Coevolutionary Model Research Articles

Evolutionary dynamics in spatial public goods games with environmental feedbacks.

Collective actions aimed at achieving goals such as resource sustainability and environmental protection often face conflicting interests between individuals and groups. These social dilemmas can be modeled using public goods games and collective risk dilemmas. However, in reality, multiple generations share a common pool of resources, leading to high costs for today's overexploitation that impacts future generations' welfare. This delayed effect creates a multigenerational conflict. To address this, we develop a coupled social-ecological coevolutionary model by establishing a relationship between individual payoffs and regional environmental quality. Our goal is to study how cooperative behaviors spread in a public goods game with environmental feedback and to identify the factors influencing this spread. We achieve this by examining the mechanisms behind certain phases and phase transitions, monitoring the spatial distribution of strategies, and assessing the environmental quality of all regions. Our findings reveal some counterintuitive results. For instance, despite cooperators' ability to enhance the environment, the overall level of cooperation in the system sometimes decreases. This is linked to cooperative clusters being invaded by defectors within the clusters' cracks. Additionally, the destructive power of defection and the cost of cooperation have more complex effects on the system.

Discontinuous Directional Wetting Transitions in Polymeric Droplets on the Heterogeneous Microcavity Surface.

The wetting transition behaviors of polymeric droplets on microcavity surfaces are familiar and play a vital role in micromanufacturing, microfluidics, and printing industries. Despite previous research indicating that microcavity surfaces can precisely control the droplet wetting state, the understanding of the complex effects of droplet spreading, surface morphology, and property of polymeric droplet on wetting transitions remains incomplete. The air-liquid interfaces (ALIs) typically arise from the entrapped air beneath the droplet on microcavity surfaces, adopting a metastable wetting state caused by either bubble escape or dissolution. Here, we discovered a previously unobserved phenomenon: the time-dependent evolution of regularly arranged ALIs with discontinuous wetting states, along with the pronounced directional wetting transitions from the Cassie-Baxter state to the Wenzel state upon deposition of polymeric droplets on heterogeneous microcavity surfaces. The durability of ALIs in microcavities was quantified, illustrating that the wetting transitions associated with droplet spreading processes obeyed power laws. By integrating the wetting theory and the viscoelastic effect of polymeric droplet, we have proposed a phenomenological coevolution model for wetting transitions that emphasizes the synergistic interaction between adjacent microcavities, resulting in the observed cluster evolution behavior of ALIs within droplets. Our study holds great significance in guiding soft manufacturing techniques utilizing internal ALIs as templates. The established mechanism opens up avenues for investigating the intricate wetting phenomena of polymeric droplets on microtextured substrates.

Co-evolutionary decoupling in EMNEs’ internationalisation: A Chinese bank's journey of drifting apart from the US-led global financial system

We develop a co-evolutionary decoupling model to explain the decoupling process and its contributors, based on a Chinese bank's dynamic interdependency with the US-led global financial system. This decoupling is the joint outcome of the firm's strategic adaptations in response to economic, institutional, and geopolitical environmental challenges. By incorporating co-evolution and international relations perspectives into international business research, we reveal how and when non-economic environmental factors can decisively hinder a firm's, especially an EMNE's, internationalisation. This study provides new firm-level evidence on the ongoing Sino-US decoupling process, extending our understanding of international business through co-evolution and international relations perspectives.

How relevant is the relationship between ostiole size and wasp head shape in the Ficus-Agaonidae mutualistic interaction?

The mutualism between Ficus species (Moraceae) and their pollinating wasps (Agaonidae) is a widely recognized coevolutionary model. In Ficus species from the Paleotropic, it has been determined that the ostiole acts as a morphological filter that affects the head dimensions of pollinating female wasps. Here, for the first time, the allometric relationship between ostiole size (diameter and length) and the shape of the head (length/width) of pollinating wasps is quantitatively explored in 6 Neotropical Ficus species (3 sect. Americanae and 3 sect. Pharmacosycea). In the case of sect. Americanae, wasp head shape was significantly correlated only with ostiole length, while in sect. Pharmacosycea both ostiole variables were correlated with head shape. The ordination analysis (NMDS) clearly reflected associations of these traits in species for both sections. The results support what has been interpreted in previous studies as reciprocal evolution between the analyzed traits, which contribute, along with other morphological and ecological traits, to the specificity between Ficus species and their pollinating wasps.

