Abstract: The key to understanding processes of transition and innovation lies in understanding behavioural change of individuals. Here, we present FOUNTAIN, an agent-based approach to modelling transitions and innovations. Its basis is a cognitive model for the agents which relies on behavioural change theories. FOUNTAIN can be used to simulate scenarios in which individuals show habitual behaviour. We can analyse the effects of interventions aimed to influence the behaviour. These effects can not only be evaluated at macro level, but, as FOUNTAIN models heterogeneous individuals, it can also provide insights in the properties and motivation of agents that show specific behaviour. Therewith, target groups can be identified that are more likely to react to interventions. These insights are very valuable for policy makers to define new policy measures. The agents have a bounded-rational cognitive model. It contains a model for habitual behaviour: agents develop a routine behaviour which they unconsciously follow, unless they are triggered to make a deliberate choice. The choice mechanism includes a trade-off between rational, utility-based choices and affective choices. The agents are placed in a social network, which facilitates social influence via peer pressure. Also, agents influence each other via environmental aspects, such as resource limitations. Simulating an agent community results in a complex system, that shows the propagating effects of behavioural change. We have developed an extended FOUNTAIN model for commuter behaviour, where agents select their work location, modality for traveling and travel time on a daily basis. We have configured the environment for the city of Utrecht in the Netherlands, and validated the commuter model by comparing the simulation results against a real-world experiment, in which car drivers were paid to avoid rush hour. As follow-up, FOUNTAIN analyses were used by policy makers while planning new interventions on commuter behaviour.

Abstract: An interdependent network is a network consisting of different types of networks that interact with each other via interconnections between them. The design of the interconnection is one of the main challenges to achieve a robust interdependent network. Due to cost considerations, network providers are inclined to interconnect nodes that are geographically close. Accordingly, we propose two topologies, the random geographic graph and the relative neighbourhood graph, for the design of interconnection in interdependent networks that incorporates the geographic location of nodes. Different from one-to-one interconnections studied in most papers, the two topologies generalize to multiple-to-multiple interconnections, meaning that one node in one network can have an arbitrary number of dependent nodes in the other network. Moreover, the two topologies are applicable in the scenario that the sizes of the coupled networks are not equal. We derive the average number of interdependent links (interlink density) for the two topologies which enables us to compare simulations performed on these two topologies with the same interlink density. For the two proposed topologies, we evaluate the impact of the interconnection structure on the robustness of interdependent networks against cascading failures. Finally, we propose the decrease of the largest functioning component after cascading failures triggered by a small fraction of failed nodes, as a robustness metric. This robustness metric quantifies the damage of the network introduced by a small fraction of initial failures well before the critical fraction of failures that collapses the whole network.

Abstract: The use of renewable energy sources will lead to enhanced distributed energy generation and an accompanying modified power grid topology. It is a major issue how to keep the network synchronized under these circumstances. We study synchronization on cyclic power grids and determine the stability of the synchronous state. To this end we first calculate all stable equilibria and type-1 saddles (with one unstable direction) of the network using an efficient algorithm. Next both linear and nonlinear stability are investigated, for the case in which generators and consumers are periodically distributed as well for random distributions. We find that the stability of the synchronous state decreases with network size, and that the most regular network topology is most stable. Nonlinear stability is considered using direct methods and measured by potential energy of saddles. We determine the potential energy for the ring network and demonstrate that this topology always leads to improved stability of the synchronous state compared to tree networks. We interpret our stability results in terms of the probability that certain connections between nodes can break. We show that heterogeneity in the distribution of generators and consumers will lead to an increased fragility of the network.

Abstract: How different urban properties such as number of hospitals, shops, patents, and crimes depend on city size? It has been demonstrated that most urban properties Y follow the allometric scaling law: Y is proportional to N^b, where N and b are population size of a city and the scaling exponent. Urban infrastructure has been shown to scale sub-linearly (b<1) reflecting large cities don't need large infrastructure, whereas output and income have been shown to scale super-linearly (b>1) reflecting high per capita in large cities. Here we empirically analyze urban scaling observed in Japanese phone directory (Yellow Pages) data collected from Telepoint provided by Zenrin Co., Ltd. This data contains comprehensive individual listings of about 7 million shops or facilities (nearly all shops, firms, hospitals, schools, parks, etc). Name, address, latitude and longitude, phone number, and industrial sector of the shop or the facility are also included. We can count the number of stores or facilities in each city. The industrial sector is divided into 39 categories. Each category is further divided into 785 subcategories. This allows us to study and discuss systematically the scaling exponent that are associated with various aspects of urban agglomeration. We show that obtained scaling exponents help to characterize urban properties.