The intrinsic cybernetics of large complex systems and how droughts turn into floods.

The cyber-physical nature of engineering systems requires the smooth integration of decision making across soft and hard infrastructure. This need is common to any systems where decision making considers multiple complex systems such as the climate, the natural and built environment, and the dynamics of large organisations. As an example, in the Anthropocene, acute droughts and floods cannot only be imputed to more extreme variations of the climate patterns, but also to the alteration of the habitable environment and of the resources that support it, hence to their governance and management. In this discussion paper we present arguments about the extent to which the natural environment is modified to support urbanisation. We expose the cyber-physical nature of large infrastructure systems taking as an example the events of the 2011 Brisbane flood and the operations of the damming system of the river Brisbane. Using literature resources and data, we show how flood defence devices had to provide for a population of almost 2 million people, while being engineered when the population was less than one million, with increase in water withdrawal mainly due to residential utilities. We show how the cyber-physical aspects of the problem materialised in moth-long delays in the governance and management structure and made the flood event transcend the boundary of a purely climatic or engineering incident. Looking beyond the Brisbane example, our conclusions point at overcoming the discontinuity between operation, management and political layers when operating on cyber-physical systems such as freshwater networks.

On the use of random graphs in analysing resource utilization in urban systems.

Urban resource models increasingly rely on implicit network formulations. Resource consumption behaviours documented in the existing empirical studies are ultimately by-products of the network abstractions underlying these models. Here, we present an analytical formulation and examination of a generic demand-driven network model that accounts for the effectiveness of resource utilization and its implications for policy levers in addressing resource management in cities. We establish simple limiting boundaries to systems' resource effectiveness. These limits are found not to be a function of system size and to be simply determined by the system's average ability to maintain resource quality through its transformation processes. We also show that resource utilization in itself does not enjoy considerable size efficiencies with larger and more diverse systems only offering increased chances of finding matching demand and supply between existing sectors in the system.

Network Communities of Dynamical Influence.

Fuelled by a desire for greater connectivity, networked systems now pervade our society at an unprecedented level that will affect it in ways we do not yet understand. In contrast, nature has already developed efficient networks that can instigate rapid response and consensus when key elements are stimulated. We present a technique for identifying these key elements by investigating the relationships between a system's most dominant eigenvectors. This approach reveals the most effective vertices for leading a network to rapid consensus when stimulated, as well as the communities that form under their dynamical influence. In applying this technique, the effectiveness of starling flocks was found to be due, in part, to the low outdegree of every bird, where increasing the number of outgoing connections can produce a less responsive flock. A larger outdegree also affects the location of the birds with the most influence, where these influentially connected birds become more centrally located and in a poorer position to observe a predator and, hence, instigate an evasion manoeuvre. Finally, the technique was found to be effective in large voxel-wise brain connectomes where subjects can be identified from their influential communities.

Using Network Dynamical Influence to Drive Consensus.

Consensus and decision-making are often analysed in the context of networks, with many studies focusing attention on ranking the nodes of a network depending on their relative importance to information routing. Dynamical influence ranks the nodes with respect to their ability to influence the evolution of the associated network dynamical system. In this study it is shown that dynamical influence not only ranks the nodes, but also provides a naturally optimised distribution of effort to steer a network from one state to another. An example is provided where the "steering" refers to the physical change in velocity of self-propelled agents interacting through a network. Distinct from other works on this subject, this study looks at directed and hence more general graphs. The findings are presented with a theoretical angle, without targeting particular applications or networked systems; however, the framework and results offer parallels with biological flocks and swarms and opportunities for design of technological networks.

Characteristics of swarms on the edge of fragmentation.

Fragmentation of particle swarms into isolated subgroups occurs when interaction forces are weak or restricted. In the restricted case, the swarm experiences the onset of bottlenecks in the graph of interactions that can lead to the fragmentation of the system into subgroups. This work investigates the characteristics of such bottlenecks when the number of particles in the swarm increases. It is shown that certain characteristics of the bottleneck can be captured by considering only the number of particles in the swarm. Considering the case of a connected communication graph constructed in the hypothesis that each particle is influenced by a fixed number of neighboring particles, a limit case is determined for which a lower limit to the Cheeger constant can be derived analytically without the need for extensive algebraic calculations. Results show that as the number of particles increases, the Cheeger constant decreases. Although ensuring a minimum number of interactions per particle is sufficient, in theory, to ensure cohesion, the swarm may face fragmentation as more particles are added to the swarm.