The core claim of the article is simple but consequential. All complex adaptive systems are complex systems, but not all complex systems are adaptive. A complex system, in the standard definition, is any system composed of many interacting components whose aggregate behavior cannot be understood by analyzing the parts in isolation. Its behavior is shaped by nonlinear interactions and feedback loops, meaning that small changes in one part of the system can have disproportionately large effects elsewhere, or vice versa. Such systems often operate far from equilibrium and exhibit emergent patterns: coherent structures or behaviors, such as storms or jet streams, that arise from local interactions but are not properties of any individual component. The authors stress that this notion of complexity contrasts sharply with systems that are merely complicated. A commercial airliner is complicated but can, in principle, be fully decomposed into subsystems whose behavior is specified by design. A turbulent fluid, a power grid under fluctuating load, or the global climate cannot be understood this way, because feedbacks, nonlinearities, and stochastic influences produce behavior that is more than the sum of the parts.

Crucially, complex systems do not have to be adaptive. Many paradigmatic complex systems in physics and engineering involve components that follow fixed rules and never learn. Molecules in a fluid, stars in a galaxy, or components in an electrical grid interact in intricate ways, but the rules of interaction are set by physical laws and engineering design. The system can show unpredictability, phase transitions, or critical phenomena, yet the components themselves do not change their behavior in response to outcomes. The authors offer the weather as the canonical example. Atmospheric dynamics are governed by fluid mechanics and thermodynamics, and interactions among air masses, heat flows, moisture, and topography generate emergent features such as storms. The system is open, nonlinear, and famously sensitive to initial conditions, which keeps forecasting challenging despite sophisticated models and data. But neither air parcels nor pressure systems adapt in the sense of learning: they do not revise their governing rules based on past forecasts or policy decisions.

A complex adaptive system adds a crucial feature to this picture. Drawing on John Holland’s definition, the authors describe a CAS as a system that involves many components that adapt or learn as they interact. In CAS, components, often called agents, change their state, strategies, or rules in response to experience. Adaptivity typically implies some form of memory or internal structure that can be updated, and it often leads to coevolution between agents and their environment. Examples abound in the life and social sciences. In an ecosystem, species evolve traits that affect and are affected by the traits of other species and by the abiotic environment. In an immune system, cells learn to recognize and respond to novel pathogens, with the repertoire of responses shaped by previous encounters. In economies and financial markets, firms and investors adjust strategies in response to prices, regulations, and each other’s behavior. Cities, too, are complex adaptive systems: households, firms, and institutions adapt to infrastructure, policies, and one another, producing shifting patterns of land use, mobility, and inequality.

Adaptation can also occur at multiple, nested levels. An agent at one level, such as a person, may itself be a complex adaptive system composed of interacting subsystems while simultaneously functioning as a component of a higher-level CAS, such as a city. In many biological and social CAS, this adaptivity is hierarchically organized: higher-level structures constrain and enable local behavior even as local interactions generate emergent system-level patterns. The authors trace the intellectual history of this distinction, noting that Walter Buckley introduced the phrase complex adaptive system into sociology in the 1960s, and that Holland and colleagues at the Santa Fe Institute later elaborated CAS as a unifying framework for systems with adaptive agents, from economies to ecosystems. In parallel, physicists and mathematicians advanced general theories of complex systems, including chaos, self-organization, and critical phenomena, often without explicit adaptivity. The CAS tradition, the authors emphasize, did not emerge as a mere correction to theories of self-organization; it developed around a distinct concern with how agents, populations, or institutions learn, remember, and revise behavior over time.

Why does this distinction matter in practice? The authors identify three consequences, the first being model choice. System dynamics models, aggregate representations of stocks, flows, and feedbacks popularized in the systems thinking community, are well suited to complex systems whose behavior is dominated by structural feedbacks rather than explicit agent-level adaptation. But they generally do not represent individual agents adapting their rules or strategies over time. When applied to systems where agent heterogeneity and learning are central, such as urban mobility, financial markets, or the spread of misinformation, purely aggregate models can miss key dynamics. The authors give a vivid example: an urban planner might construct a system dynamics model of vehicle flows and road capacity and conclude that adding a new road will reduce congestion. In reality, drivers adapt their routes, developers change land use, and the new capacity induces additional demand, potentially leaving congestion unchanged or even worse. An agent-based model that explicitly represents adaptive travelers and developers would be far better aligned with the system’s adaptive character.

