Month: August 2018

The first global trading network

Little is known about the structural patterns and dynamics of the first global trading market (FGTM), which emerged during the sixteenth century as a result of the Iberian expansion, let alone how it compares to today’s global financial markets. Here we build a representative network of the FGTM using information contained in 8725 (handwritten) Bills of Exchange from that time—which were (human) interpreted and digitalized into an online database. We show that the resulting temporal network exhibits a hierarchical, highly clustered and disassortative structure, with a power-law dependence on the connectivity that remains remarkably robust throughout the entire period investigated. Temporal analysis shows that, despite major turnovers in the number and nature of the links—suggesting fast adaptation in response to the geopolitical and financial turmoil experienced at the time—the overall characteristics of the FGTM remain robust and virtually unchanged. The methodology developed here demonstrates the possibility of building and analysing complex trading and finance networks originating from pre-statistical eras, enabling us to highlight the striking similarities between the structural patterns of financial networks separated by centuries in time.


Structural and temporal patterns of the first global trading market
Ana Sofia Ribeiro, Flávio L. Pinheiro, Francisco C. Santos, Amélia Polónia, Jorge M. Pacheco

Royal Society Open Science
Published 22 August 2018.DOI: 10.1098/rsos.180577


Towards Digital Enlightenment – Essays on the Dark and Light Sides of the Digital Revolution, Dirk Helbing (Ed.)

A new collection of essays by the author of the successful volume Thinking Ahead – Essays on Big Data, Digital Revolution, and Participatory Market Society
Examines the dangers of a world in which algorithms and social bots aim to control both the societal dynamics and individual behaviors.
Introduces novel approaches on how to redefine collective trust and build platforms to support core societal values

Towards Digital Enlightenment
Essays on the Dark and Light Sides of the Digital Revolution
Editors: Helbing, Dirk (Ed.)


Large-scale estimation of parking requirements for autonomous mobility on demand systems

Cities everywhere are anticipating new mobility technologies to help solve issues with congestion and pollution while providing afforable, accessible, reliable and convenient transportation for growing populations. The adoption of self-driving vehicles is projected to happen soon and help with achieving these goals, especially if part of a shared mobility on demand service. Potential benefits of such a system include a reduction of the number of vehicles and freeing up parking spaces, while challenges still include managing the traffic volume. Previous research focused on estimating fleet size in different scenarios. In this work, we focus on estimating minimum fleet size, parking needs and total travel distance for an autonomous mobility on demand solution serving all trips made in private vehicles in Singapore, generated from a comprehensive simulation of the city’s mobility. We specifically focus on parking demand as currently a significant amount of space has to be designated as parking in cities, which is poised to become obsolate if people switch from private vehicles to shared ones which are utilized much more efficiently. We show that over 85% reduction in the number of vehicles and parking spaces can be achieved while serving all trips made currently in private vehicles. We further show that potential increased traffic volume can be mitigated with the incorporation of ride-sharing, while offering even higher savings, up to 92% in both fleet size and parking needs.


Large-scale estimation of parking requirements for autonomous mobility on demand systems
Daniel Kondor, Paolo Santi, Kakali Basak, Xiaohu Zhang, Carlo Ratti


Transfer Entropy


Statistical relationships among the variables of a complex system reveal a lot about its physical behavior. Therefore, identification of the relevant variables and characterization of their interactions are crucial for a better understanding of a complex system. Correlation-based techniques have been widely utilized to elucidate the linear statistical dependencies in many science and engineering applications. However, for the analysis of nonlinear dependencies, information-theoretic quantities, such as Mutual Information (MI) and the Transfer Entropy (TE), have been proven to be superior. MI quantifies the amount of information obtained about one random variable, through the other random variable, and it is symmetric. As an asymmetrical measure, TE quantifies the amount of directed (time-asymmetric) transfer of information between random processes and therefore is related to the measures of causality. Open Access
© 2018 MDPI; under CC BY-NC-ND license
Transfer Entropy
Deniz Gençağa (Ed.)
Pages: VIII, 326
Published: August 2018


Advances on the Resilience of Complex Networks (Complexity Special Issue)

A common property of many complex systems is resilience, that is, the ability of the system to react to perturbations, internal failures, and environmental events by absorbing the disturbance and/or rebuild to maintain its functions. Nowadays, understanding how complex systems demonstrate resilience is critical in many different fields, because examples of collapses and crises caused by low resilience are more and more spreading all over the world including transportation, financial, energy, communication, and ecological systems.

Therefore, in the last decade, the topic of resilience has grown a lot in popularity. Studies on resilience are popular in multiple disciplines, such as ecology, environmental science, computer science, engineering, management science, economics, and phycology. They investigate resilience of a broad variety of complex systems involving individuals, teams, ecosystems, organizations, communities, supply chains, financial networks, computer networks, and building infrastructures.

Despite this multidisciplinary nature, two main perspectives in the conceptualization of resilience are recognized, that is, the static and dynamic ones [1–4]. The resilience is static when it focuses on the ability of the system to absorb disturbance and bounce back to the original equilibrium state, maintaining its core functions when shocked. In such a case, the resilience is linked to the ability to recover the original shape and features once stretched (robustness) and the capacity of the system to take alternative positions to respond better to change (flexibility). The dynamic perspective focuses on the ability of the system to evolve over time moving towards a new more favorable equilibrium state. According to this perspective, resilience concerns the adaptive capacity of the system, which is able to react to disturbance by changing its structure, processes, and functions in order to increase its ability to persist [5].

This special issue collects nine papers concerning resilience of complex systems, which accords well with the main features summarized above. They concern studies investigating resilience of complex systems in diverse disciplines (engineering, management science, computer science, economics, and organization science) and adopting both the static and dynamic perspectives. Their aim is to identify the main factors and dynamics influencing resilience of diverse systems (water system infrastructures, organizational teams, financial markets, wireless sensor network, and urban system) to a variety of unexpected and negative events


Volume 2018, Article ID 8756418, 3 pages
Advances on the Resilience of Complex Networks
Ilaria Giannoccaro, Vito Albino, and Anand Nair