Will Deter

Northeast Journal of Complex Systems (NEJCS): Vol. 6 : No. 1 , Article 4.

This work presents an innovative approach to understanding and measuring complexity in network models. We revisit several classic characterizations of complexity and propose a novel measure that represents complexity as an interactive process. This measure incorporates transfer entropy and Jensen-Shannon divergence to quantify both the information transfer within a system and the dynamism of its constituents’ state changes. To validate our measure, we apply it to several well-known simulation models implemented in Python, including: two models of residential segregation, Conway’s Game of Life, and the Susceptible-Infected-Susceptible (SIS) model. Our results reveal varied trajectories of complexity, demonstrating the efficacy and sensitivity of our measure in capturing the nuanced interplay of interactivity and dynamism in different systems. The results corroborate the notion that heterogeneity and stochasticity increase system complexity. This study contributes to the field by proposing a measure that not only quantifies the amount of complexity present in a system but also emphasizes the process of “complexing,“ marking a semantic shift from viewing complexity solely as an attribute or condition. Our findings underscore the significance of considering both interactivity and dynamism in defining and measuring complexity. The study also acknowledges limitations related to computational resources and the simplification of transfer entropy calculations, setting a clear path for future research in refining and expanding this measure of complexity.

Read the full article at: orb.binghamton.edu

Timothy Davey

Entropy 2024, 26(8), 683

Complex systems confound the typical scientific process. The non-linear relationships mean they can not being broken into smaller, more manageable parts. They also are highly sensitivity to initial conditions, making reproducibility a challenge. Both meaning it crucial we are able to easily identify when a system is acting as complex. Here we propose an information theory based measure which quantifies the uncertainty of any ensemble model arising from complex dynamics. We first compare this measure (named incoherence) to commonly used statistical tests across both continuous and discrete data. Then, we briefly investigate how incoherence can be used to quantify key characteristics of complexity such as criticality and perturbation.

Read the full article at: www.mdpi.com

Brennan Klein, Harrison Hartle, Munik Shrestha, Ana Cecilia Zenteno, David Barros Sierra Cordera, José R Nicolás-Carlock, Ana I Bento, Benjamin M Althouse, Bernardo Gutierrez, Marina Escalera-Zamudio, Arturo Reyes-Sandoval, Oliver G Pybus, Alessandro Vespignani, José Alberto Díaz Quiñonez, Samuel V Scarpino, Moritz U G Kraemer
PNAS Nexus, pgae306

During outbreaks of emerging infectious diseases, internationally connected cities often experience large and early outbreaks, while rural regions follow after some delay. This hierarchical structure of disease spread is influenced primarily by the multiscale structure of human mobility. However, during the COVID-19 epidemic, public health responses typically did not take into consideration the explicit spatial structure of human mobility when designing non-pharmaceutical interventions (NPIs). NPIs were applied primarily at national or regional scales. Here we use weekly anonymized and aggregated human mobility data and spatially highly resolved data on COVID-19 cases at the municipality level in Mexico to investigate how behavioural changes in response to the pandemic have altered the spatial scales of transmission and interventions during its first wave (March – June 2020). We find that the epidemic dynamics in Mexico were initially driven by SARS-CoV-2 exports from Mexico State and Mexico City, where early outbreaks occurred. The mobility network shifted after the implementation of interventions in late March 2020, and the mobility network communities became more disjointed while epidemics in these communities became increasingly synchronised. Our results provide dynamic insights into how to use network science and epidemiological modelling to inform the spatial scale at which interventions are most impactful in mitigating the spread of COVID-19 and infectious diseases in general.

Read the full article at: academic.oup.com

Bao Tran Truong, Xiaodan Lou, Alessandro Flammini, Filippo Menczer

PNAS Nexus, Volume 3, Issue 7, July 2024, pgae258,

Social media, seen by some as the modern public square, is vulnerable to manipulation. By controlling inauthentic accounts impersonating humans, malicious actors can amplify disinformation within target communities. The consequences of such operations are difficult to evaluate due to the challenges posed by collecting data and carrying out ethical experiments that would influence online communities. Here we use a social media model that simulates information diffusion in an empirical network to quantify the impacts of adversarial manipulation tactics on the quality of content. We find that the presence of hub accounts, a hallmark of social media, exacerbates the vulnerabilities of online communities to manipulation. Among the explored tactics that bad actors can employ, infiltrating a community is the most likely to make low-quality content go viral. Such harm can be further compounded by inauthentic agents flooding the network with low-quality, yet appealing content, but is mitigated when bad actors focus on specific targets, such as influential or vulnerable individuals. These insights suggest countermeasures that platforms could employ to increase the resilience of social media users to manipulation.

Read the full article at: academic.oup.com