The same logic applies in epidemiology and public health. Simple compartmental models of disease spread treat individuals as homogeneous and non-adaptive: they do not change contact patterns or behavior in response to risk or policy. In actual epidemics, people adapt; they reduce contacts, alter compliance, and respond to information. Treating an epidemic as a non-adaptive complex system may be adequate for early, short-term forecasting, but for long-term policy evaluation, the authors argue, a CAS perspective and models that incorporate behavioral responses are critical. The distinction also shapes how evidence is found and interpreted. The authors note that in the resilience literature, differences in terminology, including whether papers used complex systems, complex adaptive systems, or social-ecological systems, affected which studies were retrieved in systematic reviews. Important work on adaptive ecosystems could be overlooked if search terms were restricted to complex systems in a narrow sense, while treating all complex systems as adaptive risks importing results from physical systems into domains where agent behavior fundamentally changes the dynamics.

The third consequence concerns intervention itself. In a non-adaptive complex system such as a power grid, interventions change flows and feedbacks, but the components do not change their rules in response. Unintended consequences such as blackouts or cascading failures may still occur, but there is no need to worry about deliberate strategic responses by the system’s elements. By contrast, most policy-relevant systems are complex and adaptive: economies, cities, governance networks, and social-ecological systems all contain agents that respond to incentives and information. Pathogens evolve resistance, financial actors find loopholes, and communities reorganize in response to planning decisions. This adaptive response is what distinguishes CAS from non-adaptive complex systems and makes them both fascinating and difficult to govern. As Colander and Kupers argue in work cited by the authors, complexity-aware public policy should focus less on top-down control and more on creating conditions under which adaptive agents self-organize toward desirable outcomes. Nel and Taeihagh likewise highlight the soft underbelly of complexity science adoption in policymaking: non-technical challenges such as institutional incentives, capacity, and trust can make or break attempts to use complexity-informed tools.

The authors are careful to note that the CS/CAS distinction should not be read as forcing a rigid either/or ontology or a single preferred method. Many research problems require complementary descriptions: complex systems models may be appropriate for structural, physical, or network constraints, while CAS models are needed to represent learning, strategic adjustment, or evolution. The distinction is therefore methodological as much as semantic; it helps researchers decide when complementary or hybrid approaches, combining system dynamics, network science, and agent-based modeling, are necessary, especially for socio-technical systems that mix physical infrastructure with adaptive agents. As large-scale data on human behavior, ecosystems, and infrastructure proliferate, the authors see an opportunity to test when non-adaptive approximations suffice and when explicitly modeling adaptation is essential. Education and training in complexity science, they add, should explicitly address the CS/CAS distinction and its practical implications for students, policy analysts, and decision-makers.

Ultimately, the message of the Perspective is one of conceptual honesty. Complexity science now sits at a critical juncture: its concepts and tools are increasingly institutionalized, with dedicated schools, research centers, and journals, yet the rapid uptake of complexity thinking in applied domains risks diluting its meaning. For some problems, adopting a complex-systems lens means paying attention to feedback, nonlinearity, and emergent patterns in systems whose components do not adapt. For others, especially in life and social domains, complexity thinking must include adaptivity, learning, evolution, and, in some cases, goal-directed and self-regulating forms of behavior. Collapsing these cases under a single, vague notion of complexity risks disappointment and misdirected effort. Using the language of complex systems and complex adaptive systems with care, the authors conclude, can make complexity thinking a more precise, powerful, and trustworthy guide for action.

Subject of Research: Distinction between complex systems and complex adaptive systems

Article Title: Complex systems vs. complex adaptive systems: why the difference matters

Article References: Shutters, S. T., & Dhein, K. (2026). Complex systems vs. complex adaptive systems: why the difference matters. Frontiers in Complex Systems, 4, Article 1808634. https://doi.org/10.3389/fcpxs.2026.1808634

Image Credits: AI Generated

DOI: 10.3389/fcpxs.2026.1808634

Keywords: complexity science, complex adaptive systems, agent-based modeling, system dynamics, policy, emergence, Complex, systems, adaptive, difference, matters, scientific research

The core problem the framework addresses is familiar to anyone who follows climate policy. Cities around the world must decarbonize, use resources more efficiently, build resilience, and broaden access to opportunity, yet the same transition strategy that succeeds in one metropolitan area can fail inexplicably in another with a seemingly similar economy. Growth theory, sectoral models of structural change, urban scaling laws, and economic complexity research each capture part of the picture, but none explains why a specific activity establishes itself in one city and not in another. Place-based strategies preserve local context but resist cumulative comparison. The authors argue that what is needed sits between abstract general laws and case-by-case description: a middle-range theory, in the sociological sense introduced by Robert Merton, with a bounded domain, separable causal mechanisms, observable indicators, and propositions that can fail.

