The interaction of all mobile species with their environment hinges on their movement patterns: the places they visit and how frequently they go there. In human society, where the prevalent form of cohabitation is in cities, the highly dynamic and diverse movement of people is fundamental to almost every aspect of socio-economic life, including social interactions or disease spreading, and ultimately is key to the evolution of urban infrastructure, productivity, innovation and technology. However, despite the crucial role of the spatio-temporal structure of movement in cities, the laws that govern the variation of population flows to specific locations have remained elusive. Here we show that behind the apparent complexity of movement a surprisingly simple universal scaling relation drives the flow of individuals to any specific location based on both frequency of visitation and distance travelled. We derive a first principles argument stating that the number of visiting individuals should decrease as an inverse square of the product of visitation frequency and travel distance; or, equivalently, as a power law with exponent $\approx \! -$2. Using large-scale data analyses, we demonstrate that population flows obey this theoretical prediction in virtually all tested areas across the globe, ranging from Europe and America to Asia and Africa, regardless of the detailed geographies, cultures or levels of development. The revealed regularity offers unprecedented possibilities for the modelling of mobility fluxes at high spatial and temporal resolution, and it places an important constraint on any theory of movement, spatial organisation and social interaction in cities.
Increasing complexity in manufacturing domain requires radically new approaches for planning, design and coordination in manufacturing processes. In this paper we introduce an organic system architecture for supporting complex manufacturing processes. The architecture brings different approaches, legacy systems and services to work together in an interoperable manner for real time decision support, detect early warning and to accelerate learning. The architecture design is supported by ground breaking studies in service oriented systems, multi-agent systems, ecosystem based approaches and devolved ontologies. The architecture provides foundation for a new generation of service oriented enterprise information platform that can address the increasingly complex needs of real industrial environments.
Can a world cup soccer hybrid simulation, based on self - organization, evolutionary learning, and complex adaptive systems, hold the key to unlocking social complexity dynamics? Hybrid modeling, characterized by discrete and continuous dynamics with interconnected structure for complexity decomposition in engineering and applied mathematics, has proven effective in areas like complex industrial processes and automatic controlled systems. However, given the challenges of modeling multi- scale social phenomena, hybrid system representations also have the unique potential to capture complex dynamics within interconnected global social 1589 systems.
In this paper, we investigate the effect of mobility constraints on epidemic broad-cast mechanisms in DTNs (Delay-Tolerant Networks). Major factors affecting epidemic broadcast performances are its forwarding algorithm and node mobility. The impact of forwarding algorithm and node mobility on epidemic broadcast mechanisms has been actively studied in the literature, but those studies use generally unconstrained mobility models. The objective of this paper is therefore to quantitatively investigate the effect of mobility constraints on epidemic broadcast mechanisms. We evaluate the performances of P-BCAST (PUSH-based BroadCast), SA-BCAST (Self-Adaptive BroadCast), and HP-BCAST (History-based P-BCAST) with a random waypoint mobility model with mobility constraints.
This paper presents a time-series model of the United States Airport Network as a directed, weighted network, with the weight representing the total number of passengers flying from an origin to a destination airport, in a two month time period. Six independent networks are built for a given year, in order to capture the seasonal variation of passengers. To explore the evolution of the network over the past two decades, three specific years are investigated: 1990, 2000, and 2010. The results highlight the growth of the network in terms of airports and connections, and suggest a scale-free, small-world topology. In addition, the ranked passenger distribution appears to follow a logarithmic trend, implying high heterogeneity in passengers on different connections.
: Diagnosis and, when possible, prognosis of faults are essential for safe and reliable operation. The area of fault diagnosis has emerged over three decades. The majority of studies related to linear systems but real-life systems are complex and nonlinear. The development of methodologies coping with complex and nonlinear systems have matured and even though there are many un-solved problems, methodology and associated tools have become available in the form of theory and software for design. Genuine industrial cases have also become available. Analysis of system topology, referred to as structural analysis, has proven to be unique and simple in use and a recent extension to active structural techniques have made fault isolation possible in a wide range of systems. Following residual generation using these topology-based methods, deterministic and statistical change detection has proven very useful for on-line prognosis and diagnosis. For complex systems, results from non-Gaussian detection theory have been employed with convincing results. The paper presents the theoretical foundation for design methodologies that now appear as enabling technology for a new area of design of systems that are reliable in practise. Yet they are also affordable due to the use of fault-tolerant philosophies and tools that make engineering efforts minimal for their implementation. The paper includes examples for an autonomous aircraft and a baling system for agriculture.
