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Computational Social Science and Complex Systems, edited by J. Kertész, R.N. Mantegna, S. Miccichè

Sat, 02/01/2020 - 17:08

For many years, the development of large-scale quantitative social science was hindered by a lack of data. Traditional methods of data collection like surveys were very useful, but were limited. The situation has of course changed with the development of computing and information communication technology, and we now live in a world of data deluge, where the question has become how to extract important information from the plethora of data that can be accessed. Big Data has made it possible to study societal questions which were once impossible to deal with, but new tools and new multidisciplinary approaches are required. Physicists, together with economists, sociologists, computer scientists, etc. have played an important role in their development.


This book presents the 9 lectures delivered at the CCIII Summer Course Computational Social Science and Complex Systems, held as part of the International School of Physics Enrico Fermi in Varenna, Italy, from 16-21 July 2018. The course had the aim of presenting some of the recent developments in the interdisciplinary fields of computational social science and econophysics to PhD students and young researchers, with lectures focused on recent problems investigated in computational social science.


Addressing some of the basic questions and many of the subtleties of the emerging field of computational social science, the book will be of interest to students, researchers and advanced research professionals alike.


Early epidemiological analysis of the 2019-nCoV outbreak based on a crowdsourced data

Sat, 02/01/2020 - 11:49

Kaiyuan Sun, Jenny Chen, Cécile Viboud.


Starting in December 2019, Chinese health authorities have been closely monitoring a cluster of pneumonia cases in the city of Wuhan, in Hubei Province. It has been determined that the causing agent of the viral pneumonia among affected individuals is a new coronavirus (2019-nCoV). As of January 29, 2020, a total of 6,088 cases have been detected and confirmed in Mainland China, with more than 70 additional cases detected and confirmed internationally in Japan, Thailand, South Korea, Taiwan, Singapore, Vietnam, United States, France, Australia, Nepal, Canada, Cambodia, Sri Lanka, United Arab Emirates, Finland, and Germany. By using the cases detected outside China we are providing estimates of size of the Wuhan outbreak as of January 29th, 2020.​ By using an estimate of 10 days from exposure to detection and an effective population of 20 million people in Wuhan catchment area the estimated median size of the Wuhan outbreak is 31,200 infections [95% CI: 23,400-40,400]. Technical details are in the full report available below. 


Phase transitions in information spreading on structured populations

Fri, 01/31/2020 - 17:19

Davis, Jessica, Perra, Nicola, Zhang, Qian, Moreno, Yamir and Vespignani, Alessandro (2020) Phase transitions in information spreading on structured populations. Nature Physics. ISSN 1745-2473 (Print), 1745-2481 (Online) (In Press)


Mathematical models of social contagion that incorporate networks of human interactions have become increasingly popular, however, very few approaches have tackled the challenges of including complex and realistic properties of socio-technical systems. In this work we define a framework to characterize the dynamics of the Maki-Thompson rumor spreading model in structured populations, and analytically find a previously uncharacterized dynamical phase transition that separates the local and global contagion regimes. We validate our threshold prediction through extensive Monte Carlo simulations. Furthermore, we apply this framework in two real-world systems, the European commuting and transportation network and the Digital Bibliography and Library Project (DBLP) collaboration network. Our findings highlight the importance of the underlying population structure in understanding social contagion phenomena and have the potential to define new intervention strategies aimed at hindering or facilitating the diffusion of information in socio-technical systems.


Dualities and non-Abelian mechanics

Thu, 01/30/2020 - 05:48

Michel Fruchart, Yujie Zhou & Vincenzo Vitelli 
Nature volume 577, pages 636–640 (2020)


Dualities are mathematical mappings that reveal links between apparently unrelated systems in virtually every branch of physics1,2,3,4,5,6,7,8. Systems mapped onto themselves by a duality transformation are called self-dual and exhibit remarkable properties, as exemplified by the scale invariance of an Ising magnet at the critical point. Here we show how dualities can enhance the symmetries of a dynamical matrix (or Hamiltonian), enabling the design of metamaterials with emergent properties that escape a standard group theory analysis. As an illustration, we consider twisted kagome lattices9,10,11,12,13,14,15, reconfigurable mechanical structures that change shape by means of a collapse mechanism9. We observe that pairs of distinct configurations along the mechanism exhibit the same vibrational spectrum and related elastic moduli. We show that these puzzling properties arise from a duality between pairs of configurations on either side of a mechanical critical point. The critical point corresponds to a self-dual structure with isotropic elasticity even in the absence of spatial symmetries and a twofold-degenerate spectrum over the entire Brillouin zone. The spectral degeneracy originates from a version of Kramers’ theorem16,17 in which fermionic time-reversal invariance is replaced by a hidden symmetry emerging at the self-dual point. The normal modes of the self-dual systems exhibit non-Abelian geometric phases18,19 that affect the semiclassical propagation of wavepackets20, leading to non-commuting mechanical responses. Our results hold promise for holonomic computation21 and mechanical spintronics by allowing on-the-fly manipulation of synthetic spins carried by phonons.