Ecological succession supplies the causal architecture. In ecology, succession is not merely the observation that one group of species replaces another; it is an explanation of why that turnover occurs. Three broad conditions must hold: space or resources must become available, potential colonists must be present in the surrounding landscape and able to reach the site, and those arrivals must be able to establish and persist under local conditions. Interactions among organisms, historical legacies, repeated disturbance, and connections to nearby habitats then shape the realized pathway. Crucially, modern succession theory rejects a single universal progression toward a stable climax community. Outcomes depend on disturbance history, surviving legacies, arrival order, and chance, and trajectories may converge, diverge, stall, or reverse. Succession is itself a middle-range theory, offering a common causal vocabulary for explaining particular pathways rather than one universal path.

The comparison rests on more than metaphor, the authors insist. Both ecosystems and economies are complex adaptive systems: heterogeneous, partly autonomous elements whose local interactions generate aggregate patterns that feed back to change the elements themselves. Firms, workers, consumers, investors, and institutions adapt under incomplete information, interact through networks, and continually alter the environment to which they respond, just as organisms do in ecosystems. This shared classification makes a disciplined transfer of candidate mechanisms worth testing. But the authors are explicit that cities are not biological ecosystems. Economic actors anticipate, strategize, create formal rules, and deliberately redesign their environments, so an ecological mechanism becomes economically informative only when its variables can be redefined in observable economic terms, remain plausible under purposeful human action, and produce predictions that can fail.

The translation yields six economically observable components. Opportunity availability corresponds to the ecological opening for establishment: a market, technological, policy, or incumbent-exit opening, such as public procurement, emissions standards, or brownfield redevelopment. Activity availability asks whether the needed firms, founders, investors, technologies, and workers can be assembled locally or reached through migration, commuting, and intercity networks. Local capability fit asks whether an arriving activity can persist given local skills, suppliers, infrastructure, institutions, finance, land, and regulation, with relatedness analysis serving as an important but incomplete proxy. Three groups of conditions then modify these gates: interactions among activities, including facilitation through shared suppliers and competition for land and labor; historical legacies and feedback, through which early specializations leave labor pools, assets, and institutions that alter later opportunity; and extra-local connectivity, the trade, ownership, knowledge, and supply-chain links through which an open city-region can relax or externalize local constraints.

The framework is deliberately falsifiable. The authors state five propositions, each paired with the observation that would discriminate it from simpler accounts. The first holds that entry should depend jointly on opportunity and access, with the marginal effect of each approaching zero when the other is below a pre-registered threshold; if the two instead have stable additive effects, the joint-gate interpretation fails. A second proposition holds that relatedness should matter most for activities extending an established trajectory and least for pioneering activities, which should depend instead on external connectivity and policy support. A third claims that entry sequence matters even after the current portfolio is controlled: cities matched on present composition but differing in how they acquired it should diverge in subsequent entry probabilities. A fourth holds that entry probability depends on the configuration of the whole portfolio, not just pairwise similarity to each incumbent. A fifth proposes that repeated entry reorganizes production and resource flows, measurable in input-output link density, reciprocity, cycling, and import leakage, along recurrent, context-dependent trajectories.

The empirical machinery is specified in unusual detail for a conceptual paper. The authors sketch an event-history design in which the entry of a green activity into a metropolitan area is defined as employment exceeding a pre-registered minimum and location quotient in two consecutive years, with all predictors measured before the first qualifying year. Opportunity would be measured through activity-specific demand or policy openings, access through the prior stock of relevant firms and occupations in connected regions, and fit by comparing the activity’s national occupation vector with the city’s pre-entry industry-by-occupation portfolio. Complementary log-log models with activity, city, and year effects would test whether opportunity and access operate as joint gates rather than separate additive causes. On the contraction side, the framework distinguishes within-activity transformation, functional absorption, managed exit, and unmanaged collapse, insisting that contraction should not be treated as the preferred sustainability outcome in stable or growing regions.

The practical payoff is a diagnostic workflow modeled on how restoration ecologists diagnose stalled recovery. Rather than asking which related industry a city should subsidize, the succession-based diagnostic asks why an apparently feasible activity has not established. Is there an economic opportunity at all, or does the city hold the right skills while nobody is buying? Can relevant firms, workers, and technologies reach the city? Can the activity perform locally given suppliers, finance, and permitting? What interactions will the transition create with incumbents, and what sequence of interventions is required? The authors illustrate with a circular construction economy: a city might impose procurement mandates for reused materials before grading standards, deconstruction capacity, and exchange infrastructure exist, causing demand to be met by imports and local entrants to fail, whereas building those establishment conditions first allows early reuse firms to form linkages that procurement then expands.

