Gary S. Metcalf and Stuart A. Kauffman

Systems science, cybernetics, and complexity all evolved out of concerns for understanding complex phenomena in science. They also share many of the same theoretical roots, as well as histories which converge across leading figures and places in time. They can be conceived as three realms which shared and competed for prominence. All have influenced and been incorporated into scientific disciplines, though much of the history has been forgotten by current generations. Those historical roots remain relevant and important to future progress in science. This chapter provides a brief summary of the history and foundations of these domains.

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Philip Ball

J. Phys. Chem. Lett. 2021, 12, 27, 6336–6340

The issue is not, then, whether and how science can resist being “politicized”, but how the political and ideological dimensions of science can best be managed to make it most effective and beneficial both as an intellectual quest and as a means of, as Bacon put it, relieving (hu)mankind’s estate.

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Klaus Jaffe, Edrey Martinez, Ana Cecilia Soarez, Jose Gregorio Contreras, Juan C Correa, Antonio Canova
Based on what we know about thermodynamics of synergy, we explored the relationship between countries socio-cultural order (negentropy), estimated through their constitutions, indicators of Rule of Law and their academic development; with countries indicators of Free Energy (amount of useful work, productivity, socioeconomic health). The analysis of 219 indicators unveiled strong correlations between estimates of the Rule of Law and the number of Academic Publications, with the socioeconomic health indicators: GDP, Human Development Index and Infant Mortality. In contrast, correlations with the length of constitutions (number of words and of articles), suggest that the proliferation of legal rules hinders the rule of law and socioeconomic development, or that under-development and/or the lack of the rule of law foments the proliferation of legal rules. These findings suggest that not any order favors productivity (Free Energy) and that excess regulations and state tutelage increase social entropy decreasing socioeconomic health.

Read the full article at: arxiv.org

Xiaoyu Li, Chao Gao, Songxin Wang, Zhen Wang, Chen Liu & Xianghua Li 

The European Physical Journal B volume 94, Article number: 137 (2021)

The community structure, owing to its significant status, is of extraordinary significance in comprehending and detecting inherent functions in real networks. However, the community structures are always hard to be identified, and whether the existing algorithms are based on optimization or heuristics, the robustness and accuracy should be improved. The physarum (i.e., slime molds with multi heads) has proved its ability to produce foraging networks. Therefore, we adopt physarum so that the optimization-based community detection algorithms can work more efficiently. Specifically, a physarum-based network model (pnm), which is capable of identifying inter-edges of the community in a network, is used to optimize the prior knowledge of existing evolutional algorithms (i.e., genetic algorithm, particle swarm optimization algorithm and ant colony algorithm). the optimized algorithms have been compared with some advanced methods in synthetic and real networks. experimental results have verified the effectiveness of the proposed method.

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Mohamed S. Talamali, Arindam Saha, James A. R. Marshall, and Andreagiovanni Reina
Science Robotics 6(56): eabf1416
https://doi.org/10.1126/scirobotics.abf1416 
video: https://bcove.video/3zwyQpA 

You found a new better bar, with nicer drinks and healthier snacks, but you do not know how to convince your friends to deviate from their established favourite bar and take them to the new better place. Next time, you should consider convincing your friends one by one, rather than reaching out in the group chat. Recent research published today in Science Robotics, suggests that this strategy will increase your probability of convincing the entire group to choose the better bar.
The study has found that a population of naive individuals, when globally connected, can be unable to discard outdated beliefs and adopt better available alternatives. Instead, when the social network is sparse and individuals only share information locally, the population can effectively adapt to changes and reach an agreement in favour of the best option.
Researchers investigated how a swarm of autonomous robots could adapt to environmental changes and found the counterintuitive result that reduced social information would improve the spreading of localised information, and, in turn, allows an informed minority to effectively change the opinion of the entire group. This finding is opposed to the widely accepted and intuitive belief in network science that more connections lead to more effective information exchange. While information spreading speed may indeed increase, the study showed that adaptation—the ability to modify the group’s belief in light of new information—is impaired.

Read the full article at: robotics.sciencemag.org