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        <title>Frontiers in Complex Systems | New and Recent Articles</title>
        <link>https://www.frontiersin.org/journals/complex-systems</link>
        <description>RSS Feed for Frontiers in Complex Systems | New and Recent Articles</description>
        <language>en-us</language>
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        <pubDate>2026-04-20T03:08:19.661+00:00</pubDate>
        <ttl>60</ttl>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcpxs.2026.1800101</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcpxs.2026.1800101</link>
        <title><![CDATA[Decentralized coordination of autonomous agents in the Compute Continuum using consensus]]></title>
        <pubdate>2026-04-14T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Xavier Casas-Moreno</author><author>Komal Thareja</author><author>Pablo de Juan Vela</author><author>Rajiv Mayani</author><author>Josep Fanals-i-Batllori</author><author>Anirban Mandal</author><author>Ewa Deelman</author><author>Rosa M. Badia</author><author>Francesc Lordan</author>
        <description><![CDATA[The Compute Continuum—spanning IoT, Edge, Cloud, and HPC resources—is reshaping how hyper-distributed applications are designed and orchestrated. Traditional service orchestrators and workload management systems rely on centralized runtimes; however, the emerging paradigm requires decentralized coordination, where autonomous agents cooperate to achieve common goals and dynamically distribute workloads. Consensus algorithms play a crucial role in multi-agent systems (MAS), as they enable agents to reach agreement on how to coordinate and execute functionalities in a cooperative manner. While consensus has previously been applied to distributed job selection, here we extend its use to swarm environments. In this setting, agents autonomously decide which service functionalities (i.e., roles) to execute based on their capabilities and the real-time quality of service (QoS). Functionalities can be elastically activated or terminated as application needs evolve. To support this model, we leverage the COLMENA framework, a programming environment for defining and managing such dynamic services. We apply a greedy consensus-based approach to modern power systems, which are increasingly decentralized due to the large-scale integration of renewable energy sources. Centralized power plants are giving way to distributed, intermittent resources that require decentralized control paradigms. To demonstrate this, we simulate the Northeastern Power Coordinating Council’s (NPCC) 140-bus grid using the ANDES simulator in conjunction with the COLMENA middleware. We deploy this use case across six different sites in the FABRIC testbed, using up to 60 different nodes. Our results show that, under contingency scenarios such as load and generator disconnections, agents self-organize, elect local leaders, and execute optimization algorithms to stabilize grid frequency. Detection and organization times remain below 10s across all experiments, even as the number of agents per area scales from 3 to 10. Stability is restored within approximately 27s and 40s for the respective cases. Resource overhead is minimal, with CPU and memory usage remaining below 7.5% and 2%, respectively. Experiment automation and reproducibility are ensured through Kiso. These findings indicate that role-based programming models complement traditional workflows and that consensus-driven coordination can effectively decentralize decision-making in swarm environments. This approach represents a step toward enabling resilient, decentralized power systems.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcpxs.2026.1724679</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcpxs.2026.1724679</link>
        <title><![CDATA[Deployment of transatlantic computational testbeds via the infrastructure manager]]></title>
        <pubdate>2026-02-17T00:00:00Z</pubdate>
        <category>Brief Research Report</category>
        <author>Germán Moltó</author><author>Miguel Caballer</author><author>Estíbaliz Parcero</author><author>Vicente Rodríguez</author>
        <description><![CDATA[Transatlantic scientific collaborations require computational testbeds that can be provisioned on demand and reconfigured rapidly while spanning institutions in different regulatory and operational domains. During the DISCOVER-US exchange program, we integrated the Infrastructure Manager (IM), a TOSCA-based orchestrator for the computing continuum, with the Chameleon cloud infrastructure. The workflow combined federated identity management, delegated project administration, and an IM plugin that targets Chameleon’s OpenStack endpoints through application credentials. We validated the approach by deploying single virtual machines, a production-ready Galaxy environment, distributed OSCAR-based serverless clusters that offload an AI-based fish detection pipeline, a workflow for flood impact modeling, and a hybrid SLURM cluster. Transatlantic computational testbeds included dynamically provisioned computational resources from EGI Federated Cloud and Chameleon. The study also documents operational constraints encountered with lease automation, bare-metal introspection, and the exposure of Kubernetes services across wide-area networks. The resulting blueprint demonstrates a reproducible path to deploy secure, elastic, and scientifically useful transatlantic computational testbeds.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcpxs.2026.1808241</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcpxs.2026.1808241</link>
