Open Call – Conference Complex Systems (CCS 2023)

The Complex Systems Society (CSS) organizes every year a main conference (CCS) – the most important annual meeting for the complex systems research community.

The Complex Systems Society invites bids to host CCS2023.

The conference is generally held in September/October of each year.

The deadline for proposals submission is March 15, 2022.

Read the full article at: cssociety.org

Mediterranean School of Complex Networks 2022

we are calling for applications from students and young researchers in Network Science for the 7th edition of the Mediterranean School of Complex Networks, which will take place in Salina (Italy), 25 Jun – 02 Jul 2022.

From 2022 the Mediterranean School of Complex Networks and the International School of Informatics and Dynamics in Complex Networks have joined their forces into a single event.
The new board of directors include, in alphabetic order: Prof. Alex Arenas (URV, Spain), Prof. Vincenza Carchiolo (UniCT, Italy), Prof. Manlio De Domenico (UniPD, Italy), Prof. Mattia Frasca (UniCT, Italy) and Prof. Giuseppe Mangioni (UniCT, Italy)

Early applications are expected before 15 March 2022 (no payment required at this step). Seats are limited to 30 in-person attendants and 70 online attendants, due to COVID19 restrictions.

Since its first edition in 2014, our School trained more than 230 early-career researchers in Network Science from 4 continents. All details about previous editions, location, important dates and travel are available at the official website: http://mediterraneanschoolcomplex.net/
You might also want to watch the School teaser: https://www.youtube.com/watch?v=dR1apC8-HZY

Please, note that for the youngest researchers (no more than two years from their PhD completion) who are members of the Complex Systems Society, we will grant up to two scholarships covering the registration fee. Prizes are kindly sponsored by the MDPI journal Condensed Matter

More at: mediterraneanschoolcomplex.net

Nanowars can cause epidemic resurgence and fail to promote cooperation

Dirk Helbing, Matjaž Perc
In a non-sustainable, “over-populated” world, what might the use of nanotechnology-based targeted, autonomous weapons mean for the future of humanity? In order to gain some insights, we make a simplified game-theoretical thought experiment. We consider a population where agents play the public goods game, and where in parallel an epidemic unfolds. Agents that are infected defectors are killed with a certain probability and replaced by susceptible cooperators. We show that such “nanowars”, even if aiming to promote good behavior and planetary health, fail not only to promote cooperation, but they also significantly increase the probability of repetitive epidemic waves. In fact, newborn cooperators turn out to be easy targets for defectors in their neighborhood. Therefore, counterintuitively, the discussed intervention may even have the opposite effect as desired, promoting defection. We also find a critical threshold for the death rate of infected defectors, beyond which resurgent epidemic waves become a certainty. In conclusion, we urgently call for international regulation of nanotechnology and autonomous weapons.

Read the full article at: arxiv.org

Functional observability and target state estimation in large-scale networks

Arthur N. Montanari, Chao Duan, Luis A. Aguirre, and Adilson E. Motter
PNAS January 4, 2022 119 (1) e2113750119;

Observing the states of a network is fundamental to our ability to explore and control the dynamics of complex natural, social, and technological systems. The problem of determining whether the system is observable has been addressed by network control researchers over the past decade. Progress on the further problem of actually designing and implementing efficient algorithms to infer the states from limited measurements has been hampered by the high dimensionality of large-scale networks. Noting that often only a small number of state variables in a network are essential for control, intervention, and monitoring purposes, this work develops a graph-based theory and highly scalable methods that achieve accurate estimation of target variables of network systems with minimal sensing and computational resources.

Read the full article at: www.pnas.org