Key concepts of complexity science, such as nonlinearity, emergence and self-organization all follow from the old adage that the whole is greater than (or different from) the sum of the parts [1]. The fact that we use the word ‘parts’ (and like words such as components, elements, and even ‘agents’) implies that nature may be broken into separate pieces, basic ‘building blocks’ that somehow are brought together to produce coordinated behavior.
The effects of the 2007 financial crisis in the scope of housing access and loss in Spain have been devastating for a great part of the population with consequences still visible nowadays. Data available unveils the ineffectiveness of the approach undertaken by legislators and the judiciary in order to confront the complex situation.
Collective decision-making is ubiquitous when observing the behavior of intelligent agents, including humans. However, there are inconsistencies in our theoretical understanding of whether there is a collective advantage from interacting with group members of varying levels of competence in solving problems of varying complexity. Moreover, most existing experiments have relied on highly stylized tasks, reducing the generality of their results.
Socio-economic segregation shapes the dynamics of the population distribution in urban areas. In this work we extend the idea behind the Schelling’s model, i.e., that segregation emerges as a consequence of homophily and clustering, by including a more realistic mobility model. The graph-based model accounts for two control parameters, mobility μ and preference β, concurring to determine the steady-state solution for the system. As a result segregated regimes are dominant for a wide range of combinations of the control parameters, particularly when μ is small and β > βcrit.
Characterizing species diversity and composition of bacteria hosted by biota is revolutionizing our understanding of the role of symbiotic interactions in ecosystems. Determining microbiomes diversity implies the assignment of individual reads to taxa by comparison to reference databases. Although computational methods aimed at identifying the microbe(s) taxa are available, it is well known that inferences using different methods can vary widely depending on various biases.
After successfully tamping down the first surge of infection and death, Europe is now in the middle of a second coronavirus wave as it moves into winter [1]. We propose a realistic model for the evolution of the COVID-19 pandemic subject to the lockdown and quarantine measures. The dynamic equations for the entire process are derived by adopting a "kinetic-type reactions" approach. More specifically, the lockdown and the quarantine measures are modeled by some kind of inhibitor reactions where susceptible and infected individuals can be “trapped” into inactive states.
We have developed an agent-based modeling framework for simulations of bio-social stochastic processes underlying SARS-CoV-2 epidemics. The individual features of the agents that affect the process at elementary interaction scale incorporate their susceptibility to the virus, which helps us to differentiate between the symptomatic and asymptomatic cases, and the exposure time of each actor, as well as the virus survival time and potential mutations. The process is visualized by a growing bipartite graph of the infected host and viruses that they produce, see Fig.1.
Nanocavity networks in 2D surfaces are responsible for molecular confinement, where molecules are normally trapped in small size caverns. Molecular confinement is responsible for a non-thermodynamic equilibrium local fluctuating–domain of trapped molecules characterised by chaotic behaviour at the boundary of 2D interphase.
Is sound predictable, or computable? What distinguishes an organized or “complex” sound? Although the literature on complex systems is vast, research on the application of complexity measures to sound is notably scarce. Sound certainly has the potential to a “complex approach”, particularly for its double nature as acoustic entity (as studied by physics) and as conscious experience (as studied by cognitive science), which might suggest a “multilevel hypernetwork”.
The Electrocardiogram (ECG) is a record of the electrical activity of the heart which serves as the first step in diagnosis of a cardiac abnormality. To detect and associate specific features linked to diseases is the thrust of research, aimed at the development of automated diagnostic tools. However, most of the existing studies use single-lead ECG data (from a single electrode), and focus only on diseases such as Arrhythmias and Chronic Heart Failure.