        <title><![CDATA[Retraction: Toward a thermodynamic theory of evolution: a theoretical perspective on information entropy reduction and the emergence of complexity]]></title>
        <pubdate>2026-02-16T00:00:00Z</pubdate>
        <category>Retraction</category>
        
        <description></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcpxs.2026.1794474</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcpxs.2026.1794474</link>
        <title><![CDATA[Editorial: Game theory and evolutionary dynamics: unraveling complex systems]]></title>
        <pubdate>2026-02-13T00:00:00Z</pubdate>
        <category>Editorial</category>
        <author>Chengyi Tu</author><author>Hongzhong Deng</author>
        <description></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcpxs.2025.1672525</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcpxs.2025.1672525</link>
        <title><![CDATA[On the evolutionary dynamics of complexity and consciousness]]></title>
        <pubdate>2026-01-06T00:00:00Z</pubdate>
        <category>Review</category>
        <author>Shimon Edelman</author>
        <description><![CDATA[The perennial debate about the possible directionality of evolution, as indicated by the apparent increase in the complexity of living systems over time, has recently witnessed renewed arguments in favor of the growth of complexity being “entropic,” that is, consistent with the growth of entropy as it is construed in thermodynamics. Here, I offer a brief review of formal treatments of complexity and of evolutionary mechanisms that are capable of causing it to increase. I then propose that both the evolutionary emergence and the individual learning of basic phenomenal awareness, a type of consciousness, are characterized by the same time-asymmetrical dynamics. Like life itself, biological consciousness arguably evolves towards greater complexity, and for the same reasons.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcpxs.2025.1636222</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcpxs.2025.1636222</link>
        <title><![CDATA[Organizational regularities in recurrent neural networks]]></title>
        <pubdate>2026-01-05T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Claus Metzner</author><author>Achim Schilling</author><author>Andreas Maier</author><author>Patrick Krauss</author>
        <description><![CDATA[Previous work has shown that the dynamical regime of Recurrent Neural Networks (RNNs)—ranging from oscillatory to chaotic and fixed point behavior—can be controlled by the global distribution of weights in connection matrices with statistically independent elements. However, it remains unclear how network dynamics respond to organizational regularities in the weight matrix, as often observed in biological neural networks. Here, we investigate three such regularities: (1) monopolar output weights per neuron, in accordance with Dale’s principle, (2) reciprocal symmetry between neuron pairs, as in Hopfield networks, and (3) modular structure, where strongly connected blocks are embedded in a background of weaker connectivity. These regularities are studied independently, but as functions of the RNN’s general connection strength and its excitatory/inhibitory bias. For this purpose, we construct weight matrices in which the strength of each regularity can be continuously tuned via control parameters, and analyze how key dynamical signatures of the RNN evolve as a function of these parameters. Moreover, using the RNN for actual information processing in a reservoir computing framework, we study how each regularity affects performance. We find that Dale monopolarity and modularity significantly enhance task accuracy, while Hopfield reciprocity tends to reduce it by promoting early saturation, limiting reservoir flexibility.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcpxs.2025.1667670</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcpxs.2025.1667670</link>
        <title><![CDATA[Emergence as a science]]></title>
        <pubdate>2025-12-01T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Benyamin Lichtenstein</author>
        <description><![CDATA[Although this Special Issue calls for a theory of emergence, the present paper argues that the breadth of the phenomenon\a requires a science, within which various theories can be explored and tested. To identify a structure for such a science of emergence, I pursued an in-depth cross-disciplinary analysis of emergence and its emergents. The result was identifying 9 emergence Prototypes, each of which reflects a unique aspect or context of emergence. Further, within some Prototypes, decades of scientific research has led to one or more Principles that its scholars ascribe to. Finally, the potential of an emergence science is explored by introducing applications of emergence to Leadership, Entrepreneurship, and Sustainability.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcpxs.2025.1678321</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcpxs.2025.1678321</link>
