Papers

Nicholas W. Landry, Ilya Amburg, Mirah Shi, Sinan G. Aksoy
Many complex systems often contain interactions between more than two nodes, known as higher-order interactions, which can change the structure of these systems in significant ways. Researchers often assume that all interactions paint a consistent picture of a higher-order dataset's structure. In contrast, the connection patterns of individuals or entities in empirical systems are often stratified by interaction size. Ignoring this fact can aggregate connection patterns that exist only at certain scales of interaction. To isolate these scale-dependent patterns, we present an approach for analyzing higher-order datasets by filtering interactions by their size. We apply this framework to several empirical datasets from three domains to demonstrate that data practitioners can gain valuable information from this approach.

Read the full article at: arxiv.org

Austin M. Marcus and Hiroki Sayama

Complexity Volume 2023 | Article ID 8852349

Collective motion models most often use self-propelled particles, which are known to produce organized spatial patterns via their collective interactions. However, there is less work considering the possible organized spatial patterns achievable by non-self-propelled particles (nondriven), i.e., those obeying energy and momentum conservation. Moreover, it is not known how the potential energy interaction between the particles affects the complexity of the patterns. To address this, in this paper, a collective motion model with a pairwise potential energy function that conserved the total energy and momentum of the particles was implemented. The potential energy function was derived by generalizing the Lennard–Jones potential to reduce to gravity-like and billiard-ball-like potentials at the extremes of its parameter range. The particle model was simulated under a number of parameterizations of this generalized potential, and the average complexity of the spatial pattern produced by each was computed. Complexity was measured by tracking the information needed to describe the particle system at different scales (the complexity profile). It was found that the spatial patterns of the particles were the most complex around a specific ratio in the parameters. This parameter ratio described a characteristic shape of the potential energy function that is capable of producing complex spatial patterns. It is suggested that the characteristic shape of the potential energy produces complex behavior by balancing the likelihood for particles to bond. Furthermore, these results demonstrate that complex spatial patterns are possible even in an isolated system.

Read the full article at: www.hindawi.com

Sai Krishna Katla, Chenyu Lin, Juan Pérez-Mercader

Cell Reports Physical Science VOLUME 4, ISSUE 4, 101359, APRIL 19, 2023

• One-pot synthesis of simple non-biochemical, but functional, protocell populations
• Two populations emerge, differing only in that one has an advantage for reproduction
• The populations compete for food in a shared environment, with one dying out
• Simple protocells display Darwin’s “struggle for existence” without biochemistry

Read the full article at: www.cell.com

Fernanda Sánchez-Puig, Octavio Zapata, Omar K. Pineda, Gerardo Iñiguez, and Carlos Gershenson

Front. Complex Syst., 03 May 2023

Criticality has been proposed as a mechanism for the emergence of complexity, life, and computation, as it exhibits a balance between order and chaos. In classic models of complex systems where structure and dynamics are considered homogeneous, criticality is restricted to phase transitions, leading either to robust (ordered) or fragile (chaotic) phases for most of the parameter space. Many real-world complex systems, however, are not homogeneous. Some elements change in time faster than others, with slower elements (usually the most relevant) providing robustness, and faster ones being adaptive. Structural patterns of connectivity are also typically heterogeneous, characterized by few elements with many interactions and most elements with only a few. Here we take a few traditionally homogeneous dynamical models and explore their heterogeneous versions, finding evidence that heterogeneity extends criticality. Thus, parameter fine-tuning is not necessary to reach a phase transition and obtain the benefits of (homogeneous) criticality. Simply adding heterogeneity can extend criticality, making the search/evolution of complex systems faster and more reliable. Our results add theoretical support for the ubiquitous presence of heterogeneity in physical, biological, social, and technological systems, as natural selection can exploit heterogeneity to evolve complexity “for free”. In artificial systems and biological design, heterogeneity may also be used to extend the parameter range that allows for criticality.

