Complexity Digest

Subscribe to Complexity Digest feed Complexity Digest
Networking the complexity community since 1999
Updated: 23 min 16 sec ago

Suzanne Simard interview: How I uncovered the hidden language of trees

7 hours 40 min ago

First she discovered the wood wide web. Now Suzanne Simard has found that underground connections in a forest are like a brain that allows trees to form societies – and look out for their kin

Read the full article at:

Technology & Society: social, philosophical and ethical implications for the 21st century

Tue, 05/04/2021 - 12:59

Francis Heylighen

This richly illustrated manuscript including an extensive bibliography forms the lecture notes of a course with the same title. This course tries to give the students a deeper insight into what technology is, and how it affects human life on this planet. Given how pervasive and dominant technological systems have become in this 21st century, it is important to understand the dynamics that propel its ever-faster development. It is especially important to understand, on the one hand, the negative effects and dangers of this development, so that we can mitigate or evade those, on the other hand, the benefits and promises, so that we can further promote and enhance them. These issues are reviewed from a systems/cybernetics perspective. The focus is on accelerating evolution, technology as mediator and human-technology symbiosis, leading up to the notion of a global superorganism.

Read the full article at:

Too Lazy to Read the Paper: Episode 5 with Renaud Lambiotte

Tue, 05/04/2021 - 09:52


Today’s guest is Renaud Lambiotte

Renaud is an associate professor at the Mathematical Institute of Oxford University, investigating processes taking place on large networks.

In the episode, we talk about his story in science, the joy and value of exploring without a particular purpose, doing a PhD without publishing any papers, … and how reading classical texts by Boltzmann and others early on has shaped the work Renaud does even to this day.

When we get to the paper, we talk about Renaud’s recent work “Variance and covariance of distributions on graphs” (1) with co-authors Karel Devriendt and Samuel Martin-Gutierrez.


Watch at:

Distributions of historic market data: relaxation and correlations

Mon, 05/03/2021 - 12:27

M. Dashti Moghaddam, Zhiyuan Liu & R. A. Serota 
The European Physical Journal B volume 94, Article number: 83 (2021)

We investigate relaxation and correlations in a class of mean-reverting models for stochastic variances. We derive closed-form expressions for the correlation functions and leverage for a general form of the stochastic term. We also discuss correlation functions and leverage for three specific models— multiplicative, Heston (Cox-Ingersoll-Ross) and combined multiplicative-Heston—whose steady-state probability density functions are Gamma, Inverse Gamma and Beta Prime respectively, the latter two exhibiting “fat” tails. For the Heston model, we apply the eigenvalue analysis of the Fokker-Planck equation to derive the correlation function—in agreement with the general analysis— and to identify a series of time scales, which are observable in relaxation of cumulants on approach to the steady state. We test our findings on a very large set of historic financial markets data.

Read the full article at:

Transformative climate adaptation in the United States: Trends and prospects

Mon, 05/03/2021 - 11:05

Linda Shi and Susanne Moser

Science 29 Apr 2021: eabc8054
As climate change intensifies, civil society is increasingly calling for transformative adaptation that redresses drivers of climate vulnerability. We review trends in how U.S. federal government, private industry and civil society are planning for climate adaptation. We find growing divergence in their approaches and impacts. This incoherence increases maladaptive investment in climate-blind infrastructure, justice-blind reforms in financial and professional sectors, and greater societal vulnerability to climate impacts. If these actors were to proactively and deliberatively engage in transformative adaptation, they would need to address the material, relational and normative factors that hold current systems in place. Drawing on a review of transformation and collective impact literatures, we conclude with directions for research and policy engagement to support more transformative adaptation moving forward.

