Genki Ichinose, Daiki Miyagawa, Erika Chiba, Hiroki Sayama
Artificial Life

Cooperation among individuals has been key to sustaining societies. However, natural selection favors defection over cooperation. Cooperation can be favored when the mobility of individuals allows cooperators to form a cluster (or group). Mobility patterns of animals sometimes follow a Lévy flight. A Lévy flight is a kind of random walk but it is composed of many small movements with a few big movements. The role of Lévy flights for cooperation has been studied by Antonioni and Tomassini, who showed that Lévy flights promoted cooperation combined with conditional movements triggered by neighboring defectors. However, the optimal condition for neighboring defectors and how the condition changes with the intensity of Lévy flights are still unclear. Here, we developed an agent-based model in a square lattice where agents perform Lévy flights depending on the fraction of neighboring defectors. We systematically studied the relationships among three factors for cooperation: sensitivity to defectors, the intensity of Lévy flights, and population density. Results of evolutionary simulations showed that moderate sensitivity most promoted cooperation. Then, we found that the shortest movements were best for cooperation when the sensitivity to defectors was high. In contrast, when the sensitivity was low, longer movements were best for cooperation. Thus, Lévy flights, the balance between short and long jumps, promoted cooperation in any sensitivity, which was confirmed by evolutionary simulations. Finally, as the population density became larger, higher sensitivity was more beneficial for cooperation to evolve. Our study highlights that Lévy flights are an optimal searching strategy not only for foraging but also for constructing cooperative relationships with others.

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Mapping the travel geography of terrorist networks can help expose how they operate internationally. Olivier Walther, Joseph Padron, and Jason Scheuer of the University of Florida and Rafael Prieto Curiel of the Complexity Science Hub in Vienna take a close look at the 9/11 plot and find that terrorists who belonged to the same operational cell did not necessarily live in the same place at the same time. However, their itineraries closely matched their organizational structure. Distinct travel patterns and strong social ties not only made the 9/11 travel network resilient but also essentially allowed the 19 hijackers to hide in plain sight while being very mobile.

Read the full article at: www.lawfareblog.com

Gerardo L. Febres and Carlos Gershenson

Systems 2022, 10(5), 138

More than two years after the declaration of the COVID-19 pandemic, we are still experiencing contagious waves. As this is a long-lasting process, it becomes relevant to have a predictive tool to identify the intensively active places within a region. This study presents the development of a forecasting model applied to foresee the progress of the contagious process in Mexico and its regions. The method comprehends aspects of deterministic and probabilistic modeling. The deterministic part comprises the classical SIR model with some adjustments. The probabilistic part builds and populates a three-dimensional array, which is then used to describe and recall the probabilities of going from one status to another after some time, very much like a Markovian process. The process status is modeled as the combination of two conditions: the infection exponential growth parameter and a proxy variable we named “permissiveness” that accounts for all combined social activity factors affecting COVID-19 propagation. The results offer projections of the exponential growth parameter and the number of newly infected individuals for three weeks into the future. The proposed method’s capabilities allow for predicting newly COVID-19-infected individuals with reasonable precision while capturing the characteristic dynamics and behavior of the modeled system.

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Bhargav Srinivasa Desikan, Hajime Shimao, and Helena Miton

Entropy 2022, 24(9), 1175

With the increase in massive digitized datasets of cultural artefacts, social and cultural scientists have an unprecedented opportunity for the discovery and expansion of cultural theory. The WikiArt dataset is one such example, with over 250,000 high quality images of historically significant artworks by over 3000 artists, ranging from the 15th century to the present day; it is a rich source for the potential mining of patterns and differences among artists, genres, and styles. However, such datasets are often difficult to analyse and use for answering complex questions of cultural evolution and divergence because of their raw formats as image files, which are represented as multi-dimensional tensors/matrices. Recent developments in machine learning, multi-modal data analysis and image processing, however, open the door for us to create representations of images that extract important, domain-specific features from images. Art historians have long emphasised the importance of art style, and the colors used in art, as ways to characterise and retrieve art across genre, style, and artist. In this paper, we release a massive vector-based dataset of paintings (WikiArtVectors), with style representations and color distributions, which provides cultural and social scientists with a framework and database to explore relationships across these two vital dimensions. We use state-of-the-art deep learning and human perceptual color distributions to extract the representations for each painting, and aggregate them across artist, style, and genre. These vector representations and distributions can then be used in tandem with information-theoretic and distance metrics to identify large-scale patterns across art style, genre, and artist. We demonstrate the consistency of these vectors, and provide early explorations, while detailing future work and directions. All of our data and code is publicly available on GitHub.

Read the full article at: www.mdpi.com