600081300001100107490000700118520063900125653002200764653002200786653001400808653002200822100002300844700001800867856003600885       2015                        eng d  a1099-052600aWhen slower is faster0 aWhen slower is faster  a9–150 v213 aThe slower is faster (SIF) effect occurs when a system performs worse as its components try to do better. Thus, a moderate individual efficiency actually leads to a better systemic performance. The SIF effect takes place in a variety of phenomena. We review studies and examples of the SIF effect in pedestrian dynamics, vehicle traffic, traffic light control, logistics, public transport, social dynamics, ecological systems, and adaptation. Drawing on these examples, we generalize common features of the SIF effect and suggest possible future lines of research. {\copyright} 2015 Wiley Periodicals, Inc. Complexity 21: 9–15, 201510acascading effects10acollective motion10aEvolution10aphase transitions1 aGershenson, Carlos1 aHelbing, Dirk  uhttp://arxiv.org/abs/1506.0679601124nas a2200157   4500008004100000245002800041210002800069490001300097520073900110653001100849653002200860653001200882653001400894100002300908856003500931       2013                        eng d00aLiving in Living Cities0 aLiving in Living Cities0 vIn Press3 a<p>This paper presents and overview of current and potential applications of living technology to urban problems. Living technology can be described as technology that exhibits the core features of living systems. These features can be useful to solve dynamic problems. In particular, urban problems concerning mobility, logistics, telecommunications, governance, safety, sustainability, and society and culture are presented, while solutions involving living technology are reviewed. A methodology for developing living technology is mentioned, while self-organizing traffic lights are used as a case study of the benefits of urban living technology. Finally, the usefulness of describing cities as living systems is discussed.</p>
10acities10aSelf-organization10atraffic10atransport1 aGershenson, Carlos  uhttp://arxiv.org/abs/1111.365902024nam a2200157   4500008004100000020002200041245005000063210004900113260002600162520156600188653002201754653001201776653002201788100002301810856003301833       2007                        eng d  a978-0-9831172-3-000aDesign and Control of Self-organizing Systems0 aDesign and Control of Selforganizing Systems  aMexicobCopIt Arxives3 aComplex systems are usually difficult to design and control. There are several particular methods for coping with complexity, but there is no general approach to build complex systems. In this book I pro- pose a methodology to aid engineers in the design and control of com- plex systems. This is based on the description of systems as self- organizing. Starting from the agent metaphor, the methodology pro- poses a conceptual framework and a series of steps to follow to find proper mechanisms that will promote elements to find solutions by ac- tively interacting among themselves. The main premise of the method- ology claims that reducing the "friction" of interactions between el- ements of a system will result in a higher "satisfaction" of the system, i.e. better performance. A general introduction to complex thinking is given, since designing self-organizing systems requires a non-classical thought, while prac- tical notions of complexity and self-organization are put forward. To illustrate the methodology, I present three case studies. Self-organizing traffic light controllers are proposed and studied with multi-agent simulations, outperforming traditional methods. Methods for im- proving communication within self-organizing bureaucracies are ad- vanced, introducing a simple computational model to illustrate the benefits of self-organization. In the last case study, requirements for self-organizing artifacts in an ambient intelligence scenario are dis- cussed. Philosophical implications of the conceptual framework are also put forward.10aComplexity Theory10aPhysics10aSelf-organization1 aGershenson, Carlos  uhttp://tinyurl.com/DCSOS2007