Complex Adaptive Systems Research Articles

Functional team selection as a framework for local adaptation in plants and their belowground microbiomes.

Multicellular organisms are hosts to diverse communities of smaller organisms known as microbiomes. Plants have distinctive microbiomes that can provide important functions related to nutrition, defense, and stress tolerance. Empirical studies provide convincing evidence that in some -but not all - circumstances, belowground microbiomes help plants adapt to their local environment. The purpose of this review is to develop functional team selection (FTS) as a framework to help predict the conditions necessary for root microbiomes to generate local adaptation for their plant hosts. FTS envisions plants and their microbiomes as complex adaptive systems, and plant adaptations as emergent properties of these systems. If plants have the capacity to recognize and cultivate beneficial microbes and suppress pathogens, then it is possible for plants to evolve the capacity to gain adaptations by curating their microbiome. In resource-limited and stressful environments, the emergent functions of complex microbial systems may contribute to positive feedback linked to plant vigor, and ultimately, local adaptation. The key factors in this process are: 1) selective force, 2) host constitution, 3) microbial diversity, and 4) time. There is increasing interest in harnessing beneficial microbial interactions in agriculture and many microbial growth-promoting products are commercially available, but their use is controversial because a large proportion of these products fail to consistently enhance plant growth. The FTS framework may help direct the development of durable plant-microbiome systems that enhance crop production and diminish pathogens. It may also provide valuable insights for understanding and managing other kinds of host-microbe systems.

Domains of laws yet domains of No Law: Energy and work, responsible free will choice, and doing.

Domains of laws yet domains of No Law: Energy and work, responsible free will choice, and doing.

Transforming the full scope of leadership in complex adaptive systems.

Transforming the full scope of leadership in complex adaptive systems.

Strategic Construction of the Technology Ecosystem in the Healthcare Industry: Synergistic Optimization of Innovation Policy, Organizational Agility and Talent Pool

This paper proposes a strategic framework for constructing the technological ecosystem in the healthcare industry based on complex adaptive systems theory, focusing on the tripartite collaborative optimization of innovation policies, organizational agility, and talent reserves. Innovation policies guide resource allocation and reduce systemic risks through classification tools, dynamic adaptation, and ethical calibration; organizational agility enhances technical responsiveness through structural flexibility, knowledge flow, and hybrid forms; talent reserves emphasize the dynamic calibration of skill graphs and intergenerational knowledge transfer. The study proposes a dynamic coupling mechanism and a risk resistance system to mitigate policy delays, break technical path dependence, and strengthen organizational resilience. This framework provides a governance path for the transition of medical technology from isolated breakthroughs to ecological development, with its methodology applicable to high-tech service industries.

Integrated Biomimetics: Natural Innovations for Urban Design, Smart Technologies, and Human Health

Biomimetics has emerged as a transformative interdisciplinary approach that harnesses nature’s evolutionary strategies to develop sustainable solutions across diverse fields. This study explores its integrative role in shaping smart cities, advancing artificial intelligence and robotics, innovating biomedical applications, and enhancing computational design tools. By analysing the evolution of biomimetic principles and their technological impact, this work highlights how nature-inspired solutions contribute to energy efficiency, adaptive urban planning, bioengineered materials, and intelligent systems. Furthermore, this paper discusses future perspectives on biomimetics-driven innovations, emphasising their potential to foster resilience, efficiency, and sustainability in rapidly evolving technological landscapes. Particular attention is given to neuromorphic hardware, a biologically inspired computing paradigm that mimics neural processing through spike-based communication and analogue architectures. Key components such as memristors and neuromorphic processors enable adaptive, low-power, task-specific computation, with wide-ranging applications in robotics, AI, healthcare, and renewable energy systems. Furthermore, this paper analyses how self-organising cities, conceptualised as complex adaptive systems, embody biomimetic traits such as resilience, decentralised optimisation, and autonomous resource management.

Relationship between Cloud Computing Resource Allocation Algorithms and Energy Efficiency in Data Centers in China

Purpose: The purpose of this article was to analyze the relationship between cloud computing resource allocation algorithms and energy efficiency in data centers in China. Methodology: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries. Findings: ​ China show that advanced cloud resource allocation algorithms like Ant Colony Optimization and Genetic Algorithms improve data center energy efficiency by 20–35%. Integrating predictive models further reduces power use and cooling needs. However, adoption is limited by high costs and security concerns. Unique Contribution to Theory, Practice and Policy: The resource-based view (RBV), the green it adoption theory & the complex adaptive systems theory may be used to anchor future studies on relationship between cloud computing resource allocation algorithms and energy efficiency in data centers in China. Researchers and practitioners should implement pilot projects in operational cloud data centers to collect longitudinal performance data on energy savings, workload handling, and potential trade-offs (e.g., latency, migration overhead). Policymakers should consider financial incentives, green certifications, and regulatory frameworks that encourage investment in energy-aware scheduling technologies.