Prosociality in the economic Dictator Game is associated with less parochialism and greater willingness to vote for intergroup compromise

Thu, 01/30/2020 - 02:30

Mohsen Mosleh. Alexander J. Stewart, Joshua B. Plotkin, David G. Rand


Is prosociality parochial or universalist? To shed light on this issue, we examine the relationship between the amount of money given to a stranger (giving in an incentivized Dictator Game) and intergroup attitudes and behavior in the context of randomly assigned teams (a minimal group paradigm) among N = 4,846 Amazon Mechanical Turk workers. Using a set of Dynamic Identity Diffusion Index measures, we find that participants who give more in the Dictator Game show less preferential identification with their team relative to the other team, and more identification with all participants regardless of team. Furthermore, in an incentivized Voter Game, participants who give more in the Dictator Game are more likely to support compromise by voting for the opposing team in order to avoid deadlock. Together, these results suggest that – at least in this subject pool and using these measures – prosociality is better characterized by universalism than parochialism.


Keywords: Prosociality, Dictator Game, Ingroup Bias, Intergroup Attitude


Counterfactual thinking in cooperation dynamics

Wed, 01/29/2020 - 17:21

Moniz Pereira, Luis; Santos, Francisco C.


Counterfactual Thinking is a human cognitive ability studied in a wide variety of domains. It captures the process of reasoning about a past event that did not occur, namely what would have happened had this event occurred, or, otherwise, to reason about an event that did occur but what would ensue had it not. Given the wide cognitive empowerment of counterfactual reasoning in the human individual, the question arises of how the presence of individuals with this capability may improve cooperation in populations of self-regarding individuals. Here we propose a mathematical model, grounded on Evolutionary Game Theory, to examine the population dynamics emerging from the interplay between counterfactual thinking and social learning (i.e., individuals that learn from the actions and success of others) whenever the individuals in the population face a collective dilemma. Our results suggest that counterfactual reasoning fosters coordination in collective action problems occurring in large populations, and has a limited impact on cooperation dilemmas in which coordination is not required. Moreover, we show that a small prevalence of individuals resorting to counterfactual thinking is enough to nudge an entire population towards highly cooperative standards.


Phase Transitions in Spatial Connectivity during Influenza Pandemics

Tue, 01/28/2020 - 19:17

Nathan Harding, Richard E Spinney, and Mikhail Prokopenko

Entropy 202022(2), 133


We investigated phase transitions in spatial connectivity during influenza pandemics, relating epidemic thresholds to the formation of clusters defined in terms of average infection. We employed a large-scale agent-based model of influenza spread at a national level: the Australian Census-based Epidemic Model (ACEmathsizesmallMod). In using the ACEmathsizesmallMod simulation framework, which leverages the 2016 Australian census data and generates a surrogate population of ≈23.4 million agents, we analysed the spread of simulated epidemics across geographical regions defined according to the Australian Statistical Geography Standard. We considered adjacent geographic regions with above average prevalence to be connected, and the resultant spatial connectivity was then analysed at specific time points of the epidemic. Specifically, we focused on the times when the epidemic prevalence peaks, either nationally (first wave) or at a community level (second wave). Using the percolation theory, we quantified the connectivity and identified critical regimes corresponding to abrupt changes in patterns of the spatial distribution of infection. The analysis of criticality is confirmed by computing Fisher Information in a model-independent way. The results suggest that the post-critical phase is characterised by different spatial patterns of infection developed during the first or second waves (distinguishing urban and rural epidemic peaks).


The exposome and health: Where chemistry meets biology

Tue, 01/28/2020 - 17:06

Roel Vermeulen, Emma L. Schymanski, Albert-László Barabási, Gary W. Miller
Science 24 Jan 2020:
Vol. 367, Issue 6476, pp. 392-396
DOI: 10.1126/science.aay3164


Despite extensive evidence showing that exposure to specific chemicals can lead to disease, current research approaches and regulatory policies fail to address the chemical complexity of our world. To safeguard current and future generations from the increasing number of chemicals polluting our environment, a systematic and agnostic approach is needed. The “exposome” concept strives to capture the diversity and range of exposures to synthetic chemicals, dietary constituents, psychosocial stressors, and physical factors, as well as their corresponding biological responses. Technological advances such as high-resolution mass spectrometry and network science have allowed us to take the first steps toward a comprehensive assessment of the exposome. Given the increased recognition of the dominant role that nongenetic factors play in disease, an effort to characterize the exposome at a scale comparable to that of the human genome is warranted.


