Month: February 2023

Complexity Explorables | The Prisoner’s Kaleidoscope

This explorable illustrates beautiful dynamical patterns that can be generated by a simple game theoretic model on a lattice. The core of the model is the Prisoner’s Dilemma, a legendary game analyzed in game theory. In the game, two players can choose to cooperate or defect. Depending on their choice, they receive a pre-specified payoffs. The payoffs are chosen such that it seems difficult to make the right strategy choice.

Read the full article at: www.complexity-explorables.org

Complexity72h

Complexity72h is an interdisciplinary workshop for young researchers in complex systems. Participants form teams and carry out projects in a three days’ time, i.e. 72 hours. The goal of each team is to upload on the arXiv (or similar repositories) a report of their work by the end of the event. The editions of 2018 and 2019 were a success: 11 out of 11 projects became arXiv preprints and new collaborations were born. Complexity72h is back for a 2023 edition, which will take place in Palma (Mallorca, Spain) on June 26-30.

More info & application: www.complexity72h.com 
Deadline for applications: March 1st 2023

Effective Connectivity and Bias Entropy Improve Prediction of Dynamical Regime in Automata Networks

Felipe Xavier Costa, Jordan C. Rozum, Austin M. Marcus, and Luis M. Rocha

Entropy 2023, 25(2), 374

Biomolecular network dynamics are thought to operate near the critical boundary between ordered and disordered regimes, where large perturbations to a small set of elements neither die out nor spread on average. A biomolecular automaton (e.g., gene, protein) typically has high regulatory redundancy, where small subsets of regulators determine activation via collective canalization. Previous work has shown that effective connectivity, a measure of collective canalization, leads to improved dynamical regime prediction for homogeneous automata networks. We expand this by (i) studying random Boolean networks (RBNs) with heterogeneous in-degree distributions, (ii) considering additional experimentally validated automata network models of biomolecular processes, and (iii) considering new measures of heterogeneity in automata network logic. We found that effective connectivity improves dynamical regime prediction in the models considered; in RBNs, combining effective connectivity with bias entropy further improves the prediction. Our work yields a new understanding of criticality in biomolecular networks that accounts for collective canalization, redundancy, and heterogeneity in the connectivity and logic of their automata models. The strong link we demonstrate between criticality and regulatory redundancy provides a means to modulate the dynamical regime of biochemical networks.

Read the full article at: www.mdpi.com

Tracking stolen bikes in Amsterdam

Venverloo T, Duarte F, Benson T, Leoni P, Hoogendoorn S, Ratti C (2023) Tracking stolen bikes in Amsterdam. PLoS ONE 18(2): e0279906.

Crime has major influences in urban life, from migration and mobility patterns, to housing prices and neighborhood liveability. However, urban crime studies still largely rely on static data reported by the various institutions and organizations dedicated to urban safety. In this paper, we demonstrate how the use of digital technologies enables the fine-grained analysis of specific crimes over time and space. This paper leverages the rise of ubiquitous sensing to investigate the issue of bike theft in Amsterdam—a city with a dominant cycling culture, where reportedly more than 80,000 bikes are stolen every year. We use active location tracking to unveil where stolen bikes travel to and what their temporal patterns are. This is the first study using tracking technologies to focus on two critical aspects of contemporary cities: active mobility and urban crime.

Read the full article at: journals.plos.org