Dirk Helbing & Sachit Mahajan 

We critically examine the evolving functionality and challenges of democracies in the age of digital transformation and artificial intelligence (AI). Contrary to notions of democracy as a static governance form, we emphasize the importance of its adaptability, but find that recent technological and institutional shifts have undermined foundational mechanisms such as decentralized decision-making, transparent information flows, and effective self-correction. Drawing from complexity science, political theory, participatory research and computational social science, we analyze how algorithmic control, surveillance capitalism, and power asymmetries have affected core democratic principles. We pay specific attention to structural changes in political representation, civic participation, and how these have affected public trust. We further discuss a set of recent, digitally assisted approaches, ranging from deliberative platforms and participatory budgeting to fair voting systems and co-creation, which can potentially restore the legitimacy of democratic systems and their resilience. By understanding democracies as dynamic, co-evolving systems, we highlight the potential of plu-ralistic design. Aligning technological progress with constitutional principles can meaningfully repair, revive and updgrade democratic systems and institutions.

Read the full article at: www.researchgate.net

Felix Effenberger, Pedro Carvalho, Igor Dubinin, and Wolf Singer

PNAS 122 (4) e2412830122

Neocortical circuits are characterized by complex oscillatory dynamics. Whether these oscillations serve computations or are an epiphenomenon is still debated. To answer this question, we designed a computational model of a recurrent network that allows control of oscillatory dynamics (harmonic oscillator recurrent network, HORN). When operating in an oscillatory regime, HORNs outperform nonoscillatory recurrent networks in terms of learning speed, noise tolerance, and parameter efficiency. Moreover, they closely replicate the dynamics of neuronal systems, suggesting that biological neural networks are likely to also exploit the unique properties offered by oscillatory dynamics for computing. The interference patterns provided by wave-based responses allow for a holistic representation and highly parallel encoding of both spatial and temporal relations among stimulus features.

Read the full article at: www.pnas.org

Elisa C. Baek, Ryan Hyon, Karina López, Mason A. Porter & Carolyn Parkinson 

Nature Communications volume 16, Article number: 5864 (2025)

It has been proposed that information sharing, which is a ubiquitous and consequential behavior, plays a critical role in cultivating and maintaining a sense of shared reality. Across three studies, we test this theory by investigating whether or not people are especially likely to share information that they believe will be interpreted similarly by others in their social circles. Using neuroimaging data collected while people who live in the same residential community viewed brief film clips, we find that more similar neural responses across participants is associated with a greater likelihood to share content. We then test this relationship using two behavioral studies and find (1) that people are particularly likely to share content that they believe others in their social circles will interpret similarly and (2) that perceived similarity with others leads to increased sharing likelihood. In concert, our findings support the idea that people are driven to share information to create and reinforce shared understanding, which is critical to social connection.

Read the full article at: www.nature.com

Ulrik Brandes, Hadi Sotudeh, Doğan Parlak, Paolo Laffranchi & Mert Erkul
npj Complexity volume 2, Article number: 25 (2025)

We propose shape graphs as instantaneous representations of spatial arrangements in association football (soccer). Shape graphs are a novel type of subgraph of Delaunay triangulations inspired by related applications in fingerprinting and facial expression detection. They provide a foundational data structure that supports various downstream tasks in a manner that is flexible, explainable, and efficient. While previous approaches aggregate spatial positions over periods of time to stabilize the underlying signal, we instead interpret each frame individually for increased explainability at the highest possible temporal resolution. As an example use case we introduce position plots, a novel visualization capturing the characteristic fluidity of relative positioning during a game while retaining the possibility to add match context.

Read the full article at: www.nature.com

Rebecca Carstens, Raj Deshpande, Pau Esteve, Nicolò Fidelibus, Sara Linde Neven, Ramona Ottow, Lokamruth K. R., Paula Rodríguez-Sánchez, Luca Santagata, Javier M. Buldú, Brennan Klein, Maddalena Torricelli

Elite football is believed to have evolved in recent years, but systematic evidence for the pace and form of that change is sparse. Drawing on event-level records for 13,067 matches in ten top-tier men’s and women’s leagues in England, Spain, Germany, Italy, and the United States (2020-2025), we quantify match dynamics with two views: conventional performance statistics and pitch-passing networks that track ball movement among a grid of pitch (field) regions. Between 2020 and 2025, average passing volume, pass accuracy, and the percent of passes made under pressure all rose. In general, the largest year-on-year changes occurred in women’s competitions. Network measures offer alternative but complementary perspectives on the changing gameplay in recent years, normalized outreach in the pitch passing networks decreased, while the average shortest path lengths increased, indicating a wider ball circulation. Together, these indicators point to a sustained intensification of collective play across contemporary professional football.

Read the full article at: arxiv.org