Month: November 2018

Charting the Next Pandemic

This book provides an introduction to the computational and complex systems modeling of the global spreading of infectious diseases. The latest developments in the area of contagion processes modeling are discussed, and readers are exposed to real world examples of data-model integration impacting the decision-making process. Recent advances in computational science and the increasing availability of real-world data are making it possible to develop realistic scenarios and real-time forecasts of the global spreading of emerging health threats.

The first part of the book guides the reader through sophisticated complex systems modeling techniques with a non-technical and visual approach, explaining and illustrating the construction of the modern framework used to project the spread of pandemics and epidemics. Models can be used to transform data to knowledge that is intuitively communicated by powerful infographics and for this reason, the second part of the book focuses on a set of charts that illustrate possible scenarios of future pandemics. The visual atlas contained allows the reader to identify commonalities and patterns in emerging health threats, as well as explore the wide range of models and data that can be used by policy makers to anticipate trends, evaluate risks and eventually manage future events.

Charting the Next Pandemic puts the reader in the position to explore different pandemic scenarios and to understand the potential impact of available containment and prevention strategies. This book emphasizes the importance of a global perspective in the assessment of emerging health threats and captures the possible evolution of the next pandemic, while at the same time providing the intelligence needed to fight it. The text will appeal to a wide range of audiences with diverse technical backgrounds.


Charting the Next Pandemic
Modeling Infectious Disease Spreading in the Data Science Age
Ana Pastore y Piontti, Nicola Perra, Luca Rossi, Nicole Samay, Alessandro Vespignani


Information | Special Issue : Computational Social Science

The last centuries have seen a great surge in our understanding and control of ‘simple’ physical, chemical, and biological processes through data analysis and the mathematical modelling of their underlying dynamics. Encouraged by its success, researchers have recently embarked on extending such approaches to gain qualitative and quantitative understanding of social and economic systems and the dynamics in and of them. This has become possible due to the massive amounts of data generated by information-communication technologies and the unprecedented fusion of off- and on-line human activity. However, due to the presence of adaptability, feedback loops, and strong heterogeneities of the individuals and interactions making up our modern digital societies, it is yet unclear if statistical ‘laws’ of socio-technical behaviour even exist, akin to those found for natural processes. Such continuing search has resulted in the fields of computational social science and social network science, which share the goal of first analysing social phenomena and then modelling them with enough accuracy to make reliable predictions. This Special Issue invites contributions to such fields of study, with focus on the temporal evolution and dynamics of complex social systems. As topics of interest, we propose research on more realistic models of social dynamics, the use of statistical inference, machine learning, and other cross-disciplinary techniques to complement the analysis of social dynamics, and the creation of loops between data acquisition and model analysis to increase accuracy in the prediction of social trends. We hope this Special Issue will bring together expertise from a wide range of research communities interested in similar topics, including computational social science, network science, information science, and complexity science.


Cities, from Information to Interaction

From physics to the social sciences, information is now seen as a fundamental component of reality. However, a form of information seems still underestimated, perhaps precisely because it is so pervasive that we take it for granted: the information encoded in the very environment we live in. We still do not fully understand how information takes the form of cities, and how our minds deal with it in order to learn about the world, make daily decisions, and take part in the complex system of interactions we create as we live together. This paper addresses three related problems that need to be solved if we are to understand the role of environmental information: (1) the physical problem: how can we preserve information in the built environment? (2) The semantic problem: how do we make environmental information meaningful? and (3) the pragmatic problem: how do we use environmental information in our daily lives? Attempting to devise a solution to these problems, we introduce a three-layered model of information in cities, namely environmental information in physical space, environmental information in semantic space, and the information enacted by interacting agents. We propose forms of estimating entropy in these different layers, and apply these measures to emblematic urban cases and simulated scenarios. Our results suggest that ordered spatial structures and diverse land use patterns encode information, and that aspects of physical and semantic information affect coordination in interaction systems.


Cities, from Information to Interaction.
Netto, V.M.; Brigatti, E.; Meirelles, J.; Ribeiro, F.L.; Pace, B.; Cacholas, C.; Sanches, P.
Entropy 2018, 20, 834.


Power and Leadership: A Complex Systems Science Approach Part I—Representation and Dynamics

Historical social narratives are dominated by the actions of powerful individuals as well as competitions for power including warfare, revolutions, and political change. Advancing our understanding of the origins, types and competitive strength of different kinds of power may yield a scaffolding for understanding historical processes and mechanisms for winning or avoiding conflicts. Michael Mann introduced a framework distinguishing four types of power: political, military, economic, and ideological. We show this framework can be justified based upon motivations of individuals to transfer decision making authority to leaders: Desire to be a member of a collective, avoiding harm due to threat, gaining benefit due to payment, acquiring a value system. Constructing models of societies based upon these types of power enables us to distinguish between social systems and describe their dynamics. Dynamical processes include (a) competition between power systems, (b) competition between powerful individuals within a power system of a society, and (c) the dynamics of values within a powerful individual. A historical trend in kinds of power systems is the progressive separation of types of power to distinct groups of individuals. In ancient empires all forms of power were concentrated in a single individual, e.g. Caesar during the Pax Romana period. In an idealized modern democratic state, the four types of power are concentrated in distinct sets of individuals. The progressive separation of the types of power suggests that in some contexts this confers a "fitness" advantage in an evolutionary process similar to the selection of biological organisms. However, individual countries may not separate power completely. The influence of wealth in politics and regulatory capture is a signature of the dominance of economic leaders, e.g. the US. Important roles of political leaders in economics and corruption are a signature of the dominance of political leaders, e.g. China. Ideological leaders dominate in theocracies, e.g. Iran. Military leaders dominate in dictatorships or countries where military leaders play a role in the selection of leaders, e.g. Egypt.


Yaneer Bar-Yam, Power and leadership: A complex systems science approach Part I—Representation and dynamics, arXiv:1811.02896 (November 7, 2018).


Senior Researcher in Complex Systems @LakesideLabs

Lakeside Labs is a research and innovation company driven by the vision to create solutions for networked systems using concepts from self-organization. To further strengthen our team, we have an opening for a senior researcher position in complex systems engineering with emphasis on robotics/drones and autonomous transportation.

* Perform outstanding research in the field of complex systems
* Publish in high-tier scientific journals and conferences
* Actively participate in research projects
* Collaborate with companies and research partners
* Take responsibility in project management
* Contribute to project proposals on a national and European level
The successful candidate will initially work, in a team of three researchers, in a European research project on design methods for cyber-physical systems with emphasis on swarm intelligence and its integration into a model-based library.