Orchestrating Decentralized Evolution: Multinational Corporations as Complex Adaptive Systems

This paper highlights the importance of integrating complexity theory and complex adaptive systems into the study of international business. Multinational corporations have to cope with complex and rapidly changing environments, which requires implementing mechanisms for decentralized evolution of the organization so that they can adapt dynamically while maintaining coherence. Viewing multinational corporations through the lens of complexity theory provides valuable insights for managers and practitioners, equipping them to adapt to modern global business environments more effectively.

Voices from the Classroom: Identifying Animal Science Professional Development Needs of Early-Career Agricultural Educators Who Teach Predominately Minority Students in Low-Income Schools

Many early-career educators report a lack of confidence in teaching animal science and suggest that comprehensive professional development would improve their abilities to engage diverse audiences in animal science instruction. This lack of confidence in providing quality instruction in key agricultural education areas could leave some educators vulnerable to attrition. Furthermore, educators who teach in low-income schools and schools with high populations of minorities are at a higher risk of attrition. Guided by the Human Capital Theory, this study aimed to assess the animal science professional development needs of early-career educators who teach predominantly minority students in low-income schools. The population for this study was New Mexico agricultural educators participating in a year-long animal science professional development cohort. This cohort was selected from applicants based on their demonstrated need for animal science professional development. The research objective was evaluated using two focus groups, which were asked about the challenges they experienced when teaching animal science. The results suggested that the participants struggle to teach complex animal science topics such as genetics, reproduction, and carcass/live animal evaluation due to a lack of knowledge, cultural challenges, and difficulty engaging students in the curriculum. We intend to use the results of this study to inform the cohort’s year-long experience and suggest expanding professional development options to educators. We recommend evaluating the current professional development offered to agricultural educators to ensure it meets their needs. We also recommend conducting quantitative and qualitative studies to further evaluate educators’ challenges when teaching animal science.

Modeling Supply Chain Finance Resilience with a Complex Adaptive SEIJR Framework

This study develops a novel framework for supply chain financial resilience (SCFR) by integrating complex adaptive systems theory with supply chain finance and resilience concepts. To explore how disruption risks propagate through the supply chain, we propose an SEIJR epidemic model that categorizes node enterprises into five distinct states: susceptible (S), exposed (E), infected (I), quarantined (J), and recovered (R). Transitions between these states are captured using differential equations. Through numerical simulations linking this epidemiological approach to financial resilience metrics, we demonstrate several key findings: first, disruption risks temporarily reduce resilience; second, properly managed risk propagation through timely isolation and effective mitigation can transform disruptions into opportunities for systemic improvement; third, isolation measures need to work alongside recovery mechanisms to significantly improve the overall resilience of supply chain finance. Our results show that optimal isolation strategies enable the system to reach a risk-free equilibrium while simultaneously elevating the supply chain’s long-term financial resilience above initial levels. These findings offer theoretical and practical guidance for dynamic, adaptive risk management strategies in supply chain finance. Empirical validation and other research topics will be explored in subsequent studies.

Conducting Research in International Business Through a Complex Adaptive Systems Lens

Conducting Research in International Business Through a Complex Adaptive Systems Lens

Beyond Linear Thinking: Investigating Business-IT Alignment through Complexity Science

Complexity science offers a powerful perspective for studying process theory in Information Systems (IS). Process theory explores how events, actions, and interactions evolve over time. Although complexity science is well-established in other fields, its use in IS research remains limited. In this study, we apply complexity-based methods, particularly mathematical modelling, to deepen our understanding of IS processes. We focus on business-IT alignment as a case example. By building and analysing a dynamic model, we show that alignment follows complex, nonlinear patterns, such as feedback loops, tipping points, and oscillations, that traditional research methods often miss. Our findings highlight how system dynamics and complexity science can reveal hidden structures in IS processes and provide new tools for researchers.

Quantum-Cognitive Strategic Leadership: A Novel Theoretical Framework Applied to Contemporary Intelligence Architecture: A Case Study Analysis of Meta-Strategic Thinking in 21st Century Security Governance

This study introduces a novel theoretical framework termed "Quantum-Cognitive Strategic Leadership" (QCSL) that synthesizes quantum cognition theory with complexity science to understand strategic decision-making in uncertain environments. Unlike traditional strategic leadership models that rely on linear decision-making paradigms, QCSL incorporates principles of cognitive superposition, strategic uncertainty management, and adaptive intelligence architectures. Through systematic application of this framework to contemporary intelligence governance, we demonstrate how meta-strategic thinking enables leaders to navigate complex, multi-domain challenges while maintaining coherent operational effectiveness. The framework's empirical validation through case study methodology reveals how quantum-cognitive principles manifest in practical strategic leadership, offering new theoretical insights for strategic studies, cognitive science, and public administration. This research contributes to emerging literature on complexity-based leadership theory while providing practical frameworks for developing strategic capabilities in uncertain environments.