        <title><![CDATA[Effect of quenched disorder on the absorbing transition in contact processes on a comb lattice]]></title>
        <pubdate>2025-11-21T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Priyanka D. Bhoyar</author><author>Prashant M. Gade</author>
        <description><![CDATA[Power-law behavior frequently emerges in physical, biological, and social systems, particularly near continuous phase transitions characterized by diverging correlation lengths and universal scaling. The contact process is a prototypical model for studying absorbing-state phase transitions, typically belonging to the directed percolation (DP) universality class in its clean form. In this study, we investigate how quenched disorder influences the absorbing-state transition of the contact process on a one-dimensional comb lattice, a minimal geometry that incorporates structural inhomogeneity while remaining analytically and computationally tractable. In our model, activity spreads over a fraction of the branches q and is blocked in the rest. Without disorder, the system belongs to the directed percolation (DP) universality class. Introducing quenched disorder leads to significant changes in the critical dynamics. For q≤0.15, the system develops a Griffiths phase characterized by algebraic decay away from the critical point and logarithmic scaling at criticality, indicating a transition to the activated scaling universality class. In contrast, for q>0.15, the contact process on the comb lattice shows power-law decay of the order parameter only at the critical point, demonstrating a clean transition with standard critical dynamics and no extended Griffiths region. The results show that quenched disorder induces non-universal slow dynamics for small q, while larger values of q suppress the disorder-driven effects, restoring standard DP-like criticality. This transition underscores the role of lattice geometry and disorder strength in shaping nonequilibrium phase transitions.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcpxs.2025.1620260</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcpxs.2025.1620260</link>
        <title><![CDATA[Network modelling in analysing cyber-related graphs]]></title>
        <pubdate>2025-09-16T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Vesa Kuikka</author><author>Lauri Pykälä</author><author>Tuomas Takko</author><author>Kimmo K. Kaski</author>
        <description><![CDATA[To improve the resilience of the computer network infrastructure against cyber attacks or causal influences and find ways to mitigate their impact, we need to understand their structure and dynamics. Here, we propose a novel network-based influence-spreading modelling approach to investigate event trajectories or paths in attack and causal graphs with directed, weighted, cyclic and/or acyclic paths. In our model, we can perform probabilistic analyses that extend beyond traditional methods to visualise cyber-related graphs. The model uses a probabilistic method to combine paths that join within the graph. This analysis includes vulnerabilities, services, and exploitabilities. To demonstrate the applicability of our model, we present three cyber-related use cases: two attack graphs and one causal graph. This model can serve cyber analysts as a tool to produce quantitative metrics for prioritising tasks, summarising statistics, or analysing large-scale graphs.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcpxs.2025.1666594</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcpxs.2025.1666594</link>
        <title><![CDATA[Modified training drills in improving the dribbling agility of futsal athletes]]></title>
        <pubdate>2025-09-15T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Ma. Jan Donna G. Marcojos</author><author>Mylene P. Labastida</author><author>Jade Rona S. Soriano</author><author>Jennifer Degracia</author><author>Marinila T. Urboda</author><author>Louie P. Gula</author>
        <description><![CDATA[IntroductionFutsal has become increasingly popular over the years; hence, studies focusing on the integration of drills to improve the performance of athletes are relevant. This study investigates the impact of modified training drills in improving the dribbling agility of futsal athletes.MethodsA total of 13 athletes participated in the research. Their dribbling agility was classified based on the Test of Agility in Dribbling before and after the training duration. The training program, which follows the FITT principle, consisted of three sessions per week (following a TThS schedule) for 2 weeks, adapting their normal training days. Descriptive statistics (frequency counts, percentages, mean, and standard deviation) summarized the demographic characteristics and agility levels. A Wilcoxon signed-rank test was utilized