A framework for evaluating tool support for co-evolution of modeling languages, tools and models

AbstractWe present a framework for evaluating language workbenches’ capabilities for co-evolution of graphical modeling languages, modeling tools and models. As with programming, maintenance tasks such as language refinement and enhancement typically account for more work than the initial development phase. Modeling languages have the added challenge of keeping tools and existing models in step with the evolving language. As domain-specific modeling languages and tools have started to be used widely, thanks to reports of significant productivity improvements, some language workbench users have indeed reported problems with co-evolution of tools and models. Our tool-agnostic evaluation framework aims to cover changes across the whole language definition: the abstract syntax, concrete syntax, and constraints. Change impact is assessed for knock-on effects within the language definition, the modeling tools, semantics via generators, and existing models. We demonstrate the viability of the framework by evaluating MetaEdit+, EMF/Sirius and Jjodel, providing a detailed evaluation process for others to repeat with their tools. The results of the evaluation show differences among the tools: from editors not opening correctly or at all, through highlighting of items requiring manual intervention, to fully automatic updates of languages, models and editors. We call for industry to evaluate their tool choices with the framework, tool developers to extend their tool support for co-evolution, and researchers to refine the evaluation framework and evaluations presented.

Coevolution promotes the coexistence of Tasmanian devils and a fatal, transmissible cancer.

Emerging infectious diseases threaten natural populations, and data-driven modeling is critical for predicting population dynamics. Despite the importance of integrating ecology and evolution in models of host-pathogen dynamics, there are few wild populations for which long-term ecological datasets have been coupled with genome-scale data. Tasmanian devil (Sarcophilus harrisii ) populations have declined range-wide due to devil facial tumor disease (DFTD), a fatal transmissible cancer. Although early ecological models predicted imminent devil extinction, diseased devil populations persist at low densities, and recent ecological models predict long-term devil persistence. Substantial evidence supports evolution of both devils and DFTD, suggesting coevolution may also influence continued devil persistence. Thus, we developed an individual-based, eco-evolutionary model of devil-DFTD coevolution parameterized with nearly two decades of devil demography, DFTD epidemiology, and genome-wide association studies. We characterized potential devil-DFTD coevolutionary outcomes and predicted the effects of coevolution on devil persistence and devil-DFTD coexistence. We found a high probability of devil persistence over 50 devil generations (100 years) and a higher likelihood of devil-DFTD coexistence, with greater devil recovery, than predicted by previous ecological models. These novel results add to growing evidence for long-term devil persistence and highlight the importance of eco-evolutionary modeling for emerging infectious diseases.

Diversity and Host Specificity of Avian Haemosporidians in an Afrotropical Conservation Region.

Afrotropical regions have high bird diversity, yet few studies have attempted to unravel the prevalence of avian haemosporidia in conservation areas. The diversity and host specificity of parasites in biodiversity hotspots is crucial to understanding parasite distribution and potential disease emergence. We test the hypothesis that biodiverse regions are associated with highly diverse parasites. By targeting the cytochrome b (Cytb) gene, we molecularly screened 1035 blood samples from 55 bird species for avian haemosporidia infections to determine its prevalence and diversity on sites inside and adjacent to the Kruger National Park. Overall infection prevalence was 28.41%. Haemoproteus, Leucocytozoon, and Plasmodium presented prevalences of 17.39%, 9.24%, and 4.64%, respectively. One hundred distinct parasite lineages were detected, of which 56 were new lineages. Haemoproteus also presented the highest diversity compared to Leucocytozoon and Plasmodium with varying levels of specificity. Haemoproteus lineages were found to be specialists while Plasmodium and Leucocytozoon lineages were generalists. We also found a positive relationship between avian host diversity and parasite diversity, supporting an amplification effect. These findings provide insight data for host-parasite and co-evolutionary relationship models.

Understanding epistatic networks in the B1 β-lactamases through coevolutionary statistical modeling and deep mutational scanning.

Throughout evolution, protein families undergo substantial sequence divergence while preserving structure and function. Although most mutations are deleterious, evolution can explore sequence space via epistatic networks of intramolecular interactions that alleviate the harmful mutations. However, comprehensive analysis of such epistatic networks across protein families remains limited. Thus, we conduct a family wide analysis of the B1 metallo-β-lactamases, combining experiments (deep mutational scanning, DMS) on two distant homologs (NDM-1 and VIM-2) and computational analyses (in silico DMS based on Direct Coupling Analysis, DCA) of 100 homologs. The methods jointly reveal and quantify prevalent epistasis, as ~1/3rd of equivalent mutations are epistatic in DMS. From DCA, half of the positions have a >6.5 fold difference in effective number of tolerated mutations across the entire family. Notably, both methods locate residues with the strongest epistasis in regions of intermediate residue burial, suggesting a balance of residue packing and mutational freedom in forming epistatic networks. We identify entrenched WT residues between NDM-1 and VIM-2 in DMS, which display statistically distinct behaviors in DCA from non-entrenched residues. Entrenched residues are not easily compensated by changes in single nearby interactions, reinforcing existing findings where a complex epistatic network compounds smaller effects from many interacting residues.