The framework’s boundary conditions are stated with equal care. Cities are radically open, drawing labor, capital, and knowledge from distant places, so a local absence need not be a functional absence. Political power and distribution matter: a pathway may be feasible and profitable while remaining socially undesirable, and the framework does not settle whose objectives define the target state. Greater internal cycling or linkage density is not automatically sustainable; a more interconnected economy may remain carbon intensive, shift environmental burdens elsewhere, or deepen inequality. Strategic resistance by incumbent firms and coalitions must be coded according to the specific gate it alters, whether suppressing an opening, restricting access to land or finance, or raising entrants’ costs, rather than dismissed as undifferentiated history.

What the authors ultimately offer is a comparative research program rather than a universal recipe. Prior work tracking 938 U.S. urban economies through the Great Recession identified six response archetypes, from stasis to rapid shifts followed by stabilization, suggesting that responses to a common shock form a small set of recognizable pathways. Urban economic succession aims to explain which causal constraints select among those pathways and when they recur, building a library of bounded models linking recurrent configurations to mechanisms, outcomes, and policy levers. For cities racing to decarbonize, the message is sobering and useful in equal measure: the same green technology may require entirely different policy portfolios in different city-regions, and the diagnostic question that matters first is not what to build, but what is actually binding.

Subject of Research: An ecology-informed diagnostic framework for sustainable urban economic transformation

Article Title: Economic succession: an ecology-informed diagnostic framework for sustainable urban transformation

Article References: Shutters, S. T., & Applegate, J. M. (2026). Economic succession: an ecology-informed diagnostic framework for sustainable urban transformation. Frontiers in Sustainable Cities, 8, Article 1953866. https://doi.org/10.3389/frsc.2026.1953866

Image Credits: AI Generated

DOI: 10.3389/frsc.2026.1953866

Keywords: ecological succession, urban economics, sustainable cities, economic complexity, green transition, circular economy, path dependence, urban scaling, economic diversification, complex adaptive systems, decarbonization, urban metabolism

The research team, led by Trinidad Cristina Guerrero Jiménez of El Colegio de La Frontera Sur, brought together two theoretical traditions that rarely meet. The first is the Localized Agri-Food Systems approach, known as SIAL, which examines how food systems organized around specific territorial resources such as origin, productive tradition, and cultural identity can become sources of competitiveness. The second is complex adaptive systems theory, which describes how innovations emerge from unplanned local interactions, how agents self-organize without central direction, and how producers and markets coevolve. By combining the two, the researchers set out to answer a practical question: what gives small fair-trade producer organizations, or SPOs, a competitive edge when they sell roasted coffee to Mexican consumers rather than green beans to foreign buyers?

The answer required an unusually ambitious methodology. In a first quantitative phase, the team mined 12,253 customer reviews from 148 coffee shops in eight Mexican cities, from Monterrey and Guadalajara to Oaxaca and Tijuana, using a rule-based lexical classifier validated against 500 hand-labeled reviews and achieving 84 percent precision. In a second qualitative phase, ten participants from four organic fair-trade SPOs, including Campesinos Ecológicos de la Sierra Madre de Chiapas and Unión Majomut, made 39 visits to coffee shops in Oaxaca de Juárez, Monterrey, and Guadalajara and took part in four cupping sessions between 2024 and 2026. A final two-day co-construction workshop in 2026 allowed producers to validate findings and design market strategies together with the researchers.

The quantitative results were striking in their clarity. Territorial embeddedness, the degree to which a coffee is explicitly linked to its place of origin, was the most frequently mentioned concept in reviews, reaching 61 percent of positive mentions in Oaxaca, and it was the only variable with large effect sizes on customer satisfaction across every dimension measured, with partial eta-squared values between 0.17 and 0.22 for food, beverages, service, and atmosphere. In practical terms, coffee shops that anchored their product to a specific community or region were strongly associated with happier customers, a pattern consistent across all eight cities. Cooperation, by contrast, showed no statistically significant association with satisfaction, a puzzle the authors resolve elegantly: cooperation operates behind the scenes, as an invisible foundation that enables embeddedness, while consumers reward the visible outcome of that cooperation, the story and origin of the coffee, rather than the process itself.

The city-level analysis revealed that localized agri-food systems are not born equal, and, crucially, that they do not require proximity to coffee farms. Oaxaca emerged as the most consolidated SIAL, with the strongest correlation between collective resource activation and the emergence of innovations at 0.608. Tijuana, a city with no coffee-producing tradition, came second at 0.379, demonstrating that specialized urban demand and cooperation with distant producing regions can construct origin narratives from scratch. Guadalajara and Monterrey showed moderate correlations consistent with systems still developing, while Querétaro and Guanajuato displayed patterns the authors characterize as incipient, with cooperation even showing slightly negative correlations with innovation in those cities.