Read the full article at:

SARS-CoV-2 elimination, not mitigation, creates best outcomes for health, the economy, and civil liberties

Sun, 05/02/2021 - 11:47

Miquel Oliu-Barton, Bary S R Pradelski, Philippe Aghion, Patrick Artus, Ilona Kickbusch, Jeffrey V Lazarus, Devi Sridhar, Samantha Vanderslott

The Lancet

The trade-off between different objectives is at the heart of political decision making. Public health, economic growth, democratic solidarity, and civil liberties are important factors when evaluating pandemic responses. There is mounting evidence that these objectives do not need to be in conflict in the COVID-19 response. Countries that consistently aim for elimination—ie, maximum action to control SARS-CoV-2 and stop community transmission as quickly as possible—have generally fared better than countries that opt for mitigation—ie, action increased in a stepwise, targeted way to reduce cases so as not to overwhelm health-care systems.

Read the full article at:

Universal dynamics of ranking

Sun, 05/02/2021 - 08:06

Gerardo Iñiguez, Carlos Pineda, Carlos Gershenson, Albert-László Barabási
Virtually anything can be and is ranked; people and animals, universities and countries, words and genes. Rankings reduce the components of highly complex systems into ordered lists, aiming to capture the fitness or ability of each element to perform relevant functions, and are being used from socioeconomic policy to knowledge extraction. A century of research has found regularities in ranking lists across nature and society when data is aggregated over time. Far less is known, however, about ranking dynamics, when the elements change their rank in time. To bridge this gap, here we explore the dynamics of 30 ranking lists in natural, social, economic, and infrastructural systems, comprising millions of elements, whose temporal scales span from minutes to centuries. We find that the flux governing the arrival of new elements into a ranking list reveals systems with identifiable patterns of stability: in high-flux systems only the top of the list is stable, while in low-flux systems the top and bottom are equally stable. We show that two basic mechanisms – displacement and replacement of elements – are sufficient to understand and quantify ranking dynamics. The model uncovers two regimes in the dynamics of ranking lists: a fast regime dominated by long-range rank changes, and a slow regime driven by diffusion. Our results indicate that the balance between robustness and adaptability characterizing the dynamics of complex systems might be governed by random processes irrespective of the details of each system.

Read the full article at:

“Too Lazy to Read the Paper”: Episode 4 with Leidy Klotz

Fri, 04/30/2021 - 07:26

Our Episode 4 guest, Leidy Klotz, is a Professor at the University of Virginia. He studies the science of design: how we transform things from how they are – to how we want them to be. Leidy wants to apply his work outside of academia. He wants address climate change and systemic inequality, Leidy also works directly with organizations including the World Bank.

Stream and subscribe at:

“Too Lazy”: Episode 3 with Dirk Brockmann

Wed, 04/28/2021 - 07:25

This episode’s guest is Dirk Brockmann. Dirk is a physicist and complex systems researcher. He’s a professor at the Department of Biology, Humboldt University of Berlin and the Robert Koch Institute, Berlin. Berfore returning to his native Germany, he was a professor at Northwestern University.

Read the full article at:

Identifying tax evasion in Mexico with tools from network science and machine learning

Wed, 04/28/2021 - 07:19

Martin Zumaya, Rita Guerrero, Eduardo Islas, Omar Pineda, Carlos Gershenson, Gerardo Iñiguez, Carlos Pineda

Mexico has kept electronic records of all taxable transactions since 2014. Anonymized data collected by the Mexican federal government comprises more than 80 million contributors (individuals and companies) and almost 7 billion monthly-aggregations of invoices among contributors between January 2015 and December 2018. This data includes a list of almost ten thousand contributors already identified as tax evaders, due to their activities fabricating invoices for non-existing products or services so that recipients can evade taxes. Harnessing this extensive dataset, we build monthly and yearly temporal networks where nodes are contributors and directed links are invoices produced in a given time slice. Exploring the properties of the network neighborhoods around tax evaders, we show that their interaction patterns differ from those of the majority of contributors. In particular, invoicing loops between tax evaders and their clients are over-represented. With this insight, we use two machine-learning methods to classify other contributors as suspects of tax evasion: deep neural networks and random forests. We train each method with a portion of the tax evader list and test it with the rest, obtaining more than 0.9 accuracy with both methods. By using the complete dataset of contributors, each method classifies more than 100 thousand suspects of tax evasion, with more than 40 thousand suspects classified by both methods. We further reduce the number of suspects by focusing on those with a short network distance from known tax evaders. We thus obtain a list of highly suspicious contributors sorted by the amount of evaded tax, valuable information for the authorities to further investigate illegal tax activity in Mexico. With our methods, we estimate previously undetected tax evasion in the order of $10 billion USD per year by about 10 thousand contributors.