Herd behavior identification based on coevolution in human–machine collaborative multi-stage large group decision-making

As the scale of multi-stage large group decision-making (LGDM) continues to expand, the possibility of low-contribution individuals exhibiting herd behavior also increases, potentially leading to the phenomenon of “fishing in troubled waters.” This may obstruct the speed of consensus reaching while generating no valuable opinions, which is a topic worthy of exploration. Considering that humans are easily influenced by interests, the employment of machine intelligence to objectively identify herd behavior is more appropriate. In this context, a herd behavior identification method based on behavioral characteristics clustering from the perspective of human–machine collaboration is herein proposed. First, from the human side, an opinion–social network coevolution model is constructed to simulate the consensus reaching process (CRP) of the expert group. Then, the group is clustered into three subgroups in consideration of behavior that encompasses both opinion changes and trust relationship changes. Based on this, the low-contribution cluster with a herd behavior pattern can be optimized from the machine side. Through simulation experiments, it is verified that herd behavior management significantly accelerates the consensus-reaching speed under the premise of having minimal impact on the decision-making results. In general terms, this study is the first to propose the concept of herd behavior and provides a solution to manage it from a new perspective, which is suitable for application in multi-stage LGDM scenarios.

Pulse instabilities can shape virus-immune coevolution

Adaptive immune systems engage in an arms race with evolving viruses, trying to generate new responses to viral strains that continually move away from the set of genetically varying strains that have already elicited a functional immune response. It has been argued that this dynamical process can lead to a propagating pulse of an ever-changing viral population and concomitant immune response. Here, we introduce a new stochastic model of viral-host coevolution, taking into account finite-sized host populations and varying processes of immune “forgetting”. Using both stochastic and deterministic calculations, we show that there is indeed a possible pulse solution, but for a large host population size and for finite memory capacity, the pulse becomes unstable to the generation of new infections in its wake. This instability leads to an extended endemic infection pattern, demonstrating that the population-level behavior of virus infections can exhibit a wider range of behavior than had been previously realized. Published by the American Physical Society 2024

The Hierarchical Coevolutionary Units of Ecological Networks.

In ecological networks, cohesive groups of species may shape the evolution of interactions, serving as coevolutionary units. Ranging across network scales, from motifs to isolated components, elucidating which cohesive groups are more determinant for coevolution remains a challenge in ecology. We address this challenge by integrating 376 empirical mutualistic and antagonistic networks and coevolutionary models. We identified cohesive groups at four network scales containing a significant proportion of potential direct coevolutionary effects. Cohesive groups displayed hierarchical organisation, and potential coevolutionary effects overflowing lower-scale groups were contained by higher-scale groups, underscoring the hierarchy's impact. However, indirect coevolutionary effects blurred group boundaries and hierarchy, particularly under strong selection from ecological interactions. Thus, under strong selection, indirect effects render networks themselves, and not cohesive groups, as the likely coevolutionary units of ecological systems. We hypothesise hierarchical cohesive groups to also shape how other forms of direct and indirect effects propagate in ecological systems.

Viral plasticity facilitates host diversity in challenging environments

The antagonistic coevolution of microbes and viruses influences fundamentally the diversity of microbial communities. Information on how environmental variables interact with emergent defense-counterdefense strategies and community composition is, however, still scarce. Following biological intuition, diversity should increase with improved growth conditions, which offset evolutionary costs; however, laboratory and regional data suggest that microbial diversity decreases in nutrient-rich conditions. Moreover, global oceanic data show that microbial and viral diversity decline for high latitudes, although the underlying mechanisms are unknown. This article addresses these gaps by introducing an eco-evolutionary model for bacteria-virus antagonistic coevolution. The theory presented here harmonizes the observations above and identifies negative density dependence and viral plasticity (dependence of virus performance on host physiological state) as key drivers: environmental conditions selecting for slow host growth also limit viral performance, facilitating the survival of a diverse host community; host diversity, in turn, enables viral portfolio effects and bet-hedging strategies that sustain viral diversity. From marine microbes to phage therapy against antibiotic-resistant bacteria or cancer cells, the ubiquity of antagonistic coevolution highlights the need to consider eco-evolutionary interactions across a gradient of growth conditions.

