Networking the complexity community since 1999
Binghamton University’s Systems Science and Industrial Engineering Department is hiring to fill as many as six new faculty positions for Fall 2023! Areas relevant to complexity include:
* Systems Science/ Complex Systems/ Network Science (1 position)
* Computational Social Science/Biomedical Complexity/Epidemiology (1 position)
* Autonomous Systems (1 position)
* Energy Systems (1 position)
Please contact Hiroki Sayama (sayama@binghamton.edu) if interested.
More at: www.binghamton.edu
Struggling with change: The fragile resilience of collectives
Frank Schweitzer, Christian Zingg, Giona Casiraghi
Collectives form non-equilibrium social structures characterised by a volatile dynamics. Individuals join or leave. Social relations change quickly. Therefore, differently from engineered or ecological systems, a resilient reference state cannot be defined. We propose a novel resilience measure combining two dimensions: robustness and adaptivity. We demonstrate how they can be quantified using data from a software developer collective. Our analysis reveals a resilience life cycle, i.e., stages of increasing resilience are followed by stages of decreasing resilience. We explain the reasons for these observed dynamics and provide a formal model to reproduce them. The resilience life cycle allows distinguishing between short-term resilience, given by a sequence of resilient states, and long-term resilience, which requires collectives to survive through different cycles.
Read the full article at: arxiv.org
Many real systems can be modeled as networks, where the elements of the system are nodes and interactions between elements are edges. An even larger set of systems can be modeled using dynamical processes on networks, which are in turn affected by the dynamics. Networks thus represent the backbone of many complex systems, and their theoretical and computational analysis makes it possible to gain insights into numerous applications. Networks permeate almost every conceivable discipline —including sociology, transportation, economics and finance, biology, and myriad others — and the study of “network science” has thus become a crucial component of modern scientific education.
The school “Complex Networks: Theory, Methods, and Applications” offers a succinct education in network science. It is open to all aspiring scholars in any area of science or engineering who wish to study networks of any kind (whether theoretical or applied), and it is especially addressed to doctoral students and young postdoctoral scholars. The aim of the school is to deepen into both theoretical developments and applications in targeted fields.
Read the full article at: ntmg.lakecomoschool.org
Learn how the tools that enabled us to overcome the uncertainty of the weather will enable us to find new answers to modern science’s most pressing questions.
Why does your weather app say “There’s a 10% chance of rain” instead of “It will be sunny tomorrow”? In large part this is due to the insight of Tim Palmer, who made uncertainty essential to the study of weather and climate. Now he wants to apply it to how we study everything else.
In The Primacy of Doubt, Palmer argues that embracing the mathematics of uncertainty is vital to understanding ourselves and the universe around us. Whether we want to predict climate change or market crashes, understand how the brain is able to outpace supercomputers, or find a theory that links quantum and cosmological physics, Palmer shows how his vision of mathematical uncertainty provides new insights into some of the deepest problems in science. The result is a revolution—one that shows that power begins by embracing what we don’t know.
More at: www.basicbooks.com
Francesca Bertacchini, Carmelo Scuro, Pietro Pantano & Eleonora Bilotta
Scientific Reports volume 12, Article number: 16543 (2022)
Understanding the relationship between brain architecture and brain function is a central issue in neuroscience. We modeled realistic spatio-temporal patterns of brain activity on a human connectome with a Boolean networks model with the aim of computationally replicating certain cognitive functions as they emerge from the standardization of many fMRI studies, identified as patterns of human brain activity. Results from the analysis of simulation data, carried out for different parameters and initial conditions identified many possible paths in the space of parameters of these network models, with normal (ordered asymptotically constant patterns), chaotic (oscillating or disordered) but also highly organized configurations, with countless spatial–temporal patterns. We interpreted these results as routes to chaos, permanence of the systems in regimes of complexity, and ordered stationary behavior, associating these dynamics to cognitive processes. The most important result of this work is the study of emergent neural circuits, i.e., configurations of areas that synchronize over time, both locally and globally, determining the emergence of computational analogues of cognitive processes, which may or may not be similar to the functioning of biological brain. Furthermore, results put in evidence the creation of how the brain creates structures of remote communication. These structures have hierarchical organization, where each level allows for the emergence of brain organizations which behave at the next superior level. Taken together these results allow the interplay of dynamical and topological roots of the multifaceted brain dynamics to be understood.
Read the full article at: www.nature.com