Read the full article at:

Call for Abstracts: CCS2021 Lyon: Conference on Complex Systems

Tue, 04/27/2021 - 13:00

CCS2021 is the flagship conference on Complex Systems promoted by the CSS. It brings under one umbrella a wide variety of leading researchers, practitioners and stakeholders with a direct interest in Complex Systems, from Physics to Computer Science, Biology, Social Sciences, Economics, and Technological and Communication Networks, among others.

Deadline for abstract submission: May 20, 2021
Notification to authors: June 20, 2021
Early Registration: July 15, 2021
Dates of the Conference: October 25-29, 2021
Link to submit:

More info at:

Research Fellows in Cultural Data Analytics @ Tallinn University | CUDAN Open Lab

Mon, 04/26/2021 - 15:30

Tallinn University seeks to hire two Research Fellows in Cultural Data Analytics, particularly in (1) Audiovisual Machine Learning, and (2) Cultural Dynamics, to work on ambitious, high-impact research at the CUDAN ERA Chair (chair holder Prof. Dr. Maximilian Schich). Start of the employment contract: 01.07.- 01.09.2021, duration of the contract is up to 31.12.2023. Deadline of submitting the application documents is 31st May, 2021.

Read the full article at:

Synchronizing Chaos with Imperfections

Mon, 04/26/2021 - 13:46

Yoshiki Sugitani, Yuanzhao Zhang, and Adilson E. Motter
Phys. Rev. Lett. 126, 164101

Previous research on nonlinear oscillator networks has shown that chaos synchronization is attainable for identical oscillators but deteriorates in the presence of parameter mismatches. Here, we identify regimes for which the opposite occurs and show that oscillator heterogeneity can synchronize chaos for conditions under which identical oscillators cannot. This effect is not limited to small mismatches and is observed for random oscillator heterogeneity on both homogeneous and heterogeneous network structures. The results are demonstrated experimentally using networks of Chua’s oscillators and are further supported by numerical simulations and theoretical analysis. In particular, we propose a general mechanism based on heterogeneity-induced mode mixing that provides insights into the observed phenomenon. Since individual differences are ubiquitous and often unavoidable in real systems, it follows that such imperfections can be an unexpected source of synchronization stability.

Read the full article at:

Misinformation about science in the public sphere

Sun, 04/25/2021 - 10:58

Dietram A. Scheufele, Andrew J. Hoffman, Liz Neeley, and Czerne M. Reid

PNAS April 13, 2021 118 (15) e2104068118

The misinformation crisis exemplified and intensified by the COVID-19 pandemic lays a gauntlet at the door of all science communicators. Scholars, experts, educators, activists, organizers, public servants, and philanthropists share an obligation to engage in “difficult, broad-based negotiation of moral, financial, and other societal trade-offs alongside a collective investigation of scientific potential” (18). In the end, it is our hope that this colloquium issue will stimulate deeper explorations of the causes and cures for misinformation, conducted in closer collaborations among researchers and practitioners.

