Urban mobility systems are composed multiple elements with strong interactions, i.e. their future is co-determined by the state of other elements. Thus, studying components in isolation, i.e. using a reductionist approach, is inappropriate. I propose five recommendations to improve urban mobility based on insights from the scientific study of complex systems: use adaptation over prediction, regulate interactions to avoid friction, use sensors to recover real time information, develop adaptive algorithms to exploit that information, and deploy agents to act on the urban environment.
Improving Urban Mobility by Understanding its Complexity
Paper/ abstract submission deadline: February 14th, 2016
The Fifteenth International Conference on the Synthesis and Simulation of Living Systems (ALife XV) will be held in Cancun, Mexico on July 4th-8th, 2016.
The Instituto de Investigaciones en Matemáticas Aplicadas y en Sistemas (IIMAS) of the Universidad Nacional Autónoma de México (UNAM) has an open call for a research professor position in bioinformatics. This position, aimed consists of renewable one-year contracts with the possibility of tenure after three years.
The aim of these positions is to create a research group in the new campus of UNAM in Mérida, Yucatán, part of the Science and Technology Park of Yucatán.
Application deadline: October 12, 2015.
More details (in Spanish) at this link.
The morphology of urban agglomeration is studied here in the context of information exchange between different spatio-temporal scales. Urban migration to and from cities is characterised as non-random and following non-random pathways. Cities are multidimensional non-linear phenomena, so understanding the relationships and connectivity between scales is important in determining how the interplay of local/regional urban policies may affect the distribution of urban settlements. In order to quantify these relationships, we follow an information theoretic approach using the concept of Transfer Entropy. Our analysis is based on a stochastic urban fractal model, which mimics urban growing settlements and migration waves. The results indicate how different policies could affect urban morphology in terms of the information generated across geographical scales.
Murcio R, Morphet R, Gershenson C, Batty M (2015) Urban Transfer Entropy across Scales. PLoS ONE 10(7): e0133780. doi:10.1371/journal.pone.0133780 http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0133780
To 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.
Measuring the complexity of adaptive peer-to-peer systems
Michele Amoretti, Carlos Gershenson
Peer-to-Peer Networking and Applications
The slower is faster (SIF) effect occurs when a system performs worse when its components try to be 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.
When slower is faster
Carlos Gershenson, Dirk Helbing
Update: paper was published in Complexity:
As a part of the consolidation of the National Laboratory of Complexity, the Center for Complexity Science of the National Autonomous University of Mexico is seeking outstanding candidates for five one year postdoctoral positions beginning in August, 2015. Research plans from all areas related to complex systems are encouraged.
Please send CV and research plan to cgg [at] unam.mx before June 10th.
//Please forward to whom may be interested.