to determine the significant difference between the pre-test and post-test results, as the participants were not randomly sampled. A Spearman's rank correlation test was used to test the association of demographic profiles with the levels of dribbling agility.ResultsDemographic analysis revealed that most participants were 14 years old, with a majority having a height between 140-149 cm and a weight of 40-49 kg. Descriptive statistics showed a significant improvement in agility performance, as the average agility time decreased from 24.51 s in the pre-test to 20.50 s in the post-test. After training, the participants’ dribbling agility levels shifted from predominantly ‘poor’ classifications to ‘average’, ‘good’, and ‘excellent’. Statistical analysis confirmed that this difference was statistically significant (p < 0.05). Further analysis revealed that weight has a significant association with agility performance, while age and height did not.DiscussionThe results support the hypothesis that modified training drills positively impact agility. The findings suggest that weight can be considered an important factor in evaluating the impact of agility training.ConclusionThe modified agility training program effectively enhanced dribbling agility among futsal athletes. The study suggests that future researchers may extend the training duration and control external factors. It is also recommended that weight be considered in planning and evaluating agility training programs.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcpxs.2025.1609467</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcpxs.2025.1609467</link>
        <title><![CDATA[Fragility in human progress. A perspective on governance, technology and societal resilience]]></title>
        <pubdate>2025-09-10T00:00:00Z</pubdate>
        <category>Opinion</category>
        <author>G.-Fivos Sargentis</author>
        <description></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcpxs.2025.1612142</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcpxs.2025.1612142</link>
        <title><![CDATA[The value of information in multi-scale feedback systems]]></title>
        <pubdate>2025-08-29T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Louisa Jane Di Felice</author><author>Ada Diaconescu</author><author>Payam Zahadat</author><author>Patricia Mellodge</author>
        <description><![CDATA[Complex adaptive systems (CAS) can be described as system of information flows that dynamically interact across scales to adapt and survive. CAS often consist of many components that work toward a shared goal and interact across different informational scales through feedback loops, leading to their adaptation. In this context, understanding how information is transmitted among system components and across scales becomes crucial for understanding the behavior of CAS. Shannon entropy, a measure of syntactic information, is often used to quantify the size and rarity of messages transmitted between objects and observers, but it does not measure the value that information has for each observer. For this, semantic and pragmatic information have been conceptualized as describing the influence on an observer’s knowledge and actions. Building on this distinction, we describe the architecture of multi-scale information flows in CAS through the concept of multi-scale feedback systems and propose a series of syntactic, semantic, and pragmatic information measures to quantify the value of information flows for adaptation. While the measurement of values is necessarily context-dependent, we provide general guidelines on how to calculate semantic and pragmatic measures and concrete examples of their calculation through four case studies: a robotic collective model, a collective decision-making model, a task distribution model, and a hierarchical oscillator model. Our results contribute to an informational theory of complexity that aims to better understand the role played by information in the behavior of multi-scale feedback systems.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcpxs.2025.1617092</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcpxs.2025.1617092</link>
        <title><![CDATA[Unsettling the settled: simple musings on the complex climatic system]]></title>
        <pubdate>2025-08-12T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Demetris Koutsoyiannis</author><author>George Tsakalias</author>
