We demonstrate that the assembly pathway method underlying assembly theory (AT) is an encoding scheme widely used by popular statistical compression algorithms. We show that in all cases (synthetic or natural) AT performs similarly to other simple coding schemes and underperforms compared to system-related indexes based upon algorithmic probability that take into account statistical repetitions but also the likelihood of other computable patterns. Our results imply that the assembly index does not offer substantial improvements over existing methods, including traditional statistical ones, and imply that the separation between living and non-living compounds following these methods has been reported before.

Abicumaran Uthamacumaran, Felipe S. Abrahão, Narsis A. Kiani & Hector Zenil
npj Systems Biology and Applications volume 10, Article number: 82 (2024)

Read the full article at: www.nature.com

Andy Clark

Entropy 2024, 26(8), 677

According to active inference, constantly running prediction engines in our brain play a large role in delivering all human experience. These predictions help deliver everything we see, hear, touch, and feel. In this paper, I pursue one apparent consequence of this increasingly well-supported view. Given the constant influence of hidden predictions on human experience, can we leverage the power of prediction in the service of human flourishing? Can we learn to hack our own predictive regimes in ways that better serve our needs and purposes? Asking this question rapidly reveals a landscape that is at once familiar and new. It is also challenging, suggesting important questions about scope and dangers while casting further doubt (as if any was needed) on old assumptions about a firm mind/body divide. I review a range of possible hacks, starting with the careful use of placebos, moving on to look at chronic pain and functional disorders, and ending with some speculations concerning the complex role of genetic influences on the predictive brain.

Read the full article at: www.mdpi.com

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.

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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