Contrasting social and non-social sources of predictability in human mobility

Zexun Chen, Sean Kelty, Alexandre G. Evsukoff, Brooke Foucault Welles, James Bagrow, Ronaldo Menezes & Gourab Ghoshal 
Nature Communications volume 13, Article number: 1922 (2022)

Social structures influence human behavior, including their movement patterns. Indeed, latent information about an individual’s movement can be present in the mobility patterns of both acquaintances and strangers. We develop a “colocation” network to distinguish the mobility patterns of an ego’s social ties from those not socially connected to the ego but who arrive at a location at a similar time as the ego. Using entropic measures, we analyze and bound the predictive information of an individual’s mobility pattern and its flow to both types of ties. While the former generically provide more information, replacing up to 94% of an ego’s predictability, significant information is also present in the aggregation of unknown colocators, that contain up to 85% of an ego’s predictive information. Such information flow raises privacy concerns: individuals sharing data via mobile applications may be providing actionable information on themselves as well as others whose data are absent.

Read the full article at: www.nature.com

Entropy | Special Issue: Recent Advances in Guided Self-Organization

Examples of self-organising systems can be found practically everywhere: a heated fluid forms regular convection patterns of Bénard cells, neuronal ensembles self-organise into complex spike patterns, a swarm changes its shape in response to an approaching predator, ecosystems develop spatial structures in order to deal with diminishing resources, and so on.

Typically, self-organisation (SO) is defined as the evolution of a system into an organised form in the absence of external pressures. SO within a system brings about several attractive properties, in particular robustness, adaptability, and scalability. Consequently, a natural question to ask would be: Is it possible to guide the process of self-organisation towards some desirable patterns and outcomes? Over the last decades, it has become apparent that this question can be rigorously formalised across multiple domains, leading to the emergence of a new research field: Guided Self-Organisation (GSO). This has led to theoretical developments in information theory, network theory, dynamical systems, game theory, systems biology, and sociophysics, as well as practical applications in artificial intelligence, synthetic biology, unconventional computation, distributed robotics, and active matter.

More at: www.mdpi.com

Virtual Workshop: Is AI Extending the Mind?

April 11-15, 2022

The extended mind hypothesis suggests that cognition does not just occur in our minds, but also extends into the physical world around us. Cognition is then a process involving a system of coupled components that work together to enact intelligent processes. In this workshop, we revisit this point of view in the context of modern scientific advances. This workshop will consist largely of discussions centered around the main theme of the workshop, with daily keynotes addressing Agency, AI Ethics, and Human Augmentation.

More at: www.crosslabs.org

Self-Organization in Network Sociotechnical Systems

Svetlana Maltseva, Vasily Kornilov, Vladimir Barakhnin, and Alexander Gorbunov

Complexity Volume 2022 |Article ID 5714395

We can observe self-organization properties in various systems. However, modern networked dynamical sociotechnical systems have some features that allow for realizing the benefits of self-organization in a wide range of systems in economic and social areas. The review examines the general principles of self-organized systems, as well as the features of the implementation of self-organization in sociotechnical systems. We also delve into the production systems, in which the technical component is decisive, and social networks, in which the social component dominates; we analyze models used for modeling self-organizing networked dynamical systems. It is shown that discrete models prevail at the micro level. Furthermore, the review deals with the features of using continuous models for modeling at the macro level.

Read the full article at: www.hindawi.com

Complexity Research

Where do we come from? Where are we going? New research from the Santa Fe Institute explores key questions related to humanity, society, and the existence of life in our world.

Part 1: The Principles of Complexity: Understanding the Hidden Sources of Order by Dr. Stefani Crabtree

Part 2: Autocatalytic Sets: Complexity at the Interface of Chemistry and Biology by Dr. Wim Hordijk

Part 3: Beyond Pairwise: Higher-Order Interactions in Complex Systems

Read the full article at: www.templeton.org