Steven Strogatz Talks Science and Math on the Joy of x Podcast

Fri, 01/24/2020 - 17:32

The noted mathematician and author Steven Strogatz explains why he wanted to share intimate conversations with leading researchers from diverse fields in his new Quanta Magazine podcast.


AlphaFold: Using AI for scientific discovery | DeepMind

Fri, 01/24/2020 - 11:29

In our study published today in Nature, we demonstrate how artificial intelligence research can drive and accelerate new scientific discoveries. We’ve built a dedicated, interdisciplinary team in hopes of using AI to push basic research forward: bringing together experts from the fields of structural biology, physics, and machine learning to apply cutting-edge techniques to predict the 3D structure of a protein based solely on its genetic sequence.


Universals and variations in moral decisions made in 42 countries by 70,000 participants

Thu, 01/23/2020 - 17:36

Edmond Awad, Sohan Dsouza, Azim Shariff, Iyad Rahwan, and Jean-François Bonnefon


We report the largest cross-cultural study of moral preferences in sacrificial dilemmas, that is, the circumstances under which people find it acceptable to sacrifice one life to save several. On the basis of 70,000 responses to three dilemmas, collected in 10 languages and 42 countries, we document a universal qualitative pattern of preferences together with substantial country-level variations in the strength of these preferences. In particular, we document a strong association between low relational mobility (where people are more cautious about not alienating their current social partners) and the tendency to reject sacrifices for the greater good—which may be explained by the positive social signal sent by such a rejection. We make our dataset publicly available for researchers.




Complex economic activities concentrate in large cities

Wed, 01/22/2020 - 17:04

Pierre-Alexandre Balland, Cristian Jara-Figueroa, Sergio G. Petralia, Mathieu P. A. Steijn, David L. Rigby & César A. Hidalgo 
Nature Human Behaviour (2020)


Human activities, such as research, innovation and industry, concentrate disproportionately in large cities. The ten most innovative cities in the United States account for 23% of the national population, but for 48% of its patents and 33% of its gross domestic product. But why has human activity become increasingly concentrated? Here we use data on scientific papers, patents, employment and gross domestic product, for 353 metropolitan areas in the United States, to show that the spatial concentration of productive activities increases with their complexity. Complex economic activities, such as biotechnology, neurobiology and semiconductors, concentrate disproportionately in a few large cities compared to less–complex activities, such as apparel or paper manufacturing. We use multiple proxies to measure the complexity of activities, finding that complexity explains from 40% to 80% of the variance in urban concentration of occupations, industries, scientific fields and technologies. Using historical patent data, we show that the spatial concentration of cutting-edge technologies has increased since 1850, suggesting a reinforcing cycle between the increase in the complexity of activities and urbanization. These findings suggest that the growth of spatial inequality may be connected to the increasing complexity of the economy.


Reactive, Proactive, and Inductive Agents: An Evolutionary Path for Biological and Artificial Spiking Networks

Wed, 01/22/2020 - 15:41

Lana Sinapayen, Atsushi Masumori, and Takashi Ikegami

Front. Comput. Neurosci., 22 January 2020


Complex environments provide structured yet variable sensory inputs. To best exploit information from these environments, organisms must evolve the ability to anticipate consequences of new stimuli, and act on these predictions. We propose an evolutionary path for neural networks, leading an organism from reactive behavior to simple proactive behavior and from simple proactive behavior to induction-based behavior. Based on earlier in-vitro and in-silico experiments, we define the conditions necessary in a network with spike-timing dependent plasticity for the organism to go from reactive to proactive behavior. Our results support the existence of specific evolutionary steps and four conditions necessary for embodied neural networks to evolve predictive and inductive abilities from an initial reactive strategy.


