Oxford Mathematicians and Economists Maria del Rio-Chanona, Penny Mealy, Mariano Beguerisse-Díaz, François Lafond, and J. Doyne Farmer discuss their network model of labor market dynamics.

“Mathematics has explained many physical, chemical, and biological phenomena, but can it explain how the economy works? It is challenging because the economy is highly diverse, and ever-changing, with both short term fluctuations – it goes through recession and recovery periods – and long-term structural change – innovation transforms the scope and diversity of what we do.

Take the labor market, for example. Figure 1 shows what we call the occupational mobility network (1) – each node is an occupation, and the links show how likely it is that a worker in an occupation moves to another occupation. Clearly, there are many different occupations, and some occupational transitions are more likely than others. How can we model the dynamics of the labor market while taking this into account (click figure to enlarge)?

Read the full article at: www.maths.ox.ac.uk

Special issue on “Collective decision-making in living and artificial systems”
Swarm Intelligence, volume 15, issue 1–2 (2021)
Edited by A. Reina, E. Ferrante & G. Valentini

Collective decision-making is a fundamental cognitive process required for group coordination. Typically, this process requires individuals in a group to either reach a consensus on one of several available options or to distribute their workforce over different tasks. Similar collective decision-making processes can be found in a large number of systems, motivating a vast modeling effort across scientific disciplines. It can be observed across scales in a variety of animal groups, from unicellular organisms, to social insects, fish schools, and groups of mammals. In the social sciences, scientific domains such as econophysics and sociophysics emerged to investigate collective decisions in humans, deepening our understanding of the dynamics of economies and social policies. Neuroscientists also look at brains as a collection of neurons that, through numerous interactions, lead to rational decisions. Studies of collective decision-making in nature inspired the engineering of decentralized cyber-physical systems such as robot swarms and wireless sensor networks with the potential to create new emerging and disruptive technologies. Collective decision-making, ubiquitous across living and artificial collectives, can benefit from an interdisciplinary approach as apparently different systems may share similar mechanisms. With this special issue, we aim to push forward such an interdisciplinary approach by providing perspectives and insights from biology, information science, and engineering.

Read the full article at: link.springer.com

A group of mathematicians has shown that at critical moments, a symmetry called rotational invariance is a universal property across many physical systems.

Read the full article at: www.quantamagazine.org

Lucille Calmon, Juan G. Restrepo, Joaquín J. Torres, Ginestra Bianconi
Topological signals defined on nodes, links and higher dimensional simplices define the dynamical state of a network or of a simplicial complex. As such, topological signals are attracting increasing attention in network theory, dynamical systems, signal processing and machine learning. Topological signals defined on the nodes are typically studied in network dynamics, while topological signals defined on links are much less explored. Here we investigate topological synchronization describing locally coupled topological signals defined on the nodes and on the links of a network. The dynamics of signals defined on the nodes is affected by a phase lag depending on the dynamical state of nearby links and vice versa, the dynamics of topological signals defined on the links is affected by a phase lag depending on the dynamical state of nearby nodes. We show that topological synchronization on a fully connected network is explosive and leads to a discontinuous forward transition and a continuous backward transition. The analytical investigation of the phase diagram provides an analytical expression for the critical threshold of the discontinuous explosive synchronization. The model also displays an exotic coherent synchronized phase, also called rhythmic phase, characterized by having non-stationary order parameters which can shed light on topological mechanisms for the emergence of brain rhythms.

Read the full article at: arxiv.org

Mitochondria appear to communicate and cooperate with one another, both within and between cells. Biologists are only just beginning to understand how and why.

Read the full article at: www.quantamagazine.org