The qualitative phase documented how innovations emerged without central planning, at four interconnected levels. In coffee valuation, new quality criteria appeared, with shops classifying beans by specialty scores, microlot status, and processing method. In service practices, baristas deployed brewing methods such as V60, Chemex, Aeropress, and Japanese siphon with highly developed expertise. In customer interaction, the figure of the barista-educator emerged, someone who explains origin, roasting, and tasting notes and turns a commercial transaction into an educational and emotional encounter. And in the narratives of the producer organizations themselves, a fundamental realization took hold, captured in one participant’s reflection on what type of coffee, roast, and qualities their organization could realistically sell in Guadalajara.

Self-organization appeared in its purest form during the cupping sessions, which the researchers describe as intermediate projects, structured spaces acting as living laboratories where the fair-trade niche meets the conventional market regime. When producers and SPO representatives stepped into these sessions, they spontaneously assumed the role of educators of origin, while baristas transformed into evaluators and critics of the coffee. This role reconfiguration was not centrally planned; it arose from local interactions among agents with complementary but asymmetric knowledge. Producers understood territory, production, and the bean but not urban market expectations, while roasters acted as bridges, and baristas and shop owners commanded direct knowledge of service and consumer trends. The openness of shops to receiving samples and contact information became the germ of informal agreements, while one shop’s willingness not to change suppliers illustrated self-organization through resistance, stabilizing the system around trust and local convenience.

Perhaps the most consequential outcome was second-order learning, a transformation in valuation frameworks rather than a mere accumulation of information. The producers’ central discovery was that not all coffee shops are the same. The domestic market, once perceived as a homogeneous and inaccessible block, resolved into a spectrum of differentiated niches: a specialty segment where microlots and scored coffees command premiums, an intermediate segment where consistent quality paired with a good story suffices, and a traditional segment where competitive pricing matters most. Producers also recognized new strategic roles for themselves, including the possibility of becoming roasters or forming alliances with local ones, and the need for strategic planning before market visits, targeting owners rather than employees.

The authors frame these findings within the complex systems concept of the edge of chaos, the productive tension between the chaos of innovation and the order of self-organization. Coevolution operated in both directions: SPOs adapted their strategies to the value logics of specialty shops, while shops and baristas, by educating their customers, were simultaneously reshaping consumer expectations and creating the very segment in which producer coffee could find a differentiated space. The researchers also introduce the notion of a value ceiling, arguing that SPO competitiveness lies not in bean quality alone but in articulating an integrated system of actors, resources, narratives, and innovations that anchors coffee to its territory and humanizes the value chain, giving face and name to the people behind the product.

The study is candid about its limits. The data characterize the TripAdvisor-visible segment of coffee shops rather than the entire market, the qualitative sample of ten participants from four organizations cannot be generalized, and the 2024 to 2026 timeframe cannot capture long-term coevolutionary dynamics. Yet even within these bounds, the implications reach well beyond coffee. For any cooperative movement confronting diluting certification schemes and unequal value chains, the Mexican coffee case offers a replicable protocol combining data mining, participant observation, and co-construction workshops, and a conceptual toolkit for turning territorial identity into competitive strategy. The lesson is deceptively simple: the most valuable asset a small producer owns may not be the bean itself, but the story of the land it grew on, told by the person who grew it.

Subject of Research: Emergence and self-organization of innovations among small-scale fair-trade coffee producers entering the Mexican domestic market

Article Title: Selling on your own land: emergence and self-organization of innovations among small-scale coffee producers to sell in the Mexican domestic market

Article References: Guerrero Jiménez, T. C., Gómez Vargas, J. M., Herrera Hernández, O. B., & Martínez Villanueva, L. (2026). Selling on your own land: emergence and self-organization of innovations among small-scale coffee producers to sell in the Mexican domestic market. Frontiers in Complex Systems, 4, Article 1830387. https://doi.org/10.3389/fcpxs.2026.1830387

Image Credits: AI Generated

DOI: 10.3389/fcpxs.2026.1830387

Keywords: specialty coffee, small-scale producer organizations, fair trade, territorial embeddedness, localized agri-food systems, complex adaptive systems, self-organization, emergence of innovations, Mexican domestic market, second-order learning, coevolution, Oaxaca

The research, conducted by Sihan Lu, Yan Zhou, and Tao Hong of the Harbin Institute of Technology in China, starts from a theoretical observation about the academic literature. Most scholarship on generative AI in the public sector has examined the technology through a siloed lens, focusing on a single role at a time. Studies of AI as a governance tool emphasize efficiency gains and service improvement. Studies of AI as a regulatory object emphasize risk, liability, and the need for oversight. A smaller body of work treats AI as a novel decision-making agent with its own capacity to act. Each perspective is useful, but each is incomplete, and the fragmentation obscures what happens at the micro level when these roles intersect inside a single administrative system.

