Dino Pedreschi, Luca Pappalardo, Emanuele Ferragina, Ricardo Baeza-Yates, Albert-László Barabási, Frank Dignum, Virginia Dignum, Tina Eliassi-Rad, Fosca Giannotti, János Kertész, Alistair Knott, Yannis Ioannidis, Paul Lukowicz, Andrea Passarella, Alex Sandy Pentland, John Shawe-Taylor, Alessandro Vespignani

Artificial Intelligence Volume 339, February 2025, 104244

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Aleksandar Haber, Ferenc Molnar,  Adilson E. Motter

Chaos 34, 123166 (2024)

In the classical control of network systems, the control actions on a node are determined as a function of the states of all nodes in the network. Motivated by applications where the global state cannot be reconstructed in real time due to limitations in the collection, communication, and processing of data, here we introduce a control approach in which the control actions can be computed as a function of the states of the nodes within a limited state information neighborhood. The trade-off between the control performance and the size of this neighborhood is primarily determined by the condition number of the controllability Gramian. Our theoretical results are supported by simulations on regular and random networks and are further illustrated by an application to the control of power-grid synchronization. We demonstrate that for well-conditioned Gramians, there is no significant loss of control performance as the size of the state information neighborhood is reduced, allowing efficient control of large networks using only local information.

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Takahiro Yabe, Bernardo García Bulle Bueno, Morgan R. Frank, Alex Pentland & Esteban Moro 
Nature Human Behaviour (2024)

Disruptions, such as closures of businesses during pandemics, not only affect businesses and amenities directly but also influence how people move, spreading the impact to other businesses and increasing the overall economic shock. However, it is unclear how much businesses depend on each other during disruptions. Leveraging human mobility data and same-day visits in five US cities, we quantify dependencies between points of interest encompassing businesses, stores and amenities. We find that dependency networks computed from human mobility exhibit significantly higher rates of long-distance connections and biases towards specific pairs of point-of-interest categories. We show that using behaviour-based dependency relationships improves the predictability of business resilience during shocks by around 40% compared with distance-based models, and that neglecting behaviour-based dependencies can lead to underestimation of the spatial cascades of disruptions. Our findings underscore the importance of measuring complex relationships in patterns of human mobility to foster urban economic resilience to shocks.

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Vicky Chuqiao Yang, Jacob J Jackson, Christopher P Kempes 
PNAS Nexus, Volume 3, Issue 11, November 2024, pgae503,

Cities exhibit consistent returns to scale in economic outputs, and urban scaling analysis is widely adopted to uncover common mechanisms in cities’ socioeconomic productivity. Leading theories view cities as closed systems, with returns to scale arising from intra-city social interactions. Here, we argue that the interactions between cities, particularly via shared organizations such as firms, significantly influence a city’s economic output. By examining global data on city connectivity through multinational firms alongside urban scaling Gross Domestic Product (GDP) statistics from the United States, EU, and China, we establish that global connectivity notably enhances GDP, while controlling for population. After accounting for global connectivity, the effect of population on GDP is no longer distinguishable from linear. To differentiate between local and global mechanisms, we analyzed homicide case data, anticipating dominant local effects. As expected, inter-city connectivity showed no significant impact. Our research highlights that inter-city effects affect some urban outputs more than others. This empirical analysis lays the groundwork for incorporating inter-city organizational connections into urban scaling theories and could inform future model development.

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