Author: cxdig

The effect of co-location on human communication networks

Daniel Carmody, Martina Mazzarello, Paolo Santi, Trevor Harris, Sune Lehmann, Timur Abbiasov, Robin Dunbar & Carlo Ratti
Nature Computational Science volume 2, pages494–503 (2022)

The ability to rewire ties in communication networks is vital for large-scale human cooperation and the spread of new ideas. We show that lack of researcher co-location during the COVID-19 lockdown caused the loss of more than 4,800 weak ties—ties between distant parts of the social system that enable the flow of novel information—over 18 months in the email network of a large North American university. Furthermore, we find that the reintroduction of partial co-location through a hybrid work mode led to a partial regeneration of weak ties. We quantify the effect of co-location in forming ties through a model based on physical proximity, which is able to reproduce all empirical observations. Results indicate that employees who are not co-located are less likely to form ties, weakening the spread of information in the workplace. Such findings could contribute to a better understanding of the spatiotemporal dynamics of human communication networks and help organizations that are moving towards the implementation of hybrid work policies to evaluate the minimum amount of in-person interaction necessary for a productive work environment.

Read the full article at: www.nature.com

Esteban Moro – Collaborating with the Competition

Esteban’s work lies in the intersection of big data and computational social science, with special attention to human dynamics, collective intelligence, social networks and urban mobility in problems like viral marketing, natural disaster management, or economical segregation in cities.

Esteban is creative and exploring in his work, and has made important contributions in a number of topics, especially recently using massive datasets to understand problems like how humans communicate, how to political opinion spreads in social networks or building alternative wellbeing indexes.

In our conversation, we talk about his career, his science, and go deep with his 2011 paper “Dynamical strength of social ties in information spreading”, which is not only an interesting paper … but also comes with a very nice story of how we can build communities in science by collaborating even between competing groups.

Listen at: toolazy.buzzsprout.com

When to Be Critical? Performance and Evolvability in Different Regimes of Neural Ising Agents

Sina Khajehabdollahi, Jan Prosi, Emmanouil Giannakakis, Georg Martius, Anna Levina

Artificial Life (In Press)

It has long been hypothesized that operating close to the critical state is beneficial for natural and artificial evolutionary systems. We put this hypothesis to test in a system of evolving foraging agents controlled by neural networks that can adapt the agents’ dynamical regime throughout evolution. Surprisingly, we find that all populations that discover solutions evolve to be subcritical. By a resilience analysis, we find that there are still benefits of starting the evolution in the critical regime. Namely, initially critical agents maintain their fitness level under environmental changes (for example, in the lifespan) and degrade gracefully when their genome is perturbed. At the same time, initially subcritical agents, even when evolved to the same fitness, are often inadequate to withstand the changes in the lifespan and degrade catastrophically with genetic perturbations. Furthermore, we find the optimal distance to criticality depends on the task complexity. To test it we introduce a hard task and a simple task: For the hard task, agents evolve closer to criticality, whereas more subcritical solutions are found for the simple task. We verify that our results are independent of the selected evolutionary mechanisms by testing them on two principally different approaches: a genetic algorithm and an evolutionary strategy. In summary, our study suggests that although optimal behaviour in the simple task is obtained in a subcritical regime, initializing near criticality is important to be efficient at finding optimal solutions for new tasks of unknown complexity.

Read the full article at: direct.mit.edu