To overcome that limitation, the authors reconceptualize the public management system as a complex adaptive system, a framework borrowed from the study of ecosystems, markets, and other environments in which many interacting agents produce outcomes no single actor intends. Within this framework, generative AI is modeled as an adaptive agent rather than a static instrument. That shift matters because adaptive agents learn, respond to feedback, and alter the behavior of the other agents around them. A chatbot that answers citizen queries changes how civil servants work; a drafting assistant changes how policy documents are produced; a predictive model changes which cases get attention. The technology does not sit outside the governance system. It becomes part of the system’s own dynamics.

The empirical core of the study is a single-case analysis of Singapore’s Smart Nation initiative, one of the world’s most sustained and comprehensive national programs for embedding digital and intelligent technologies in public administration. By tracking governance strategies and their outcomes across a range of situations within that program, the researchers were able to observe how the same underlying technology was assigned different roles in different contexts, and how those assignments generated tensions that no single regulatory rule could resolve.

The central theoretical contribution of the paper is what the authors call the Governance Impossible Triangle. According to this framework, generative AI’s triple identity creates persistent tensions among three goals that public administrators must pursue at the same time: maximizing efficiency, safeguarding the legitimacy of decisions, and maintaining effective risk control. The triangle is impossible in the same sense that classic policy trilemmas are impossible. Push hard on efficiency, for example by automating more decisions end to end, and legitimacy comes under strain because citizens and officials lose the ability to understand and contest how outcomes were reached. Push hard on legitimacy, for example by requiring extensive human review and explanation, and the efficiency gains that justified adoption begin to erode. Push hard on risk control, and both efficiency and responsiveness suffer. The three corners pull against one another continuously, and no configuration satisfies all of them at once.

What makes the triangle particularly difficult is that the three identities of generative AI activate the three corners in different combinations. As a tool, the technology is primarily an efficiency asset, and the governance question is how much discretion to delegate to it. As a regulatory object, it is primarily a risk to be contained, and the question is how to oversee systems whose behavior even their developers cannot fully predict. As a decision-making agent, it touches legitimacy directly, because democratic governance rests on the premise that consequential decisions are attributable to accountable human institutions. When a single platform shifts between these roles depending on the task at hand, the governance burden shifts with it, often faster than rules and oversight structures can adapt.

The authors’ response to this dilemma is the Contextual Identity Configuration Model, a framework that rejects one-size-fits-all regulation in favor of dynamic, context-sensitive assignment of AI’s role. The model is driven by four variables: problem complexity, risk severity, value conflicts, and explainability requirements. Simple, low-risk, low-conflict problems with clear standards can tolerate a high degree of algorithmic tool-intensity, meaning the AI acts with substantial autonomy as an efficiency instrument. As complexity rises, as stakes rise, as values come into conflict, or as the need for explanation grows, the model calls for recalibrating the system, reducing agentic power and increasing human oversight, so that the technology reverts toward a supportive tool role rather than an autonomous one.

To make this concrete, the study analyzes four prototypical scenarios of AI deployment in public governance. In routine optimization, where tasks are standardized and risks are low, generative AI can operate with high tool-intensity, delivering efficiency with minimal threat to legitimacy. In professional assistance, the AI supports trained experts who retain judgment and accountability, a middle configuration that captures productivity while keeping humans in the loop. In high-stakes discretion, where decisions affect rights, liberties, or significant resources, the model demands strong explainability and human authority, constraining the AI to an advisory function. In complex crises, where speed matters but uncertainty is extreme, the framework calls for layered defenses and continuous feedback so that algorithmic recommendations inform but never replace accountable human command. Across all four scenarios, the governing principle is the same: management systems must dynamically recalibrate algorithmic tool-intensity and agentic power through continuous feedback loops rather than fixing the technology’s role in advance.

The practical implications the authors draw are institutional as much as technical. They argue that governments should build multi-layered defense systems, in which technical safeguards, procedural review, and human accountability operate together rather than as substitutes for one another. They also propose joint accountability networks, arrangements in which responsibility for AI-assisted decisions is distributed across developers, agencies, and oversight bodies in ways that remain legible to the public. The goal, in their formulation, is a profound alignment between technological empowerment and core public values, so that the efficiency of generative AI does not come at the cost of the legitimacy and trust on which public institutions depend.

