Networking the complexity community since 1999
Author: cxdig
We see consciousness in AI the same way we see faces in clouds, says neuroscientist Anil Seth. He explores the all-too-human tendency to project inner life onto machines that are brilliant mimics, not sentient beings, and gives a definitive answer to the urgent question: Will AI ever gain consciousness?
Watch at: www.youtube.com
C. Marchuk, L. Ríos, A. González, S. González, G. Pereira and C. von Lücken, “A Computational Economic Complexity Model for Regional Economic Integration: Analysis of the EU, MERCOSUR, URUPABOL, and the AndeanCommunity,” 2025 IEEE CHILEAN Conference on Electrical, Electronics Engineering, Information and Communication Technologies (CHILECON), Valparaíso, Chile, 2025, pp. 1-8, doi: 10.1109/CHILECON66915.2025.11476476.
Regional Economic Integration is a process by which countries seek mutual benefits through the reduction of trade, social, and political barriers. This paper introduces a computational mathematical model grounded in Economic Complexity Theory to analyze economic blocs as unified entities. Four case studies are examined: the European Union, MERCOSUR, URUPABOL, and the Andean Community. Using real export data and complexity metrics, we identify the combined productive capacities of member countries. Results reveal that integration enhances product diversity and increases the ubiquity of exports within the bloc. The study demonstrates that regional integration boosts development and strengthens competitiveness in the global economy. The proposed methodological approach provides a novel tool for regional analysis and serves as a foundation for future strategies in economic cooperation and productive planning. This research contributes to understanding how collective capabilities can generate synergies that exceed individual national potentials, particularly in the context of Latin American regional development.
Read the full article at: ieeexplore.ieee.org
Mario Franco & Carlos Gershenson
Front. Artif. Intell., Volume 9 – 2026
Nowadays, neural networks act as a synonym for artificial intelligence. Present neural network models, although remarkably powerful, are inefficient both in terms of data and energy. Several alternative forms of neural networks have been proposed to address some of these problems. Specifically, spiking neural networks are suitable for efficient hardware implementations. However, effective learning algorithms for spiking networks remain elusive, although it is suspected that effective plasticity mechanisms could alleviate the problem of data efficiency. Here, we present a new framework for spiking neural networks—Spark (https://github.com/Nogarx/Spark)—built upon the idea of modular design, from simple components to entire models. The aim of this framework is to provide an efficient and streamlined pipeline for spiking neural networks. We showcase this framework by solving the sparse-reward cartpole problem with simple plasticity mechanisms. We hope that a framework compatible with traditional ML pipelines may accelerate research in the area, specifically for continuous and unbatched learning, akin to the one animals exhibit
Read the full article at: www.frontiersin.org
The Summer School in Complexity and Emergence in Economic and Social Systems is inspired by the pioneering approach of the Santa Fe Institute. This program introduces participants to the tools and ideas central to the study of complex adaptive systems.
Through a combination of lectures, hands-on coding sessions, and interdisciplinary discussions, participants will explore how emergent phenomena—such as financial crises, innovation diffusion, urban growth, and collective decision-making—arise from decentralized interactions among agents.
Many of today’s most pressing social and economic challenges exhibit emergent properties that traditional equilibrium-based methods struggle to explain.
This Summer School addresses that gap by equipping participants with state-of-the-art tools from complexity science, enabling them to analyze systems where collective behavior and adaptation drive outcomes.
Enroll at: bse.eu
The universe creatively sets the rules for its own becoming.
Stuart Kauffman is a theoretical biologist and leading complexity scientist who has argued that the self-organization of organisms is as influential in evolution as natural selection. His seminal book on the subject is “The Origins of Order: Self-Organization and Natural Selection in Evolution” (1993). He spoke recently with Noema Editor-in-Chief Nathan Gardels.
Read the full article at: www.noemamag.com
