Debates about artificial intelligence (AI) tend to revolve around whether large models are intelligent, autonomous agents. Some AI researchers and commentators speculate that we are on the cusp of creating agents with artificial general intelligence (AGI), a prospect anticipated with both elation and anxiety. There have also been extensive conversations about cultural and social consequences of large models, orbiting around two foci: immediate effects of these systems as they are currently used, and hypothetical futures when these systems turn into AGI agents—perhaps even superintelligent AGI agents. But this discourse about large models as intelligent agents is fundamentally misconceived. Combining ideas from social and behavioral sciences with computer science can help us to understand AI systems more accurately. Large models should not be viewed primarily as intelligent agents but as a new kind of cultural and social technology, allowing humans to take advantage of information other humans have accumulated.

HENRY FARRELL, ALISON GOPNIK, COSMA SHALIZI, AND JAMES EVANS Authors Info & Affiliations
SCIENCE 13 Mar 2025 Vol 387, Issue 6739

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Arthur N. Montanari, Ana Elisa D. Barioni, Chao Duan, Adilson E. Motter

The study of flocking in biological systems has identified conditions for self-organized collective behavior, inspiring the development of decentralized strategies to coordinate the dynamics of swarms of drones and other autonomous vehicles. Previous research has focused primarily on the role of the time-varying interaction network among agents while assuming that the agents themselves are identical or nearly identical. Here, we depart from this conventional assumption to investigate how inter-individual differences between agents affect the stability and convergence in flocking dynamics. We show that flocks of agents with optimally assigned heterogeneous parameters significantly outperform their homogeneous counterparts, achieving 20-40% faster convergence to desired formations across various control tasks. These tasks include target tracking, flock formation, and obstacle maneuvering. In systems with communication delays, heterogeneity can enable convergence even when flocking is unstable for identical agents. Our results challenge existing paradigms in multi-agent control and establish system disorder as an adaptive, distributed mechanism to promote collective behavior in flocking dynamics.

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Ricard Solé, Manlio De Domenico

The path toward the emergence of life in our biosphere involved several key events allowing for the persistence, reproduction and evolution of molecular systems. All these processes took place in a given environmental context and required both molecular diversity and the right non-equilibrium conditions to sustain and favour complex self-sustaining molecular networks capable of evolving by natural selection. Life is a process that departs from non-life in several ways and cannot be reduced to standard chemical reactions. Moreover, achieving higher levels of complexity required the emergence of novelties. How did that happen? Here, we review different case studies associated with the early origins of life in terms of phase transitions and bifurcations, using symmetry breaking and percolation as two central components. We discuss simple models that allow for understanding key steps regarding life origins, such as molecular chirality, the transition to the first replicators and cooperators, the problem of error thresholds and information loss, and the potential for “order for free” as the basis for the emergence of life.

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Claudia Westermann

Constructivist Foundations 20(2): 67–71

Context: In 2024, we celebrated the 60th-anniversary meeting of the American Society for Cybernetics (ASC. In more than eighty, mostly participatory, sessions, the conference stretched over five days. Under the overarching theme “Living Cybernetics Playing Language,” the conference encouraged participants to reflect on cybernetics in everyday contexts, ranging from academic research to the building of communities. This special issue of Constructivist Foundations contains four target articles that emerged from this conference, and their related discussions. Problem: Sixty years after the foundation of the ASC, defining cybernetics is still a challenge. Diversity, one could say, has haunted cybernetics since its inception. There are many practices that refer to cybernetics in many disciplinary fields and contexts, but do these different practices share anything or do they rely on different aspects of (historical) cybernetic practices? Method: I present the contributions to this special issue as case studies of cybernetic practice and diversity and expose them to the questions mentioned above. Results: Cybernetic practices are as diverse in their methods as the disciplines to which they relate. And yet, as the study of the four target articles and the related commentaries show, these practices all embrace uncertainty. This embrace is the foundation for a particular technicity in which formation and reflexivity become intertwined and co-evolve. In its engagement with contemporary challenges, cybernetic technicity introduces recursive links setting relations across boundaries. Contemporary cybernetic practice, through varied approaches, is a living tradition of enacting open futures. Implications: Cybernetic thinking does not necessarily become detectable through a common vocabulary or set of references, but rather through a particular inherent logic, which links thinking and doing in a recursive co-evolving relationship. Constructivist content: The editorial discusses second-order approaches to cybernetics.

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Thomas F. Varley, Pedro A. M. Mediano, Alice Patania, Josh Bongard

The study of irreducible higher-order interactions has become a core topic of study in complex systems. Two of the most well-developed frameworks, topological data analysis and multivariate information theory, aim to provide formal tools for identifying higher-order interactions in empirical data. Despite similar aims, however, these two approaches are built on markedly different mathematical foundations and have been developed largely in parallel. In this study, we present a head-to-head comparison of topological data analysis and information-theoretic approaches to describing higher-order interactions in multivariate data; with the aim of assessing the similarities and differences between how the frameworks define “higher-order structures.” We begin with toy examples with known topologies, before turning to naturalistic data: fMRI signals collected from the human brain. We find that intrinsic, higher-order synergistic information is associated with three-dimensional cavities in a point cloud: shapes such as spheres are synergy-dominated. In fMRI data, we find strong correlations between synergistic information and both the number and size of three-dimensional cavities. Furthermore, we find that dimensionality reduction techniques such as PCA preferentially represent higher-order redundancies, and largely fail to preserve both higher-order information and topological structure, suggesting that common manifold-based approaches to studying high-dimensional data are systematically failing to identify important features of the data. These results point towards the possibility of developing a rich theory of higher-order interactions that spans topological and information-theoretic approaches while simultaneously highlighting the profound limitations of more conventional methods.

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