The study arrives at a moment when many governments are experimenting with generative AI faster than they are theorizing it. Its contribution is to name the structural problem: the same technology that makes administration faster also makes it harder to explain, and the same flexibility that makes it useful makes it hard to regulate. By modeling the public sector as a complex adaptive system and the AI within it as an adaptive agent, the researchers offer policymakers a vocabulary for the trade-offs they are already making, often implicitly. The Governance Impossible Triangle will not be dissolved, the authors suggest, but it can be managed, provided that regulators abandon uniform rules in favor of an agile, context-based governance paradigm in which the identity of the technology, and the controls attached to it, are deliberately configured to match the demands of each task.

Subject of Research: The role and governance of generative AI in public administration and government decision-making

Article Title: The “Triple identity” of generative AI in public governance: dilemmas and adaptive solutions

Article References: Lu, S., Zhou, Y., & Hong, T. (2026). The “Triple identity” of generative AI in public governance: dilemmas and adaptive solutions. Humanities and Social Sciences Communications. https://doi.org/10.1057/s41599-026-09400-4

Image Credits: AI Generated

DOI: 10.1057/s41599-026-09400-4

Keywords: generative AI, public governance, complex adaptive systems, Smart Nation Singapore, algorithmic accountability, AI regulation, governance trilemma, decision-making agents, public administration, risk control, legitimacy, adaptive governance

The study’s starting point is a theoretical one. Disaster and emergency management, the authors contend, behaves as a complex adaptive system: a network of interdependent actors—central and local government agencies, emergency services, Indigenous partners, civil society organizations, and private sector entities—whose roles and relationships shift as hazard conditions evolve. Three properties of such systems matter most for DEM. Self-organization allows actors to reconfigure coordination autonomously, as observed during the 2011 Tōhoku disaster in Japan, where local actors improvised networks after formal command structures collapsed. Nonlinearity means disruptions can propagate in ways that produce consequences disproportionate to their initial cause, a dynamic famously analyzed by Charles Perrow in his work on normal accidents. Emergent behavior, meanwhile, means system performance arises from interactions among actors rather than from prescribed plans, and therefore cannot be fully anticipated or controlled in advance.

These characteristics expose a structural mismatch. Leadership scholarship has evolved from trait-based theories toward relational, situational, and transformational models, yet it has remained anchored within the organizational unit rather than the interorganizational system. Even systems-oriented approaches such as meta-leadership and complexity leadership theory retain a largely leader-centric orientation, privileging the individual actor as the primary locus of control. The limitations of that orientation are visible in disaster history: post-event analyses of Hurricane Katrina identified failures of individual leadership alongside structural fragmentation, unclear cross-agency mandates, and inadequate mechanisms for aligning distributed actors toward shared objectives. The 2010 Haiti earthquake similarly showed how leadership capacity interacted with pre-existing institutional weakness and fragmented international coordination.

To fill this gap, the researchers turned to stewardship theory, which emerged as a counterpoint to agency theory’s assumption that actors are self-interested and require monitoring and incentives to perform. Stewardship instead conceptualizes actors as fundamentally motivated to pursue collective goals when organizational identification and value alignment are strong. Extended to the system level, stewardship becomes a form of meta-governance—concerned with shaping a system’s goals, rules, feedback, and response—rather than with directing operational activity. The concept has been applied in natural resource governance, public health system management, and complexity-informed public policy, but had not been systematically examined within DEM, despite the field’s combination of distributed authority, multi-agency interdependence, and nonlinear hazard environments.

The empirical work employed constructivist networked grounded theory, a methodology combining Charmaz’s constructivist grounded theory with social network analysis to examine both the relational structure of the DEM system and practitioners’ experiences within it. Data were collected between August 2024 and June 2025 from participants recruited across national forums, professional networks, emergency services, territorial authorities, civil society organizations, and private sector actors. Interviews ranged from 43 to 96 minutes, with a mean of approximately 60 minutes, and analysis proceeded through iterative coding, constant comparison, and progressive category refinement until theoretical saturation was reached. Notably, stewardship was not a predetermined analytical construct; it emerged inductively as the integrative category explaining how participants understood coherence, legitimacy, learning, and adaptive coordination to be sustained across organizational boundaries.

Four interdependent themes emerged from the analysis. The first concerned governing a distributed system. Participants described uncertainty about who holds overarching responsibility for the system as a whole, with national oversight perceived as bounded to particular hazards rather than extending across the full breadth of interagency coordination. Recurrent misalignment between nationally designed arrangements and local operational realities was a persistent friction, sharpened by the fact that national guidance, absent legislative force, cannot be required to be followed. Participants also described ambiguity about which actors were recognized as legitimate contributors to the system, with the operational network extending well beyond formally prescribed structures.