Read the full article at:

Growth, death, and resource competition in sessile organisms

Sat, 04/24/2021 - 10:54

Edward D. Lee, Christopher P. Kempes, and Geoffrey B. West

PNAS April 13, 2021 118 (15) e2020424118

Although termite mounds stand out as an example of remarkably regular patterns emerging over long times from local interactions, ecological spatial patterns range from regular to random, and temporal patterns range from transient to stable. We propose a minimal quantitative framework to unify this variety by accounting for how quickly sessile organisms grow and die mediated by competition for fluctuating resources. Building on metabolic scaling theory for forests, we reproduce a wide range of spatial patterns and predict transient features such as population shock waves that align with observations. By connecting diverse ecological dynamics, our work will help apply lessons from model systems more broadly (e.g., by leveraging remote mapping to infer local ecological conditions).

Read the full article at:

Graph Metrics for Network Robustness—A Survey

Fri, 04/23/2021 - 15:12

Milena Oehlers and Benjamin Fabian

Mathematics 2021, 9(8), 895

Research on the robustness of networks, and in particular the Internet, has gained critical importance in recent decades because more and more individuals, societies and firms rely on this global network infrastructure for communication, knowledge transfer, business processes and e-commerce. In particular, modeling the structure of the Internet has inspired several novel graph metrics for assessing important topological robustness features of large complex networks. This survey provides a comparative overview of these metrics, presents their strengths and limitations for analyzing the robustness of the Internet topology, and outlines a conceptual tool set in order to facilitate their future adoption by Internet research and practice but also other areas of network science.

Read the full article at:

Urban Informatics

Fri, 04/23/2021 - 11:03

Urban informatics is an interdisciplinary approach to understanding, managing, and designing the city using systematic theories and methods based on new information technologies. Integrating urban science, geomatics, and informatics, urban informatics is a particularly timely way of fusing many interdisciplinary perspectives in studying city systems. This edited book aims to meet the urgent need for works that systematically introduce the principles and technologies of urban informatics. The book gathers over 40 world-leading research teams from a wide range of disciplines, who provide comprehensive reviews of the state of the art and the latest research achievements in their various areas of urban informatics. The book is organized into six parts, respectively covering the conceptual and theoretical basis of urban informatics, urban systems and applications, urban sensing, urban big data infrastructure, urban computing, and prospects for the future of urban informatics. 

Open Access Book at:

How Maxwell’s Demon Continues to Startle Scientists

Fri, 04/23/2021 - 10:50

The thorny thought experiment has been turned into a real experiment — one that physicists use to probe the physics of information.

Read the full article at:

Computational Epidemiology at the time of COVID-19 by Alessandro Vespignani

Thu, 04/22/2021 - 14:50


Colloquium Virtual Complexity at C3-UNAM
Universities for Science Consortium

“Computational Epidemiology at the time of COVID-19”
Alessandro Vespignani
Network Science Institute at Northeastern University

The data science revolution is finally enabling the development of large-scale data-driven models that provide real- or near-real-time forecasts and risk analysis for infectious disease threats. These models also provide rationales and quantitative analysis to support policy-making decisions and intervention plans. At the same time, the non-incremental advance of the field presents a broad range of challenges: algorithmic (multiscale constitutive equations, scalability, parallelization), real-time integration of novel digital data streams (social networks, participatory platform, human mobility etc.). I will review and discuss recent results and challenges in the area, and focus on ongoing work aimed at responding to the COVID-19 pandemic.

Short Bio:
Alessandro Vespignani is the Director of the Network Science Institute and Sternberg Family Distinguished University Professor at Northeastern University. He is a professor with interdisciplinary appointments in the College of Computer and Information Science, College of Science, and the Bouvé College of Health Sciences. Dr. Vespignani’s work focuses on statistical and numerical simulation methods to model spreading phenomena, including the realistic and data-driven computational modeling of biological, social, and technological systems. For several years his work has focused on the spreading of infectious diseases, working closely with the CDC and the WHO.

Watch at:

Melanie Mitchell Takes AI Research Back to Its Roots

Wed, 04/21/2021 - 14:03

To build a general artificial intelligence, we may need to know more about our own minds, argues the computer scientist Melanie Mitchell.

Full episode at: