Kyu Hyong Park, Felipe Xavier Costa, Luis M. Rocha, Réka Albert, and Jordan C. Rozum
PRX Life 1, 023009

Complex living systems are thought to exist at the “edge of chaos” separating the ordered dynamics of robust function from the disordered dynamics of rapid environmental adaptation. Here, a deeper inspection of 72 experimentally supported discrete dynamical models of cell processes reveals previously unobserved order on long time scales, suggesting greater rigidity in these systems than was previously conjectured. We find that propagation of internal perturbations is transient in most cases, and that even when large perturbation cascades persist, their phenotypic effects are often minimal. Moreover, we find evidence that stochasticity and desynchronization can lead to increased recovery from regulatory perturbation cascades. Our analysis relies on new measures that quantify the tendency of perturbations to spread through a discrete dynamical system. Computing these measures was not feasible using current methodology; thus, we developed a multipurpose CUDA-based simulation tool, which we have made available as the open-source Python library cubewalkers. Based on novel measures and simulations, our results suggest that—contrary to current theory—cell processes are ordered and far from the edge of chaos.

Read the full article at: link.aps.org

Arturo González, et al.

2023 XLIX Latin American Computer Conference (CLEI)

International Economic Integration entails collaborative efforts among nations to overcome barriers and achieve shared benefits. The development of analytical models is crucial for comprehending the interaction of countries as a collective entity. In South America, MERCOSUR stands out, comprising Argentina, Brazil, Paraguay, and Uruguay. In this study, Economic Complexity metrics were applied to analyze productive capacities within MERCOSUR. The results underscore integration’s significance by forming economic blocs, capabilities are expanded, enriching diversity and the scope of production. Economic complexity serves as a pivotal tool for assessing interdependence and enhancing countries’ global positioning. In summary, this work highlights how economic integration, exemplified by MERCOSUR, enhances productive capacity, propelling development, and competitiveness in an interconnected world. Leveraging a novel computational mathematical model, this study offers insights into the complex dynamics of international economic integration, shedding light on strategies to foster growth and collaboration in a rapidly evolving global landscape.

Read the full article at: ieeexplore.ieee.org

Michael L. Wong, et al.

The universe is replete with complex evolving systems, but the existing macroscopic physical laws do not seem to adequately describe these systems. Recognizing that the identification of conceptual equivalencies among disparate phenomena were foundational to developing previous laws of nature, we approach a potential “missing law” by looking for equivalencies among evolving systems. We suggest that all evolving systems—including but not limited to life—are composed of diverse components that can combine into configurational states that are then selected for or against based on function. We then identify the fundamental sources of selection—static persistence, dynamic persistence, and novelty generation—and propose a time-asymmetric law that states that the functional information of a system will increase over time when subjected to selection for function(s).

Read the full article at: www.pnas.org

Giordano De Marzo, Luciano Pietronero, David Garcia

Scale-free networks are one of the most famous examples of emergent behavior and are ubiquitous in social systems, especially online social media in which users can follow each other. By analyzing the interactions of multiple generative agents using GPT3.5-turbo as a language model, we demonstrate their ability to not only mimic individual human linguistic behavior but also exhibit collective phenomena intrinsic to human societies, in particular the emergence of scale-free networks. We discovered that this process is disrupted by a skewed token prior distribution of GPT3.5-turbo, which can lead to networks with extreme centralization as a kind of alignment. We show how renaming agents removes these token priors and allows the model to generate a range of networks from random networks to more realistic scale-free networks.

Read the full article at: arxiv.org