        <description><![CDATA[Our revisit of fundamental issues of climate challenges the notion and term of the “greenhouse effect”, and attempts a scientific reevaluation using minimal assumptions, such as Newton’s laws, maximum entropy and gas spectroscopy. It replaces terms like “greenhouse gas” with “radiatively active gas” (RAG) and “greenhouse effect” with “atmospheric radiative effect” (ARE). While ARE exists in several planets’ atmospheres, on Earth it is primarily driven by water vapor and clouds, with CO2 playing a minor role (especially anthropogenic CO2 which represents 4% of total emissions). Equilibrium thermodynamics, via entropy maximization or molecular collision simulation, leads to an isothermal atmosphere at about 250 K (the average temperature of the troposphere and stratosphere) irrespective of RAG presence or not. It is the troposphere’s 6.5 K/km temperature gradient (lapse rate), partly shaped by moist adiabatic processes, that drives the atmosphere away from this equilibrium and warms the surface to about 288 K on average, with ARE (mainly water vapor and clouds) contributing to the warming, but only when this gradient exists. The temperature gradient varies spatially and temporally and, since 1950, has weakened in the tropics and grown in the polar areas, resulting in a decrease of the surface equator-to-pole gradient, as expected in global warming conditions.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcpxs.2025.1612998</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcpxs.2025.1612998</link>
        <title><![CDATA[Traces of tricritical dynamics beyond SSB in finite-size systems undergoing second-order phase transition: the case of the 3D Ising model]]></title>
        <pubdate>2025-08-01T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Yiannis Contoyiannis</author><author>Stelios M. Potirakis</author><author>Stavros G. Stavrinides</author><author>Michael P. Hanias</author><author>Pericles Papadopoulos</author><author>Niki-Lina Matiadou</author>
        <description><![CDATA[In finite-size thermal systems that exhibit second-order phase transition, the fluctuations of the order parameter ϕn obey type I intermittent dynamics at their pseudocritical temperature Tpc. Moreover, as recently demonstrated, spontaneous symmetry breaking (SSB) is gradually completed as temperature is reduced until reaching an SSB completion temperature, TSSB. Within this temperature zone, ϕn obey the dynamics of critical intermittency. This behavior has also been observed in pre-seismic fracture-induced electromagnetic emissions (FEME) of the MHz band—a real-world finite-size system undergoing a second-order phase transition. Interestingly, MHz FEME has recently been found to consistently present indications of tricritical dynamics after the SSB. We examine here whether this could also be true for a finite-size thermal system. We conduct a numerical experiment for the 3D Ising model at different temperatures by gradually reducing temperature beyond SSB and analyze order parameter fluctuations using the method of critical fluctuations (MCF) and a recently introduced wavelet-based method for detecting scaling behavior in noisy experimental data. Our results reveal that power-laws still exist within a very narrow zone of temperatures right after SSB completion for the 3D Ising model. These power-laws are shown to be compatible with another form of intermittency that determines the dynamics of the order parameter fluctuations close to the Griffiths tricritical point. As a possible interpretation of this finding, we suggest that our results imply that 3D Ising presents, just below TSSB, an imprint approaching the Griffiths tricritical point from the second-order phase transition line.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcpxs.2025.1630050</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcpxs.2025.1630050</link>
        <title><![CDATA[RETRACTED: Toward a thermodynamic theory of evolution: a theoretical perspective on information entropy reduction and the emergence of complexity]]></title>
        <pubdate>2025-07-31T00:00:00Z</pubdate>
        <category>Hypothesis and Theory</category>
        <author>Carlos Mendoza Montano</author>
        <description><![CDATA[Traditional evolutionary theory explains adaptation and diversification through random mutation and natural selection. While effective in accounting for trait variation and fitness optimization, this framework provides limited insight into the physical principles underlying the spontaneous emergence of complex, ordered systems. A complementary theory is proposed: that evolution is fundamentally driven by the reduction of informational entropy. Grounded in non-equilibrium thermodynamics, systems theory, and information theory, this perspective posits that living systems emerge as self-organizing structures that reduce internal uncertainty by extracting and compressing meaningful information from environmental noise. These systems increase in complexity by dissipating energy and exporting entropy, while constructing coherent, predictive internal architectures, fully in accordance with the second law of thermodynamics. Informational entropy reduction is conceptualized as operating in synergy with Darwinian mechanisms. It generates the structural and informational complexity upon which natural selection acts, whereas mutation and selection refine and stabilize those configurations that most effectively manage energy and information. This framework extends previous thermodynamic models by identifying informational coherence, not energy efficiency, as the primary evolutionary driver. Recently formalized metrics, Information Entropy Gradient (IEG), Entropy Reduction Rate (ERR), Compression Efficiency (CE), Normalized Information Compression Ratio (NICR), and Structural Entropy Reduction (SER), provide testable tools to evaluate