Guido Caldarelli, Oriol Artime, Giulia Fischetti, Stefano Guarino, Andrzej Nowak, Fabio Saracco, Petter Holme, Manlio De Domenico

Physics Reports Volume 1186, 5 August 2026, Pages 1-75

The boundaries between physical and social networks have narrowed with the advent of the Internet and its pervasive platforms. This has given rise to a complex adaptive information ecosystem where individuals and machines compete for attention, leading to emergent collective phenomena. The flow of information in this ecosystem is often non-trivial and involves complex user strategies—from the forging or strategic amplification of manipulative content to large-scale coordinated behavior—that trigger misinformation cascades, echo-chamber reinforcement, and opinion polarization. We argue that statistical physics provides a suitable and necessary framework for analyzing the unfolding of these complex dynamics on socio-technological systems. This review systematically covers the foundational and applied aspects of this framework. The review is structured to first establish the theoretical foundation for analyzing these complex systems, examining both structural models of complex networks and physical models of social dynamics (e.g., epidemic and spin models). We then ground these concepts by describing the modern media ecosystem where these dynamics currently unfold, including a comparative analysis of platforms and the challenge of information disorders. The central sections proceed to apply this framework to two central phenomena: first, by analyzing the collective dynamics of information spreading, with a dedicated focus on the models, the main empirical insights, and the unique traits characterizing misinformation; and second, by reviewing current models of opinion dynamics, spanning discrete, continuous, and coevolutionary approaches. In summary, we review both empirical findings based on massive data analytics and theoretical advances, highlighting the valuable insights obtained from physics-based efforts to investigate these phenomena of high societal impact.

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Arthur Montanari, Ana Elisa Barioni, and Adilson Motter

In a murmuration of starlings, abrupt evasive maneuvers from a few birds in response to a passing falcon can trigger a collective response across the whole group. Within a fraction of a second, local turns are amplified through thousands of neighboring interactions between birds, and the entire flock twists and folds as if it were a single organism. During the annual northbound migration of sardines along the coast of South Africa, dense schools rapidly reorganize into spinning bait balls when dolphins approach, using collective geometry to confuse predators and dilute individual risk. On land, herds of millions of wildebeest coordinate traveling direction and timing across open plains and narrow passages during their yearly migration throughout the Serengeti. Desert locusts also march across long distances in the Sahel and Arabian Peninsula, producing vast swarms that move as a unit when tactile stimulation and high population density trigger a phase transition from individualistic to coordinated behavior in the form of rolling waves.

Read the full article at: www.siam.org

Fabio Oddi, Federico Naldini, Leo D’Amato, Grégory Marlière, Paola Pellegrini, Vito Trianni

Recent research is exploring self-organised traffic management as a solution for scaling to complex real-world networks. In such a system, trains predict their neighbourhood, produce traffic plan hypotheses, and agree via consensus with neighbours on a future traffic plan to be implemented. This paper investigates a structural parameter within this pipeline: the predictive neighbourhood horizon. The horizon is used by trains to identify future potential conflicts with neighbours, and to establish the local interaction topology, that is, the subset of trains to negotiate with. As the primary design variable, the horizon directly determines the size and density of the social interaction graph, whereas its impact on the complexity of local sub-problems and the distributed consensus dynamics represents a trade-off to be explored. Through a closed-loop simulation framework the study evaluates how variations of the horizon impact the overall decentralised coordination process, from initial conflict detection to distributed schedule consensus. The analysis focuses on investigating the potential trade-off introduced by the horizon choice: balancing local tractability and computational responsiveness with the need for global schedule coherence and feasibility in safety-critical environments. Contrary to intuition, our empirical results indicate that the short time horizons suffice, while long values compromise local tractability and computational responsiveness with no gain in global schedule optimality.

Read the full article at: arxiv.org

Melanie Mitchell

A new term has been coined to describe AI in its current form: “jagged intelligence.” The term captures the fact that the landscape of AI capabilities is profoundly uneven: the tools demonstrate excellent abilities on certain problems but surprising failures on other similar problems. For humans, one kind of skill can often predict abilities at similar skills; this is not the case in the jagged landscape of AI. Last fall, Ilya Sutskever, a cofounder of OpenAI, argued that there are no easy fixes to this problem: “These models somehow just generalize dramatically worse than people. It’s a very fundamental thing.”

Read the full article at: yalereview.org