What does not kill Gaia makes her stronger: impacts of external perturbations on biosphere evolution

ABSTRACT Life on Earth has experienced numerous upheavals over its approximately 4 billion year history. In previous work, we have discussed how interruptions to stability lead, on average, to increases in habitability over time, a tendency we called entropic Gaia. Here, we continue this exploration, working with the Tangled Nature Model of co-evolution, to understand how the evolutionary history of life is shaped by periods of acute environmental stress. We find that while these periods of stress pose a risk of complete extinction, they also create opportunities for evolutionary exploration which would otherwise be impossible, leading to more populous and stable states among the survivors than in alternative histories without a stress period. We also study how the duration, repetition and number of refugia into which life escapes during the perturbation affects the final outcome. The model results are discussed in relation to both Earth history and the search for alien life.

Modelling co-evolution of resource feedback and social network dynamics in human-environmental systems

Games with environmental feedback have become a crucial area of study across various scientific domains, modelling the dynamic interplay between human decisions and environmental changes, and highlighting the consequences of our choices on natural resources and biodiversity. In this work, we propose a co-evolutionary model for human-environment systems that incorporates the effects of knowledge feedback and social interaction on the sustainability of common pool resources (CPRs). The model represents consumers as agents who adjust their resource extraction based on the resource’s state. These agents are connected through social networks, where links symbolize either affinity or aversion among them. The interplay between social dynamics and resource dynamics is explored, with the system’s evolution analyzed across various network topologies and initial conditions. We find that knowledge feedback can independently sustain CPRs. However, the impact of social interactions on sustainability is dual-faceted: it can either support or impede sustainability, influenced by the network’s connectivity and heterogeneity. A notable finding is the identification of a critical network mean degree, beyond which a depletion/repletion transition parallels an absorbing/active state transition in social dynamics, i.e. individual agents and their connections are/are not prone to being frozen in their social states. Furthermore, the study examines the evolution of the social network, revealing the emergence of two polarized groups where agents within each community have the same affinity. Finally, we observe an inverse relationship between system complexity and sustainability. Comparative analyses using Monte–Carlo simulations and rate equations are employed, along with analytical arguments, to reinforce the study’s findings. The model successfully captures key aspects of the human-environment system, offering valuable insights to understand how both the spread of information and social dynamics may impact the sustainability of CPRs.

Nuclear Receptors and the Hidden Language of the Metabolome.

Nuclear hormone receptors (NHRs) are a family of ligand-regulated transcription factors that control key aspects of development and physiology. The regulation of NHRs by ligands derived from metabolism or diet makes them excellent pharmacological targets, and the mechanistic understanding of how NHRs interact with their ligands to regulate downstream gene networks, along with the identification of ligands for orphan NHRs, could enable innovative approaches for cellular engineering, disease modeling and regenerative medicine. We review recent discoveries in the identification of physiologic ligands for NHRs. We propose new models of ligand-receptor co-evolution, the emergence of hormonal function and models of regulation of NHR specificity and activity via one-ligand and two-ligand models as well as feedback loops. Lastly, we discuss limitations on the processes for the identification of physiologic NHR ligands and emerging new methodologies that could be used to identify the natural ligands for the remaining 17 orphan NHRs in the human genome.

Edge-based strategies enhance cooperation in intertwined dynamics of cooperation and synchronization

In various ecosystems and human societies, living organisms and systems often exhibit cooperative and synchronous behaviors during survival. Recently, co-evolutionary models of cooperation and synchronization have shed light on the underlying mechanisms driving such behaviors, offering an intriguing avenue for studying these phenomena. However, current research predominantly focuses on a point-strategy decision mode, where each individual is limited to taking the same actions towards its neighbors. Such an assumption implies that an individual is unable to differentiate its neighbors and adjust its strategy accordingly, making it difficult to capture the complexity and diversity of interactive behaviors observed in reality. To this end, we introduce the interaction mode based on the edge-strategy and investigate how synchronization and cooperation co-evolve under interaction diversity. Specifically, we explore a scenario in which individuals can adopt different strategic decisions toward their neighbors based on their differences, thereby extending the traditional point strategy. Our research clarifies the positive role of interaction diversity in alleviating the evolutionary Kuramoto’s dilemma. Moreover, it provides new insights and ideas for comprehending the influence of edge-strategy on the co-evolution of cooperative and synchronous behaviors.