Leadership by Learning Design: embrace complexity where it exists

There is a mismatch between established practice in learning design and the increasingly complex nature of the challenges the world is facing. This article connects learning design to complexity science so learners can be better equipped to create a thriving future. Learning Design methods have traditionally leant heavily upon the reduction of learning into consumable units of knowledge, skill, or understanding, that constructively align to create higher-level learning. Those structured and predetermined outcomes assume a level of order and predictability that is not applicable to all contexts, and even less applicable the more volatile, unpredictable, rapidly changing our world becomes. This practice places the learning designer as passive and complicit in perpetuating those challenges. Learning design can lean upon complexity science to treat learning as a complex adaptive system of interrelated and co-dependent parts. When the Anthro-complexity approach of the Cynefin Framework is applied to the design of learning experiences for climate action and decolonisation, this sense-making framework identifies opportunities to change praxis so that the learners are better prepared to address wicked problems in an increasingly complex world. These are changes that learning designers need to lead in an ethical and considered fashion.

Adaptive Leadership in Complex Systems: Moving Beyond Bureaucratic Constraints

In increasingly complex dynamic environments, traditional bureaucratic organizations face a fundamental challenge: how to maintain stability while simultaneously developing the capacity for rapid adaptation to emergent changes. This paper explores the role of adaptive leadership in managing complex adaptive systems (CAS) through the lens of Complex Leadership Theory (CLT), focusing on the transformation of bureaucratic structures. Through a critical synthesis of classical and contemporary theories, the paper reveals the essential tension between bureaucratic systems, based on hierarchy and control, and the principles of CLT, which recognize leadership as a distributed, emergent process. The analysis emphasizes how bureaucratic organizations, despite their advantages in stable conditions, become vulnerable in complex environments because of their delayed response to changes and institutional inertia. In contrast, CLT offers a framework in which adaptive leadership acts as a catalyst for organizational transformation, fostering self-organization, collaboration, and innovation through the interactions of multiple actors. The paper illustrates how adaptive leaders, through strategies such as supporting experimentation and tolerance for uncertainty, enable systems to develop dynamically without completely abandoning structural frameworks. In conclusion, the research offers practical guidelines for the application of CLT in bureaucratic organizations, emphasizing the need for a cultural transformation, redistribution of responsibilities, and the development of leaders capable of managing the paradox of "control and creativity." This research contributes to leadership theory by reinterpreting the role of leaders in CAS, highlighting that adaptive leadership is not an alternative to bureaucracy, but a tool for its restructuring under the demands of complexity.

Complex Relationship of Personality Traits on Teachers' Perceptions of Sustainable Careers: A Qualitative Comparative Analysis With the Big Five

ABSTRACTThis study investigates how the interaction of teachers’ personality traits and demographic characteristics shapes their perceptions of sustainable careers (PoSC) within the framework of Complex Adaptive Systems (CAS). By looking at the Big Five Personality Traits along with conditions like age, gender, marital status, and parenthood, the research uses Qualitative Comparative Analysis to understand how these traits combine. Data collected from 315 teachers across Türkiye yielded seven distinct configurations that contribute to high PoSC. Across these, agreeableness, conscientiousness, and open‐mindedness emerged as core traits, while extraversion and negative emotionality demonstrated varying influences depending on demographic context. The research underscores the nonlinear, context‐dependent nature of career sustainability and highlights equifinality—multiple pathways leading to the same outcome. By integrating sustainable careers theory with CAS, this study offers a novel lens to understand how individual and systemic conditions co‐shape career experiences in education. It also offers useful information about developing differentiated support strategies based on personality‐demographic interactions, particularly within the centralized and culturally specific context of the Turkish education system.

Understanding First, Solutions Second

This paper applies rigorous systems thinking methodologies to critically evaluate Australia's proposed Cybersecurity Professionalization Scheme within the context of Australia's projected workforce shortages. Through the analytical frameworks of DSRP Theory (Distinctions, Systems, Relationships, Perspectives) and Agent-Based Approach (ABA), this research interrogates the complex adaptive system of cybersecurity workforce development. Meta-analysis of empirical evidence across industries reveals a consistent pattern: while professionalization schemes increase practitioner earnings and potentially enhance service quality in high-risk domains, they simultaneously create significant market entry barriers and can reduce workforce growth. The study identifies that Australia's proposed scheme exhibits strengths in its industry-led design philosophy and integration with existing certification frameworks, but raises concerns regarding systemic impacts on cost inflation, industry adoption rates, and workforce diversity. The ABA analysis identifies emergent properties that may undermine the scheme's objectives and proposes design principles to mitigate unintended consequences. This paper contributes to the critical discourse on professionalization by demonstrating how a systems thinking approach can reveal dynamics invisible to traditional policy analysis. This research provides stakeholders with an evidence-based evaluation framework to assess the scheme's effectiveness and sustainability, while offering specific recommendations aligned with systems design principles that could enhance positive outcomes while mitigating negative systemic impacts. As a result, the fundamental question emerges: does Australia have an adequate understanding of the cybersecurity workforce system prior to implementing solutions?