Read the full article at: www.frontiersin.org

Arsham Ghavasieh and Manlio De Domenico
Phys. Rev. E 107, 044304

The network density matrix formalism allows for describing the dynamics of information on top of complex structures and it has been successfully used to analyze, e.g., a system's robustness, perturbations, coarse-graining multilayer networks, characterization of emergent network states, and performing multiscale analysis. However, this framework is usually limited to diffusion dynamics on undirected networks. Here, to overcome some limitations, we propose an approach to derive density matrices based on dynamical systems and information theory, which allows for encapsulating a much wider range of linear and nonlinear dynamics and richer classes of structure, such as directed and signed ones. We use our framework to study the response to local stochastic perturbations of synthetic and empirical networks, including neural systems consisting of excitatory and inhibitory links and gene-regulatory interactions. Our findings demonstrate that topological complexity does not necessarily lead to functional diversity, i.e., the complex and heterogeneous response to stimuli or perturbations. Instead, functional diversity is a genuine emergent property which cannot be deduced from the knowledge of topological features such as heterogeneity, modularity, the presence of asymmetries, and dynamical properties of a system.

Read the full article at: link.aps.org

González, A.; González, S.; Pereira, G.; Blanco, G. and von Lücken, C. (2023). In Proceedings of the 8th International Conference on Complexity, Future Information Systems and Risk - COMPLEXIS, ISBN 978-989-758-644-6; ISSN 2184-5034, SciTePress, pages 109-121. DOI: 10.5220/0012059400003485

International Economic Integration can be described as a process in which a group of countries seeks mutual benefits through mechanisms such as the elimination and/or reduction of trade, social, and political barriers between others. From an economic point of view, the importance of the integration of countries is fundamental for their development simply because most of them are part of some system of international economic integration. In this work, the issue of economic integration will not be discussed in depth but instead will oversee proposing some well-known metrics in the field of economic development that could be very useful as analysis and decision-making tools. in the process of regional economic integration. In this sense, this work proposes using concepts and metrics of Economic Complexity and Economic Fitness to identify combined productive capacities between countries that are part of an economic block, whether real or fictitious. The problem in understanding how econo mically integrate the countries is to identify the combined productive capacities that would exist if two or more countries that make up an economic block are considered as a single country. Experimental analyzes were carried out for a fictitious case, where a world with 10 countries and 15 products is presented; in addition, 3 economic blocks were defined, which were analyzed applying economic complexity and economic fitness metrics. The results obtained reflect the great importance of economic integration since, by establishing economic blocks, it is possible to capture more productive capacities by improving both the diversity of the economic block and the ubiquity of the products produced in it by addressing the productive capacities of the member countries.

Read the full article at: www.scitepress.org

Emanuela Del Dottore, Barbara Mazzolai

Artificial Life

Plants thrive in virtually all natural and human-adapted environments and are becoming popular models for developing robotics systems because of their strategies of morphological and behavioral adaptation. Such adaptation and high plasticity offer new approaches for designing, modeling, and controlling artificial systems acting in unstructured scenarios. At the same time, the development of artifacts based on their working principles reveals how plants promote innovative approaches for preservation and management plans and opens new applications for engineering-driven plant science. Environmentally mediated growth patterns (e.g., tropisms) are clear examples of adaptive behaviors displayed through morphological phenotyping. Plants also create networks with other plants through subterranean roots–fungi symbiosis and use these networks to exchange resources or warning signals. This article discusses the functional behaviors of plants and shows the close similarities with a perceptron-like model that could act as a behavior-based control model in plants. We begin by analyzing communication rules and growth behaviors of plants; we then show how we translated plant behaviors into algorithmic solutions for bioinspired robot controllers; and finally, we discuss how those solutions can be extended to embrace original approaches to networking and robotics control architectures.

Read the full article at: direct.mit.edu

Annalisa Caligiuri, Victor M. Eguíluz, Leonardo Di Gaetano, Tobias Galla, and Lucas Lacasa

Phys. Rev. E 107, 044305

By interpreting a temporal network as a trajectory of a latent graph dynamical system, we introduce the concept of dynamical instability of a temporal network and construct a measure to estimate the network maximum Lyapunov exponent (nMLE) of a temporal network trajectory. Extending conventional algorithmic methods from nonlinear time-series analysis to networks, we show how to quantify sensitive dependence on initial conditions and estimate the nMLE directly from a single network trajectory. We validate our method for a range of synthetic generative network models displaying low- and high-dimensional chaos and finally discuss potential applications.

