01685nas 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/1506.06796