The complexity trade-off between morphological richness and word order freedom in Romance languages: A quantitative perspective

Abstract The complexity trade-off hypothesis suggests a balance between different linguistic features across human languages. This study investigates this hypothesis by quantitatively examining the evolution from Latin to Modern Romance languages. We focus on the trade-off between morphological richness and word order freedom, providing insights into their dynamic interrelations during linguistic evolution. Our analysis demonstrates that morphological richness and word order freedom are distributed along a continuum, with Latin exhibiting higher morphological complexity and freer word order and Modern Romance languages having lower morphological richness and more rigid word order. This evolution reflects the principles of efficiency in complex adaptive systems, showing a significant complexity trade-off where increases in one feature often result in decreases in the other. These findings indicate the adaptive nature of linguistic systems and offer valuable insights for diachronic typological research, enhancing our understanding of the complexity trade-off from a quantitative perspective.

Synergistic Foundations of a Resilient National Energy System: a Complex Systems Approach

The current paradigm of national energy security, based on risk analysis, is conceptually flawed and practically ineffective in the current conditions of globalization and growing uncer-tainty. Based on the science of complexity, synergetics, and self-organization theory, the ne-cessity of transition from a strategy of predictable protection to a strategy of adaptive security is substantiated. An approach to building a resilient energy infrastructure based on the identifi-cation of system invariants and the targeted stimulation of emergent properties inherent in complex systems is proposed: self-organization, autopoiesis, and adaptive variability. Drawing on the example of Ukraine, the feasibility of forming a national program to ensure the adaptive resilience of the energy system is considered, combining massive decentralized local genera-tion, economic incentives to boost resilience, and strategic technology transfer. It is also con-sidered how the synergistic approach can be used to develop high-performance data centers, artificial intelligence infrastructure, and advanced research institutes, turning excess energy in-to a driving force for national intellectual and economic growth.

Swarm Intelligence and Urban Futures: Rethinking Cities as Co-Evolving Ecologies

Emergent phenomena in natural and vernacular systems offer radical paradigms for reimagining the future of urban life. This paper articulates Swarm Urbanism: a theoretical and operational model positioning cities as co-evolving, bioadaptive, sapient ecologies. Drawing on insights from complexity science, biomimicry, decentralized governance, and critical urban theory, the research critiques prevailing Smart City, Resilient City, and Doughnut Urbanism frameworks, exposing their lingering teleological and centralized biases. Instead, Swarm Urbanism advances an ethos of distributed agencies, stigmergic infrastructure, mutualistic economies, and dynamic, ethically reflexive governance. Embracing emergence as a constitutive dynamic, rather than a problem to be managed, emergent cities are envisioned as living, evolving systems capable of sensing, learning, adapting, and co-flourishing within planetary boundaries. Yet the approach rigorously confronts the inherent fragilities of complex decentralized systems: risks of entropy, chaotic collapse, unjust emergences, and informational opacity. Transitional research pathways — including agent-based simulations, living labs, and phased pilot programs — are proposed to responsibly cultivate emergent urban resilience. Ultimately, this work situates Swarm Urbanism as a post-Anthropocenic urban epistemology: a tentative, adaptive choreography of complexity, ethics, and planetary co-evolution in an era of unprecedented uncertainty.

Beyond Sustainability: Paradigms for Complexity and Resilience in the Built Environment

Conventional approaches in architecture and urban planning still rest on modernist, deterministic assumptions that downplay the nonlinearity and deep uncertainty that characterize contemporary cities. Sustainability, although crucial, has often been operationalized through incremental, efficiency-oriented checklists that struggle to address systemic transformation. This conceptual theory synthesis reframes the built environment as a complex adaptive system and interrogates three paradigms that have arisen in the wake of the sustainability turn: resilience planning, adaptive planning, and regenerative design. Drawing on an integrative, narrative review of interdisciplinary scholarship, the article maps these paradigms onto a functional “what–how–why” theoretical scaffold: resilience specifies what socio-technical capacities must be safeguarded or allowed to transform; adaptive planning sets out how planners can steer under conditions of deep uncertainty through sign-posted, flexible pathways; and regenerative design articulates why interventions should move beyond mitigation toward net-positive socio-ecological outcomes. This synthesis positions each paradigm along an uncertainty spectrum and identifies their complementary contributions.