Agent-based modelling and simulation is now beginning to establish itself as a suitable technique for studying biological systems. However, a major issue in using agent-based simulations to study complex systems such as those in Systems Biology is the fact that simulations are ‘opaque’. While we have knowledge of individuals’ behaviour through agent rules and have techniques for evaluating global behaviour by aggregating the states of individuals, methods for identifying the interactive mechanisms giving rise to this global behaviour are lacking. Formulating precise hypotheses about these multi-level behaviours is also difficult without an established formalism for describing them. The complex event formalism allows relationships between agent-rule-generated events to be defined so that behaviours at different levels of abstraction to be described. Complex event types define categories of these behaviours, which can then be detected in simulation, giving us computational method for distinguishing between alternative interactive mechanisms underlying a higher level behaviour. We apply the complex event formalism to an agent-based model of cell populations in the colonic crypt and demonstrate how competition and selection events can be identified in simulation at both the individual and clonal level, allowing us to computationally test hypotheses about the interactive mechanisms underlying a clone’s success.
This paper presents a graphical enterprises modelling method oriented to process simulation. Among static, decisional and dynamical contexts of modelling, only the dynamical context is treated in this article. This formalism was inspired by well-known methods like SADT and UML. It is a part of a larger analysis and optimisation environment that is also in development in our research centre (the CREGI). The goal of this environment is to simplify the use of simulation and optimisation methods in industrial contexts.
One impact of the introduction of television, accor ding to widely held views, is an undermining of tra ditional values and social organization. In this study, we s imulated this process by representing social commun ication as a Random Boolean Network in which the individuals are nodes, and each node’s state represents an opinion (yes / no) about some issue. Television is modelled as hav ing a direct link to every node in the network. Two scenarios were considered. First, we found that, except in th e most cohesive networks, television rapidly breaks down cohesion (agreement in opinion). Second, the introd uction of Hebbian learning leads to a polarizing ef fect: one subgroup strongly retains the original opinion, whi le a splinter group adopts the contrary opinion. Th e system displays criticality with respect to connectivity a nd the level of exposure to television. More genera lly, the results suggest that patterns of communication in networks can help to explain a wide variety of social phenom ena.
We consider an evolving network of interacting species which exhibits self-organized criticality. The system is characterized by repeated crashes in which a large number of species are extinct and subsequent recoveries. We investigate the macroscopic properties of this system prior to such crashes, concentrating on the variance of the relative population sizes of species and its evolution over time. A simple score function is constructed to determine the probability of a crash within a certain time interval to be used as a predictor for crashes.
One of the most essential temporal structures in life systems is a cycle, which can be observed in any hierarchy of living things, such as TCA cycle in cytoplasmic lever, the cell cycle in cell division, and the life cycle of living things. The importance of this structure has been pointed out by several authors, such as Eigen's hypercycle, Maturana's autopoiesis, Kauffman's NK network, and Fontana's Algorithmic Chemistry. However, these researches do not systematically address the conditions under which such cycle structures will emerge and become stable. In this paper, abstract rewriting system on a multiset is introduced to a model chemical reaction as a symbolic rewriting system acting on a multiset, which can be viewed as a chemical reaction system in a test tube. By use of this model, we made experiments on Brusselator model (the Brusselator is a model of chemical oscillations which are found on the Belousov-Zabotinsky reaction). And we confirm that non-linear oscillation emerges.
The definition of macroscopic observables with a microscopic foundation for complex systems is one of the approaches taken to understand such systems and predict their future behaviour. In the biological evolution, the notion of information and entropy has been proposed as a candidate for such a quantity. We will argue that its definition should be hierarchical, that is it will depend on the level of abstraction at which the system is observed. We will propose several definitions of information for different levels of evolution and support our claim with simulations from evolutionary algorithms and structure optimization, where we will concentrate on neural systems.
