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The advent of Large Language Models (LLMs) offers to transform research across natural and social sciences, offering new paradigms for understanding complex systems. In particular, Generative Agent-Based Models (GABMs), which integrate LLMs to simulate human behavior, have attracted increasing public attention due to their potential to model complex interactions in a wide range of artificial environments. This paper briefly reviews the disruptive role LLMs are playing in fields such as network science, evolutionary game theory, social dynamics, and epidemic modeling. We assess recent advancements, including the use of LLMs for predicting social behavior, enhancing cooperation in game theory, and modeling disease propagation. The findings demonstrate that LLMs can reproduce human-like behaviors, such as fairness, cooperation, and social norm adherence, while also introducing unique advantages such as cost efficiency, scalability, and ethical simplification. However, the results reveal inconsistencies in their behavior tied to prompt sensitivity, hallucinations and even the model characteristics, pointing to challenges in controlling these AI-driven agents. Despite their potential, the effective integration of LLMs into decision-making processes -whether in government, societal, or individual contexts- requires addressing biases, prompt design challenges, and understanding the dynamics of human-machine interactions. Future research must refine these models, standardize methodologies, and explore the emergence of new cooperative behaviors as LLMs increasingly interact with humans and each other, potentially transforming how decisions are made across various systems., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.)

Risk cascading propagation research mostly focuses on complex theoretical networks. Recently, the vulnerability of international chip supply has increased notably, and it is strategically important to explore how shortage risk affects the emergence dynamics of the real chip trade systems. This study abstracts the global chip trade relationship data for 2009-2021 into multiple asymmetrically weighted networks. Using macro-network and micro-node indicators, we explore the topological traits of international chip trade networks and their evolutionary laws. Accordingly, we propose risk cascading propagation models driven by node failure and edge restraint and further innovate to open up the research paradigm of focused-edge networks. Furthermore, a community infection-driven risk cascading propagation mechanism that incorporates community risk absorption capacity is introduced, considering both the propagation attenuation effects and the trade dependency degree. A multi-dimensional dynamic perspective reveals the hidden systemic risks in international chip trade. The main results are as follows: first, international chip trade networks are highly connected and cohesive, consistent with small-world characteristics. Second, the proportion of economies that collapse because of supply shortage risk shocks increases with the impact coefficient α / β . The dominant power in chip crisis propagation has shifted from Europe and America to Asia, and mainland China's risk penetration capacity has enhanced significantly. Third, focused-edge networks conform to a multi-hub radiation pattern. Before the COVID-19 pandemic, the degree of control and spillover effects of chip supply shortages in hub economies on the international trade was increasing progressively. Fourth, an increase in absorption capacity λ or attenuation factor γ consistently leads to a decline in avalanche scale, with λ exhibiting critical thresholds. These findings will help policymakers pursue efficient management strategies for chip trade, thereby improving supply stability and security., (© 2024. The Author(s).)

Feedback loops between strategies and the environment are commonly observed in socio-ecological, evolution-ecological, and psychology-economic systems. However, the impact of mutations in these feedback processes is often overlooked. This study proposes a novel model that integrates the public goods game with environmental feedback, considering the presence of mutations. In our model, the enhancement factor of the public goods game combines positive and negative incentives from the environment. By employing replicator-mutator (RM) equations, we provide an objective understanding of the system's evolutionary state, focusing on identifying conditions that foster cooperation and prevent the tragedy of the commons. Specifically, mutations play a crucial role in the RM dynamics, leading to the emergence of an oscillatory tragedy of the commons. By verifying the Hopf bifurcation condition, we establish the existence of a stable limit cycle, providing valuable insights into sustained oscillation strategies. Moreover, the feedback mechanism inherent in the public goods game model offers a fresh perspective on effectively addressing the classic dilemma of the tragedy of the commons., (© 2024 Author(s). Published under an exclusive license by AIP Publishing.)