Co-evolutionary dynamics and heterogeneity in corporate social responsibility: A case study on multinational corporation subsidiaries

This study aims to understand the heterogeneity of corporate social responsibility (CSR) approaches across subsidiaries of the same multinational corporations (MNCs) and determine the co-evolutionary dynamics that enhance the global cohesiveness of MNCs' CSR strategies and performance. The study draws on co-evolutionary theory and adopts an explanatory research design based on embedded multiple case study methodology. Semi-structured interviews with top management and employees responsible for CSR or related areas were conducted, and secondary data collection was done for triangulation and validation. The findings suggest that MNCs experience heterogeneity in their CSR, particularly in the implementation approaches, philanthropic and environmental CSR dimensions, and that MNCs with significant heterogeneity at the strategic level have less cohesive strategic CSR practices. The study also found that MNCs that encourage and facilitate active collaboration between subsidiaries have more cohesive and strategic CSR, and each MNC's CSR is driven by different co-evolutionary dynamics. The study contributes to the expansion of co-evolutionary theory to the study of CSR and offers a dynamic CSR co-evolution model explaining MNC CSR heterogeneity in real-world situations, along with potential levers to achieve more globally cohesive strategic CSR.

Coevolutionary dynamics of collective cooperation and dilemma strength in a collective-risk game

Human behavioral decision-making influences the gaming environment. In turn, changes in the gaming environmental impact individual strategic choices. However, there is scant exploration into how human behavioral decision-making coevolves with dilemma strength. Here, we propose a coevolutionary game model based on collective-risk social dilemma, where an increase in cooperators within the game group reduces the dilemma strength, and vice versa. Upon examining this coupled system, we find that the system is capable of achieving a relatively optimal state, wherein the population sustains a high level of cooperation and the dilemma strength remains at the lowest level. In addition, we have identified the conditions for the emergence of tristability and bistability in the coupled system and numerically validated our theoretical results. Furthermore, we find that the incorporation of institutional rewards not only promotes the appearance of the system's optimal state, where all individuals choose to cooperate and the dilemma strength is at its lowest level, but it also effectively averts the manifestation of the system's worst state, where all individuals resort to defection and the dilemma strength reaches its highest level. These findings illuminate how cooperation can be sustained when a dynamical coupling exists between individual decision-making and dilemma strength. Published by the American Physical Society 2024

Location-routing optimization problem for electric vehicle charging stations in an uncertain transportation network: An adaptive co-evolutionary clustering algorithm

The rapid growth of Electric Vehicles (EVs) has led to issues such as insufficient and unevenly distributed charging stations, posing an increasingly severe challenge. This not only means higher economic and time costs for EV users traveling to charging stations, but also indicates that EVs need to incur additional energy consumption. To address these challenges, this paper first establishes an extended space-time-state network to describe the temporal variability and uncertainty of the traffic environment. Secondly, considering the impact of the energy consumption incurred by EVs traveling to charging stations (CSs) on the layout of CSs, a co-evolutionary optimization model of the location-routing problem is proposed. To solve the model, a two-stage Adaptive Co-evolutionary Clustering Algorithm (ACECA) integrating an adaptive clustering framework and a co-evolutionary mechanism is designed. Finally, the experiment results indicate that there exists a game between a short-term economic investment and long-term benefits of energy conservation and emission reduction. Moreover, ACECA demonstrates stronger solution performance and robustness compared to other algorithms, with the resulting CS location schemes showing superior advantages in energy conservation and in cost saving for users. The research findings can provide theoretical support for the planning of charging station locations for electric vehicles.

Convergence analysis of the time-varying discrete-time Altafini model on signed network

This paper mainly studies the convergence of time-varying discrete-time Altafini model. Firstly, we convert the bipartite consensus of the Altafini model with n nodes into consensus of the DeGroot model with 2 n nodes by adopting lifting approach. Secondly, based on structural balance theory and node relabelling theory, the consensus of enlarged DeGroot model can be further equivalently converted into the consensus of one simple DeGroot model with n nodes. Thirdly, by using the equivalent transformation and the ergodic coefficient theory, we change the sufficient condition from ‘repeatedly jointly strongly connected’ graphs to ‘repeatedly jointly scrambling’ graphs, and verify that the new condition also can lead to the bipartite consensus of the Altafini model. This novel criterion allows for determining the bipartite consensus of the Altafini model without the condition of ‘repeatedly jointly strongly connected’ graphs. Next, we provide a concise proof for the case of opinions converging to 0 with the condition of ‘repeatedly jointly strongly connected’ by the ergodic coefficient theory. Finally, we develop a reasonable coevolution model to illustrate the effectiveness and strong applicability of our main theoretical results.