Read the full article at: link.aps.org

If we want to understand complex constructions, such as ourselves, assembly theory says we must account for the entire history of how such entities came to be.

Read the full article at: www.quantamagazine.org

Masayuki Ushio, Kazufumi Watanabe, Yasuhiro Fukuda, Yuji Tokudome and Kohei Nakajima

Royal Society Open Science April 2023 Volume 10 Issue 4

Ecological dynamics is driven by complex ecological networks. Computational capabilities of artificial networks have been exploited for machine learning purposes, yet whether an ecological network possesses a computational capability and whether/how we can use it remain unclear. Here, we developed two new computational/empirical frameworks based on reservoir computing and show that ecological dynamics can be used as a computational resource. In silico ecological reservoir computing (ERC) reconstructs ecological dynamics from empirical time series and uses simulated system responses for information processing, which can predict near future of chaotic dynamics and emulate nonlinear dynamics. The real-time ERC uses real population dynamics of a unicellular organism, Tetrahymena thermophila. The temperature of the medium is an input signal and population dynamics is used as a computational resource. Intriguingly, the real-time ecological reservoir has necessary conditions for computing (e.g. synchronized dynamics in response to the same input sequences) and can make near-future predictions of empirical time series, showing the first empirical evidence that population-level phenomenon is capable of real-time computations. Our finding that ecological dynamics possess computational capability poses new research questions for computational science and ecology: how can we efficiently use it and how is it actually used, evolved and maintained in an ecosystem?

Read the full article at: royalsocietypublishing.org

Huayan Pei, Guanghui Yan & Yaning Huang 

The European Physical Journal B volume 96, Article number: 44 (2023)

Contact reduction is an effective strategy to mitigate the spreading of epidemic. However, the existing reaction–diffusion equations for infectious disease are unable to characterize this effect. Thus, we here propose an extended susceptible-infected-recovered model by incorporating contact rate into the standard SIR model, and concentrate on investigating its impact on epidemic transmission. We analytically derive the epidemic thresholds on homogeneous and heterogeneous networks, respectively. The effects of contact rate on spreading speed, scale and outbreak threshold are explored on ER and SF networks. Simulations results show that epidemic dissemination is significantly mitigated when contact rate is reduced. Importantly, epidemic spreads faster on heterogeneous networks while broader on homogeneous networks, and the outbreak thresholds of the former are smaller.

Read the full article at: link.springer.com

Jian Gao, Dashun Wang

The ongoing artificial intelligence (AI) revolution has the potential to change almost every line of work. As AI capabilities continue to improve in accuracy, robustness, and reach, AI may outperform and even replace human experts across many valuable tasks. Despite enormous efforts devoted to understanding AI's impact on labor and the economy and its recent success in accelerating scientific discovery and progress, we lack a systematic understanding of how advances in AI may benefit scientific research across disciplines and fields. Here we develop a measurement framework to estimate both the direct use of AI and the potential benefit of AI in scientific research by applying natural language processing techniques to 87.6 million publications and 7.1 million patents. We find that the use of AI in research appears widespread throughout the sciences, growing especially rapidly since 2015, and papers that use AI exhibit an impact premium, more likely to be highly cited both within and outside their disciplines. While almost every discipline contains some subfields that benefit substantially from AI, analyzing 4.6 million course syllabi across various educational disciplines, we find a systematic misalignment between the education of AI and its impact on research, suggesting the supply of AI talents in scientific disciplines is not commensurate with AI research demands. Lastly, examining who benefits from AI within the scientific workforce, we find that disciplines with a higher proportion of women or black scientists tend to be associated with less benefit, suggesting that AI's growing impact on research may further exacerbate existing inequalities in science. As the connection between AI and scientific research deepens, our findings may have an increasing value, with important implications for the equity and sustainability of the research enterprise.

Read the full article at: arxiv.org