The second theme centered on trust and inclusive participation as the relational infrastructure of coordination. Participants emphasized that the quality of pre-existing relationships was a more reliable predictor of coordination effectiveness than formal arrangements or statutory roles—one practitioner quipped that you need to build a relationship over eight cups of coffee before you can start asking for something. Civil society organizations were described as providing relational networks and community intelligence that are substantial but underrecognized within formal system definitions. Indigenous partnership emerged as a distinct dimension: under Te Tiriti o Waitangi, signed in 1840, Māori hold an enduring basis for partnership in governance, and participants described how the quality of that engagement directly shaped both the legitimacy and operational reach of the system, though Treaty-based partnership quality varied significantly across regions.

The third theme addressed information sharing and learning. The aspiration of a common operating picture was widely referenced but characterized as aspirational rather than operational—one participant observed that the only common thing about it is the word common. Incompatible information systems, platform fragmentation, and institutional protectiveness over data combined to produce what the authors call a feedback deficit: a system theoretically capable of shared sensemaking but operationally fragmented along institutional boundaries. Compounding this, insights gained through events and exercises were described as fading as urgency declined and personnel changed, leaving institutional memory residing in individuals rather than organizational systems and rendering learning sporadic rather than cumulative.

The fourth theme concerned sustaining capability and legitimacy over the long term. Resourcing was described as unevenly distributed, with reliance on locally generated ratepayer revenue producing what participants called a postcode lottery of capability, where organizational capacity reflects local financial circumstances rather than hazard exposure or statutory obligation. Workforce sustainability emerged as a significant concern, with 31 of 40 participants describing burnout, illness, recruitment delays, inconsistent professional standards, and the absence of any centralized repository for qualifications or competency requirements. Accountability, meanwhile, was described as diffuse, operating primarily through post-event inquiry rather than ongoing assurance of preparedness and coordination quality.

From these findings, the authors distill a three-pillar framework of system stewardship. Relational stewardship encompasses the trust, boundary-spanning networks, and inclusive participation through which coordination becomes possible, including sustained investment in Treaty-based partnerships with iwi and the formalization of relationships into organizational arrangements rather than individual positions. Informational stewardship covers shared situational awareness, data transparency, and feedback-informed learning, requiring both technical investment in compatible information management and cultural change that treats learning as a shared system function. Institutional stewardship concerns equitable resourcing, workforce capability, and accountability mechanisms that sustain capability and legitimacy over time. Crucially, the three pillars are interdependent: weakness in one degrades the others, while alignment across all three creates the conditions for sustained adaptive performance. Trust facilitates information sharing; shared understanding strengthens institutional confidence; equitable resourcing enables the sustained engagement through which relationships are built.

The practical implications are considerable. For national agencies, stewardship implies a reorientation of the governance function—from setting requirements to cultivating conditions, from assuring compliance to strengthening capability. For policymakers, the framework makes visible the structural interdependencies between funding, professional standards, and accountability mechanisms, demonstrating that reforms to any single dimension in isolation are unlikely to produce durable improvement. The authors caution that the conditions underpinning system stewardship should not be assumed to persist indefinitely; trust, shared learning, institutional legitimacy, and collaborative relationships require continual investment and renewal, and may be eroded by organizational change, resource constraints, and competing priorities. The study’s limitations are acknowledged: findings derive from a single national context, reflect participant interpretations rather than direct observation, and capture a single point in time, though the framework forms part of a cumulative research program. Ultimately, the study argues that strengthening disaster and emergency management requires moving beyond episodic leadership during events toward the deliberate, continuous stewardship of the relational, informational, and institutional conditions through which adaptive coordination, collective learning, and long-term system coherence are cultivated and sustained.

Subject of Research: System stewardship as a complex adaptive system governance framework for disaster and emergency management in Aotearoa New Zealand

Article Title: Stewardship for Disaster and Emergency Management: A Complex Adaptative System Governance Framework

Article References: Miller, T., Le De, L., & Hore, K. (2026). Stewardship for Disaster and Emergency Management: A Complex Adaptative System Governance Framework. International Journal of Disaster Risk Science, 17(4), 725-738. https://doi.org/10.1007/s13753-026-00762-6

Image Credits: AI Generated

DOI: 10.1007/s13753-026-00762-6

Keywords: disaster and emergency management, system stewardship, complex adaptive systems, meta-governance, governance framework, adaptive coordination, Aotearoa New Zealand, trust and collaboration, institutional memory, emergency management workforce, Māori partnership, disaster risk science