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Fragmentation, echo chambers, and their amelioration in social networks have been a growing concern in the academic and non-academic world. This paper shows how, under the assumption of homophily, echo chambers and fragmentation are system-immanent phenomena of highly flexible social networks, even under ideal conditions for heterogeneity. We achieve this by finding an analytical, network-based solution to the Schelling model and by proving that weak ties do not hinder the process.

In this research, we investigate the evolution of cooperation in a complex system, namely the healthcare system in England, which is made up of populations consisting of different healthcare providers interacting with patients. Our investigation examines individual agents’ behaviors as viewed by an external policymaker, in this case, the Health Department. Generally, policies are initiated and managed by the Health Department, which allocates a specific budget to interfere. To this end, our analysis here is carried out based on a baseline model we developed in [1].

Cities have become the economic bedrock of modern nations and this transition will likely be continued in the coming years as an estimated three billion people will move into cities by 2030. Nevertheless, while urbanisation can entail economic dynamism and social development, it can also create enormous social challenges. The management of natural hazards and pollution, the exclusion of the poor from the city’s socioeconomic fabric and the subsequent surge of social and economic inequalities have become some of the pressing issues that modern metropolises need to address.

One of the most concerning aspects of the ongoing COVID-19 pandemics is that it disproportionately affects people from Black and African American backgrounds, creating a so-called "COVID-19 infection gap", i.e., a marked difference between the percentage of African Americans citizens in a community and the percentage of African Americans infected by (or died of) COVID-19. The abnormal impact of COVID-19 on these ethnic groups seem to be almost uncorrelated with other risk factors, including co-morbidity, poverty, level of education, access to healthcare and response to cures.

Starting in the late 20th century, the Brazilian federal government created several programs to increase the access to water and sanitation. However, although these programs made improvements in water access, sanitation was generally overlooked. While water supply, and waste collection are available in the majority of the Brazilian municipalities, the sewage system is still spatially concentrated in the Southeast region and in the most urbanized areas. The Southeast region has roughly 42% of Brazilian population and includes the cities such as Rio de Janeiro and São Paulo.

Online searches have been used as a tool for close to real-time study of different health-related behaviours, including identifying disease outbreaks. However, many models have been criticized for ignoring whether such activity is related to an actual disease. Here we propose a methodology to disentangle online search behaviours that are driven by actual disease from others that can be caused by other motives, including media-driven curiosity or information seeking. In particular, we are taking advantage of the current and past pandemics to identify different search-patterns.

A central paradigm in modeling opinion dynamics is based on the notion of “information diffusion” - the idea that opinions spread like viruses through social networks, diffusing between individuals, communities, and countries. Empirical cases of opinion spread on social networks, such as diverging stances towards vaccines or climate change, cannot be fully explained by these social contagion processes alone. In a model proposed by Goldberg & Stein [1], agents observe each other’s behaviors, but this does not directly lead to simple or complex contagion.

Social and political turbulence in the United States and a number of European countries has been rising in recent years. My research, which combines analysis of historical data with the tools of complexity science, has identified the deep structural forces that work to undermine societal stability and resilience to internal and external shocks.

A framework for studying the vulnerability and the recovery of networks and interdependent networks will be presented. In interdependent networks, such as infrastructures, when nodes in one network fail, they cause dependent nodes in other networks to also fail. This may happen recursively and can lead to a cascade of failures and to a sudden fragmentation of the system. I will present analytical solutions based on percolation theory, for the critical thresholds and the giant component of a network of n interdependent networks.

There will be a session with short introductions by members of the Editorial Board of Journals of the European Physical Society, and the Complexity Journal, and PCI Network Science. Everyone is invited to attend and ask questions on what is the best approach to publish successfully. The session is intended for young scientists to have a first-hands exposure face-toface with the editors. Additionally, the editors always ask for feedback from the community. Here is a chance to have both these in this session.