Complex networks are widespread in real-world environments across diverse domains. Real-world networks tend to form spontaneously through interactions between individual agents. Inspired by this, we design an evolutionary game model in which agents participate in a prisoner's dilemma game (PDG) with their neighboring agents. Agents can autonomously modify their connections with neighbors using reinforcement learning to avoid unfavorable environments. Interestingly, our findings reveal some remarkable results. Exploiting reinforcement learning-based adaptive networks improves cooperation when juxtaposed with existing PDGs performed on homogeneous networks. At the same time, the network's topology evolves from homogeneous to heterogeneous states. This change occurs as players gain experience from past games and become more astute in deciding whether to join PDGs with their current neighbors or disconnect from the least profitable neighbors. Instead, they seek out more favorable environments by establishing connections with second-order neighbors with higher rewards. By calculating the degree distribution and modularity of the adaptive network in a steady state, we confirm that the adaptive network follows a power law and has a clear community structure, indicating that the adaptive network is similar to networks in the real world. Our study reports a new phenomenon in evolutionary game theory on networks. It proposes a new perspective to generate scale-free networks, which is generating scale-free networks by the evolution of homogeneous networks rather than typical ways of network growth and preferential connection. Our results provide new aspects to understanding the network structure, the emergence of cooperation, and the behavior of actors in nature and society., (© 2024 Author(s). Published under an exclusive license by AIP Publishing.)

While prosocial exclusion has been proposed as a mechanism to maintain cooperation in one-shot social dilemma games, the evolution of prosocial peer exclusion in response to the threat of antisocial peer exclusion, particularly in structured populations, remains insufficiently understood. In this study, we employ an extended spatial public goods game to investigate the evolution of prosocial peer exclusion and its impact on cooperation in the presence of both prosocial and antisocial peer exclusion. Our model encompasses four primary strategies: traditional cooperation and defection, prosocial peer exclusion targeting defectors, and antisocial peer exclusion targeting cooperators. Our findings illuminate that the presence of antisocial peer exclusion significantly disrupts network reciprocity and suppresses cooperation. However, when coexisting with prosocial peer exclusion, it does not undermine the latter's efficacy in upholding cooperation, except in scenarios with low exclusion costs Unlike the cooperation-sustaining cyclic dominance pattern observed in the exclusive presence of prosocial peer exclusion, the co-presence of prosocial and antisocial peer exclusion gives rise to more intricate pathways for maintaining cooperation. These pathways include cyclic dominance involving traditional cooperation, prosocial peer exclusion, and antisocial peer exclusion, or a similar pattern involving traditional defection and the two exclusion strategies, or even cyclic dominance among all four strategies. In essence, our study enhances the theoretical framework concerning the effectiveness of the prosocial exclusion strategy, contributing to a more comprehensive understanding of its dynamics., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

The existence of neutral species carries profound ecological implications that warrant further investigation. In this paper, we study the impact of neutral species on biodiversity in a spatial tritrophic system of cyclic competition, in which the neutral species are identified as the fourth species that may affect the competition process of the other three species under the rock-paper-scissors (RPS) rule. Extensive simulations showed that neutral species can promote coexistence in a high mobility regime within the system. When coexistence occurs, we found that the state can be maintained by two mechanisms: Species can either (i) adhere to traditional RPS rule or (ii) form patches to resist invasion. Our findings might aid in understanding the impact of neutral species on biodiversity in ecosystems.

We investigate evolving dynamics of cyclically competing species on spatially extended systems with considering a specific region, which is called the "wildlife refuge," one of the institutional ways to preserve species biodiversity. Through Monte-Carlo simulations, we found that the refuge can play not groundbreaking but an important role in species survival. Species coexistence is maintained at a moderate mobility regime, which traditionally leads to the collapse of coexistence, and eventually, the extinction is postponed depending on the competition rate rather than the portion of the refuge. Incorporating the extinction probability and Fourier transform supported our results in both stochastic and analogous ways. Our findings may provide valuable evidence to assist fields of ecological/biological sciences in understanding the presence and construction of refuges for wildlife with associated effects on species biodiversity.