entropy-reducing dynamics across biological and artificial systems. Empirical support is drawn from diverse domains, including autocatalytic networks in prebiotic chemistry, genome streamlining in microbial evolution, predictive coding in neural systems, and ecosystem-level energy-information coupling. Together, these examples demonstrate that informational entropy reduction is a pervasive, measurable feature of evolving systems. While this article presents a theoretical perspective rather than empirical results, it offers a unifying explanation for major evolutionary transitions, the emergence of cognition and consciousness, the rise of artificial intelligence, and the potential universality of life. By embedding evolution within general physical laws that couple energy dissipation to informational compression, this framework provides a generative foundation for interdisciplinary research on the origin and trajectory of complexity.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcpxs.2025.1604132</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcpxs.2025.1604132</link>
        <title><![CDATA[Exploring interconnections among atoms, brain, society, and cosmos with network science and explainable machine learning]]></title>
        <pubdate>2025-06-30T00:00:00Z</pubdate>
        <category>Hypothesis and Theory</category>
        <author>Daniele Caligiore</author><author>Anna Monreale</author><author>Giulio Rossetti</author><author>Angela Bongiorno</author><author>Giuseppe Fisicaro</author>
        <description><![CDATA[This paper presents a methodology combining Network Science (NS) and Explainable Machine Learning (XML) that could hypothetically uncover shared principles across seemingly disparate scientific domains. As an example, it presents how the approach could be applied to four fields: materials science, neuroscience, social science, and cosmology. The study focuses on criticality, a phenomenon associated with the transition of complex systems between states, characterized by sudden and significant behavioral shifts. By proposing a five-step methodology—ranging from relational data collection to cross-domain analysis with XML—the paper offers a hypothetical framework for potentially identifying criticality-related features in these fields and transferring insights across disciplines. The results of domains cross-fertilization could support practical applications, such as improving neuroprosthetics and brain-machine interfaces by leveraging criticality in materials science and neuroscience or developing advanced materials for space exploration. The parallels between neural and social networks could deepen our understanding of human behavior, while studying cosmic and social systems may reveal shared dynamics in large-scale, interconnected structures. A key benefit could be the possibility of using transfer learning, that is XML models trained in one domain might be adapted for use in another with limited data. For instance, if common aspects of criticality in neuroscience and cosmology are identified, an algorithm trained on brain data could be repurposed to detect critical states in cosmic systems, even with limited cosmic data. This interdisciplinary approach advances theoretical frameworks and fosters practical innovations, laying the groundwork for future research that could transform our understanding of complex systems across diverse scientific fields.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcpxs.2025.1590952</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcpxs.2025.1590952</link>
        <title><![CDATA[Cooperative behavior in pre-state societies: an agent based approach to the Axum civilization]]></title>
        <pubdate>2025-06-18T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Riccardo Vasellini</author><author>Gilda Ferrandino</author><author>Luisa Sernicola</author><author>Daniele Vilone</author><author>Chiara Mocenni</author>
        <description><![CDATA[IntroductionThis study intends to test the hypothesis that, contrary to traditional interpretation, the social structure of the polity of Aksum–especially in its early stages–was not characterized by a vertical hierarchy with highly centralized administrative power, and that the leaders mentioned in the few available inscriptions were predominantly ritual leaders with religious rather than coercive political authority. This hypothesis, suggested by the available archaeological evidence, is grounded in Charles Stanish's model, which posits that pre-state societies could achieve cooperative behavior without the presence of coercive authority.MethodsUsing agent-based modeling applied to data inspired by the Aksum civilization, we examine the dynamics of cooperation in the presence and absence of a Public Goods Game.ResultsResults show that while cooperative behavior can emerge in the short term without coercive power, it may not be sustainable over the long term, suggesting a need for centralized authority to foster stable, complex societies.DiscussionThese findings provide insights into the evolutionary pathways that lead to state formation and complex social structures.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcpxs.2025.1575210</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcpxs.2025.1575210</link>