Analysis and control of epidemics in temporal networks with self-excitement and behavioral changes

Wed, 01/22/2020 - 14:29

Lorenzo Zino, Alessandro . Rizzo, Maurizio Porfiri

European Journal of Control


The complexity of interaction patterns among individuals in social systems plays a fundamental role on the inception and spreading of epidemic outbreaks. Empirical evidence has shown that the network of social interactions may co-evolve with the spread of the disease at comparable time-scales. Time-varying features have also been documented in the study of the propensity of individuals toward social activity, leading to the emergence of burstiness and temporal clustering. These temporal network dynamics are not independent of the disease evolution, whereby infected individuals could experience changes in their tendency to form connections, spontaneously or due to exogenous control policies. Neglecting these phenomena in modeling epidemics could lead to dangerous mispredictions of an outbreak and ineffective control interventions. In this paper, we propose a mathematically tractable modeling framework that relies on a limited number of parameters and encapsulates all these instances of complex phenomena through the lens of activity driven networks. Hawkes processes, Markov chains, and stability theory are leveraged to assist in the analysis of the framework and the formulation of theory-based control interventions. Our mathematical findings confirm the intuition that bursty activity patterns, typical of humans, facilitate epidemic spreading, while behavioral changes aiming at individual isolation could accelerate the eradication of epidemics. The proposed tools are demonstrated on a real-world case of influenza spreading in Italy. Overall, this work contributes new insight into the theory of temporal networks, laying the foundations for the analysis and control of spreading processes over networks with complex interaction patterns.


Neural Dendrites Reveal Their Computational Power

Tue, 01/21/2020 - 17:38

The dendritic arms of some human neurons can perform logic operations that once seemed to require whole neural networks.


Mediterranean School of Complex Networks 2020

Tue, 01/21/2020 - 16:59

Date: 5 Sep – 12 Sep 2020
Location: Salina, Sicily


In the last decade, network theory has been revealed to be a perfect instrument to model the structure of complex systems and the dynamical process they are involved into. The wide variety of applications to social sciences, technological networks, biology, transportation and economic, to cite just only some of them, showed that network theory is suitable to provide new insights into many problems.
Given the success of the Sixth Edition in 2019 of the Mediterranean School of Complex Networks, we call for applications to the Seventh Edition in 2020.


Network experiment demonstrates converse symmetry breaking

Tue, 01/21/2020 - 10:23

F. Molnar, T. Nishikawa, and A.E. Motter,
Nature Physics (2020), doi:10.1038/s41567-019-0742-y.

Symmetry breaking—the phenomenon in which the symmetry of a system is not inherited by its stable states—underlies pattern formation, superconductivity and numerous other effects. Recent theoretical work has established the possibility of converse symmetry breaking, a phenomenon in which the stable states are symmetric only when the system itself is not. This includes scenarios in which interacting entities are required to be non-identical in order to exhibit identical behaviour, such as in reaching consensus. Here we present an experimental demonstration of this phenomenon. Using a network of alternating-current electromechanical oscillators, we show that their ability to achieve identical frequency synchronization is enhanced when the oscillators are tuned to be suitably non-identical and that converse symmetry breaking persists for a range of noise levels. These results have implications for the optimization and control of network dynamics in a broad class of systems whose function benefits from harnessing uniform behaviour.


A scalable pipeline for designing reconfigurable organisms

Sat, 01/18/2020 - 14:59

Sam Kriegman, Douglas Blackiston, Michael Levin, and Josh Bongard


Most technologies are made from steel, concrete, chemicals, and plastics, which degrade over time and can produce harmful ecological and health side effects. It would thus be useful to build technologies using self-renewing and biocompatible materials, of which the ideal candidates are living systems themselves. Thus, we here present a method that designs completely biological machines from the ground up: computers automatically design new machines in simulation, and the best designs are then built by combining together different biological tissues. This suggests others may use this approach to design a variety of living machines to safely deliver drugs inside the human body, help with environmental remediation, or further broaden our understanding of the diverse forms and functions life may adopt.


Report: The future of urban science: integrating the social and natural sciences 

Fri, 01/17/2020 - 16:42

Urban science seeks to understand the fundamental processes that drive, shape and sustain cities and urbanization. It is a multi/transdisciplinary approach involving concepts, methods and research from the social, natural, engineering and computational sciences, along with the humanities. This report is intended to convey the current “state of the art” in urban science while also clearly indicating how urban science builds upon and complements (but does not replace) prior work on cities and urbanization in many other disciplines. The report does not aim at a fully comprehensive synopsis of work done under the rubric of “urban science” but it does aim to convey what makes urban science different from discipline-based examinations of cities and urbanization. It also highlights novel insights generated by the inherently multidisciplinary inquiry that urban science exemplifies."


Tenth International Conference on Complex Systems

Fri, 01/17/2020 - 13:43

The International Conference on Complex Systems is a unique interdisciplinary forum that unifies and bridges the traditional domains of science and a multitude of real world systems. Participants will contribute and be exposed to mind expanding concepts and methods from across the diverse field of complex systems science. The conference will be held July 26-31, 2020, in Nashua, NH, USA.