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.

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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).

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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

Marco Pangallo, Alberto Aleta, R. Maria del Rio-Chanona, Anton Pichler, David Martín-Corral, Matteo Chinazzi, François Lafond, Marco Ajelli, Esteban Moro, Yamir Moreno, Alessandro Vespignani & J. Doyne Farmer
Nature Human Behaviour (2023)

Despite the global impact of the coronavirus disease 2019 pandemic, the question of whether mandated interventions have similar economic and public health effects as spontaneous behavioural change remains unresolved. Addressing this question, and understanding differential effects across socioeconomic groups, requires building quantitative and fine-grained mechanistic models. Here we introduce a data-driven, granular, agent-based model that simulates epidemic and economic outcomes across industries, occupations and income levels. We validate the model by reproducing key outcomes of the first wave of coronavirus disease 2019 in the New York metropolitan area. The key mechanism coupling the epidemic and economic modules is the reduction in consumption due to fear of infection. In counterfactual experiments, we show that a similar trade-off between epidemic and economic outcomes exists both when individuals change their behaviour due to fear of infection and when non-pharmaceutical interventions are imposed. Low-income workers, who perform in-person occupations in customer-facing industries, face the strongest trade-off.

Read the full article at: www.nature.com