        <title><![CDATA[Spectrum optimization of dynamic networks for reduction of vulnerability against adversarial resonance attacks]]></title>
        <pubdate>2025-05-30T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Alp Sahin</author><author>Nicolas Kozachuk</author><author>Rick S. Blum</author><author>Subhrajit Bhattacharya</author>
        <description><![CDATA[Resonance is a well-known phenomenon that happens in systems with second order dynamics. In this paper, we address the fundamental question of making a network robust to signal being periodically pumped into it at or near a resonant frequency by an adversarial agent with the aim of saturating the network with the signal. Toward this goal, we develop the notion of network vulnerability, which is measured by the expected resonance amplitude on the network under a stochastically modeled adversarial attack. Assuming a second order dynamics model based on the network graph Laplacian and a known stochastic model for the adversarial attack, we propose two methods for minimizing the network vulnerability–one through direct optimization of the spectrum of the network graph, and another through optimization of an auxiliary network graph attached to the main network. We provide theoretical foundations for these methods as well as extensive numerical results analyzing the effectiveness of both methods in reducing the network vulnerability.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcpxs.2025.1563687</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcpxs.2025.1563687</link>
        <title><![CDATA[On cyclostationary linear inverse models: a mathematical insight and implication]]></title>
        <pubdate>2025-04-22T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Justin Lien</author><author>Yan-Ning Kuo</author><author>Hiroyasu Ando</author><author>Shoichiro Kido</author>
        <description><![CDATA[Cyclostationary linear inverse models (CS-LIMs) are advanced data-driven techniques for extracting first-order time-dependent dynamics and random forcing information from cyclostationary observational data. This study focuses on the mathematical perspective of CS-LIMs and presents two variants, namely, e-CS-LIM and l-CS-LIM. The e-CS-LIM, improved from the original CS-LIM, constructs the first-order dynamics through the interval-wise application of the stationary LIM (ST-LIM), capturing the integrated effect of each interval where similar cyclostationary dependencies are present. This approach provides robustness against noise but is affected by the Nyquist issue, similar to the ST-LIM. The l-CS-LIM, on the other hand, estimates the time-dependent Jacobian of the underlying system. Although more sensitive to noise, this method is free from the Nyquist issue. Numerical experiments demonstrate that both CS-LIM variants effectively capture the temporal structure of the underlying system using synthetic observational data. Moreover, when applied to real-world ENSO data, CS-LIMs yield consistent results that align well with the observations and current El Niño–Southern Oscillation (ENSO) understanding.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcpxs.2025.1569364</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcpxs.2025.1569364</link>
        <title><![CDATA[Conflict and cooperation: a systematic exploration]]></title>
        <pubdate>2025-04-16T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Leonardo Castro-Gonzalez</author><author>Rodrigo Leal-Cervantes</author><author>Ekkehard Ernst</author>
        <description><![CDATA[Economic cooperation is inherently dynamic, with agents adjusting the frequency, mechanisms, and intensity of their interactions over time. When scaling this behaviour to a large number of agents, we obtain a complex cooperation network where interaction dynamics influence the system’s macro-state. This study looks into how network topologies impact the survival of economic cooperation. Specifically, we explore the effect of topologies in sustaining cooperation through the survival of a “saving trait”, a feature that promotes cooperative interactions among agents. In our model, similar to a Stag Hunt (SH) game with memory, agents adapt their saving traits based on the profitability of past interactions with others. We simulate the game on seven distinct network structures sourced from the public repository Netzschleuder and analyse the robustness of the saving trait under topological shocks. From the seven studied networks, we recover the two equilibria dynamics from the SH game for four of them. For the remaining three, we obtain stable mixed states. These findings show that network topology affects the survival of the saving trait and its vulnerability to widespread topological shocks (over 25% of edges shifted or added). This work contributes to the interdisciplinary effort to understand economic cooperation by integrating insights from network science, game theory, and the social sciences.]]></description>
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