Recent outbreaks of novel infectious diseases (e.g., COVID-19, H2N3) have highlighted the threat of pathogen transmission, and vaccination offers a necessary tool to relieve illness. However, vaccine efficacy is one of the barriers to eradicating the epidemic. Intuitively, vaccine efficacy is closely related to age structures, and the distribution of vaccine efficacy usually obeys a Gaussian distribution, such as with H3N2 and influenza A and B. Based on this fact, in this paper, we study the effect of vaccine efficacy on disease spread by considering different age structures and extending the traditional susceptible-infected-recovery/vaccinator(SIR/V) model with two stages to three stages, which includes the decision-making stage, epidemic stage, and birth-death stage. Extensive numerical simulations show that our model generates a higher vaccination level compared with the case of complete vaccine efficacy because the vaccinated individuals in our model can form small and numerous clusters slower than that of complete vaccine efficacy. In addition, priority vaccination for the elderly is conducive to halting the epidemic when facing population ageing. Our work is expected to provide valuable information for decision-making and the design of more effective disease control strategies., Competing Interests: We declare we have no competing interests., (© 2022 Elsevier Ltd. All rights reserved.)

High-throughput metabolomics can be used to investigate the therapeutic targets and metabolic mechanisms of traditional Chinese medicine (TCM) formulae, which have multiple targets in disease therapy, but it is a great challenge to explore their mechanism of action. Huaxian capsule (HXC) is a classical formula in TCM that has therapeutic effects on a sepsis-associated Qi deficiency and blood stasis syndrome (SQBS). However, its targets and metabolic mechanisms need more investigation. To investigate the therapeutic effects of HXC in the treatment of SQBS and elucidate the potential mechanism, we used a high-throughput metabolomics strategy based on the ultraperformance liquid chromatography/mass spectrometry combined with chemometrics to analyze and identify differential metabolites and pathways. The pathological examination of organs and biochemical indices was also performed to verify the successful establishment of the rat model and protective effects of HXC. Pathological symptoms and biochemical indicators of SQBS rats were reversed by the HXC treatment. A total of 24 potential biomarkers were identified to indicate the difference between the control and model groups; they were closely associated with ten metabolic pathways and regulated by the HXC administration. From the pathway analysis, we further understood the protective activity of HXC against SQBS, which affected amino acid metabolism, molecular transport, small molecule biochemistry and cell signaling as well as vitamin and mineral metabolism. In conclusion, HXC protects against SQBS by modulating the metabolic biomarkers and functional pathways., Competing Interests: The authors declare no competing interests., (This journal is © The Royal Society of Chemistry.)

In this paper, we consider an asymmetric reproductive ability on interdependent networks and investigate how this setting affects the evolution of cooperation. In detail, players decide to update their strategies at each step on main network (network B), while for sub network (network A), players update their strategies with a fixed probability p. Obviously, the system restores the traditional case when p = 1, where cooperation can survive by interdependent network reciprocity. And our asymmetric set-up comes into play when p < 1. Numerical simulation results show that our asymmetric coupling will hinder the overall cooperation level for small p. In detail, the introduction of asymmetric reproductive ability urges the formation of symmetry breaking and further weakens the positive impact by location synchronous effect. And the root cause is entirely distinct situation of utility differences on two networks. These observations further demonstrate a class of phenomena on interdependent networks that it would have catastrophic consequences on one network even if a unrelated change only occurs seemingly on another network.

High-throughput metabolic profiling technology has been used for biomarker discovery and to reveal underlying metabolic mechanisms. Sepsis-induced myocardial dysfunction (SMD) is a common complication in sepsis patients, and severely affects their quality of life. However, the pathogenesis of SMD is currently unclear, and there has been inadequate basic research. In this study, metabolic profiling was explored by liquid chromatography/mass spectrometry (LC/MS) combined with chemometrics and bioinformatic analysis. The global metabolome data were analyzed using chemometrics analysis including principal component analysis and partial least squares discriminant analysis for significant metabolites. Variable importance for projection values obtained utilizing a pattern recognition method were used to identify potential biomarkers. The differential metabolites were putatively identified using the metabolome database and bioinformatics analysis was conducted via Ingenuity Pathway Analysis (IPA) to predict the likely functional alterations. In total, 21 differential metabolites were found in SMD and these were involved in phenylalanine, tyrosine and tryptophan biosynthesis, arachidonic acid metabolism, glycine, serine and threonine metabolism, and so on. The analysis revealed that the metabolites were strongly related to molecular transport, and small molecule biochemistry metabolic pathways. The present study indicates that high-throughput metabolic profiling, combined with chemometrics and a bioinformatic platform, can reveal the likely functional alterations in disease and could provide more precise and credible information in the basic research of disease pathogenesis., Competing Interests: The authors declare no competing interests., (This journal is © The Royal Society of Chemistry.)