City rats: From rat behaviour to human spatial cognition in urban environments

The structure and shape of an urban environment influence our ability to find our way about in the city^1-2^. Indeed, urban designers who face the challenge of planning environments that facilitate wayfinding^3^, have a consequent need to understand the relations between the urban environment and spatial cognition^4^. Previous studies have suggested that certain qualities of city elements, such as a distinct contrast with the background (e.g. The Eiffel Tower in Paris), or a clear morphology (e.g. the grid layout of Manhattan's streets) affect spatial behaviour and cognition^1,5-7^. However, only a few empirical studies have examined the relations between the urban environment and spatial cognition. Here we suggest that testing rats in experimental environments that simulate certain facets of urban environment can provide an insight into human spatial behaviour in urban environments with a similar layout. Specifically, we simulated two city layouts: (1) a grid street layout such as that of Manhattan; and (2) an irregular street layout such as that of Jerusalem. We found that the rats that were tested in the grid layout covered more ground and visited more locations, compared with the restricted movement demonstrated by the rats tested in the irregular layout. This finding in rats is in accordance with previous findings that urban grids conduce to high movement flow throughout the city, compared to low movement flow in irregular urban layouts^8-9^. Previous studies revealed that the spatial behaviour of rats and humans is controlled by the same underlying mechanisms^10-11^. In the same vein, we show that rats demonstrate spatial movement patterns that recall those of humans in similar urban environments. Rat behaviour may thus offer an in-vivo means for testing and analyzing the spatial cognitive principles of specific urban designs and for inferring how humans may perceive a particular urban environment and orient in it

Coevolution of agents and networks: Opinion spreading and community disconnection

We study a stochastic model for the coevolution of a process of opinion formation in a population of agents and the network which underlies their interaction. Interaction links can break when agents fail to reach an opinion agreement. The structure of the network and the distribution of opinions over the population evolve towards a state where the population is divided into disconnected communities whose agents share the same opinion. The statistical properties of this final state vary considerably as the model parameters are changed. Community sizes and their internal connectivity are the quantities used to characterize such variations.Comment: To appear in Phys. Lett.

Challenges in network science: Applications to infrastructures, climate, social systems and economics

Network theory has become one of the most visible theoretical frameworks that can be applied to the description, analysis, understanding, design and repair of multi-level complex systems. Complex networks occur everywhere, in man-made and human social systems, in organic and inorganic matter, from nano to macro scales, and in natural and anthropogenic structures. New applications are developed at an ever-increasing rate and the promise for future growth is high, since increasingly we interact with one another within these vital and complex environments. Despite all the great successes of this field, crucial aspects of multi-level complex systems have been largely ignored. Important challenges of network science are to take into account many of these missing realistic features such as strong coupling between networks (networks are not isolated), the dynamics of networks (networks are not static), interrelationships between structure, dynamics and function of networks, interdependencies in given networks (and other classes of links, including different signs of interactions), and spatial properties (including geographical aspects) of networks. This aim of this paper is to introduce and discuss the challenges that future network science needs to address, and how different disciplines will be accordingly affected. Graphical abstrac

Zipf's law, 1/f noise, and fractal hierarchy

Fractals, 1/f noise, Zipf's law, and the occurrence of large catastrophic events are typical ubiquitous general empirical observations across the individual sciences which cannot be understood within the set of references developed within the specific scientific domains. All these observations are associated with scaling laws and have caused a broad research interest in the scientific circle. However, the inherent relationships between these scaling phenomena are still pending questions remaining to be researched. In this paper, theoretical derivation and mathematical experiments are employed to reveal the analogy between fractal patterns, 1/f noise, and the Zipf distribution. First, the multifractal process follows the generalized Zipf's law empirically. Second, a 1/f spectrum is identical in mathematical form to Zipf's law. Third, both 1/f spectra and Zipf's law can be converted into a self-similar hierarchy. Fourth, fractals, 1/f spectra, Zipf's law, and the occurrence of large catastrophic events can be described with similar exponential laws and power laws. The self-similar hierarchy is a more general framework or structure which can be used to encompass or unify different scaling phenomena and rules in both physical and social systems such as cities, rivers, earthquakes, fractals, 1/f noise, and rank-size distributions. The mathematical laws on the hierarchical structure can provide us with a holistic perspective of looking at complexity such as self-organized criticality (SOC).Comment: 20 pages, 9 figures, 3 table

Self-organized integration vs. self-organized disintegration: an unfinished study

This paper refers to an issue Haken and myself were discussing, started to work on, prepared a preliminary draft, but never managed to complete and transform it into a full-scale study and publication. Here, in memoriam of Hermann Haken, my dear friend and colleague for many years, I present it as it is – an unfinished study with some innovative ideas that will have to be further elaborated in the future

Exploring the Role of Spatial Cognition in Predicting Urban Traffic Flow through Agent-based Modelling

Urban systems are highly complex and non-linear in nature, defined by the behaviours and interactions of many individuals. Building on a wealth of new data and advanced simulation methods, conventional research into urban systems seeks to embrace this complexity, measuring and modelling cities with increasingly greater detail and reliability. The practice of transportation modelling, despite recent developments, lags behind these advances. This paper addresses the implications resulting from variations in model design, with a focus on the behaviour and cognition of drivers, demonstrating how different models of choice and experience significantly influence the distribution of traffic. It is demonstrated how conventional models of urban traffic have not fully incorporated many of the important findings from the cognitive science domain, instead often describing actions in terms of individual optimisation. We introduce exploratory agent-based modelling that incorporates representations of behaviour from a more cognitively rich perspective. Specifically, through these simulations, we identify how spatial cognition in respect to route selection and the inclusion of heterogeneity in spatial knowledge significantly impact the spatial extent and volume of traffic flow within a real-world setting. These initial results indicate that individual-level models of spatial cognition can potentially play an important role in predicting urban traffic flow, and that greater heed should be paid to these approaches going forward. The findings from this work hold important lessons in the development of models of transport systems and hold potential implications for policy

Challenges in network science: Applications to infrastructures, climate, social systems and economics

Network theory has become one of the most visible theoretical frameworks that can be applied to the description, analysis, understanding, design and repair of multi-level complex systems. Complex networks occur everywhere, in man-made and human social systems, in organic and inorganic matter, from nano to macro scales, and in natural and anthropogenic structures. New applications are developed at an ever-increasing rate and the promise for future growth is high, since increasingly we interact with one another within these vital and complex environments. Despite all the great successes of this field, crucial aspects of multi-level complex systems have been largely ignored. Important challenges of network science are to take into account many of these missing realistic features such as strong coupling between networks (networks are not isolated), the dynamics of networks (networks are not static), interrelationships between structure, dynamics and function of networks, interdependencies in given networks (and other classes of links, including different signs of interactions), and spatial properties (including geographical aspects) of networks. This aim of this paper is to introduce and discuss the challenges that future network science needs to address, and how different disciplines will be accordingly affected