Academic General Internal Medicine, or GIM, sits at the crossroads of patient care, education, research, and systems improvement. For those who aspire to lead, the skills cultivated through a generalist perspective provide a strong foundation for navigating volatility, uncertainty, complexity, and ambiguity, the conditions collectively known as VUCA. From this vantage point, GIM leaders integrate evidence across domains, translate it for different audiences, and keep teams moving when answers are incomplete. They frame problems, set shared aims, and adapt as contexts shift. In unpredictable environments, the ability to manage uncertainty, utilize practical judgment, foster inclusion, and integrate systems thinking enables GIM leaders to deliver high-value care, effective education, and impactful research and policy.

Examining leadership in healthcare generally, and academic medicine specifically, through generalist and specialist lenses highlights how generalist leadership provides the integrative capacity to align, translate, and apply specialist expertise in complex systems. The generalist–specialist contrast clarifies why breadth supports leadership in medicine: specialists excel in niches and with precision, while generalists remain agile for a wide variety of occurrences and are arguably the best approach with an unknown future. Many of the most complex challenges in healthcare—managing multimorbidity, addressing care gaps, reducing health inequities, and implementing new technologies—are fundamentally systemic. Generalists synthesize recommendations, balance risks, elicit patient preferences, and unite multidisciplinary teams. When the path is uncertain, leadership is not about superseding experts but about integrating expertise to develop patient-, learner-, and population-centered plans. Any academic leader, specialists included, can possess these skills; however, they should be identified as a generalist leadership skill set.

In volatile settings, generalist leaders often frame issues and integrate data, while specialist leaders optimize and articulate a path forward. Generalist leaders in medicine operate within these realities daily while anticipating future directions. They already navigate shifting technologies, fiscal pressures, expanding patient access to artificial intelligence, and evolving learner needs, requiring problem-solving and action in settings defined by uncertainty. Generalist leadership skills are embedded in the daily work of medicine. Integrative clinical judgment requires balancing conflicting guidelines, navigating transitions across settings, and aligning care plans with what matters most to patients. Cross-disciplinary communication connects patient priorities with colleagues, care teams, payers, and community partners to coordinate action around shared goals. Networked execution depends on understanding how systems function, mobilizing the right people and resources, and translating observations into meaningful action. Emotional regulation and inclusion foster psychologically safe environments for learners, staff, and patients, particularly during difficult conversations and periods of change. Finally, comprehensivists, including general internists, pediatricians, and family physicians, bring a whole-person perspective that integrates competing priorities, navigates complexity, and connects diverse viewpoints in service of patients, teams, and communities.

Generalists are adept at identifying and diagnosing situational challenges and selecting appropriate responses. The VUCA framework offers one practical tool for understanding what is driving a situation. Is the challenge being driven by volatility, uncertainty, complexity, or ambiguity? Once the underlying context is identified, leaders can align actions with the demands of the situation. In volatile environments, generalist leaders build in flexibility through adaptable timelines and diversified approaches. In uncertain situations, they create information through disciplined experiments and small pilots, much as clinicians gather additional data when confronted with diagnostic uncertainty. When faced with complexity, they standardize and simplify processes, clarify roles, and map workflows and handoffs to better understand the interdependencies that shape clinical and learning environments. In ambiguous situations, they create pilots and prototypes to clarify the path forward.

The central generalist leadership inquiry is: which problem are we solving, for whom, and how will we know? A VUCA lens for a rapidly changing environment would ask, for volatility, how do we ride this change; for uncertainty, what would reduce the unknowns; for complexity, where and what are the relationships that matter most; and for ambiguity, what is really the problem we are trying to solve? This diagnostic habit matters because misreading the driver of a crisis leads to misaligned responses. Treating an ambiguous problem as if it were merely volatile, for example, produces frantic activity rather than genuine clarification. Naming the condition first converts diffuse anxiety into a targeted leadership question, and that question then disciplines the choice of experiments, timelines, and stakeholder conversations that follow.

In clinical settings, generalist leadership involves serving as the primary interpreter for patients and care systems. Generalists take on the first and often hardest work: making sense of undifferentiated presentations, stitching together discordant data, and leading shared decisions when certainty is not on the table. They are the managers of algorithmic and guideline outputs, applying probabilistic reasoning, guiding patients through complex findings, and participating in co-produced, shared decision-making, which fosters inclusion, trust, and actionable choices. They also design the team, clinical or research, on purpose: generalists frame and communicate the uncertainty and keep watch for context shifts, reframing hypotheses and diagnoses as signals change. This approach also role models transparent uncertainty management for learners and team members, showing that admitting incomplete knowledge is a professional strength rather than a liability.

Learners are stepping into clinics and classrooms where numeracy, probabilistic reasoning, the ability to spot algorithmic bias, and true cross-disciplinary collaboration are baseline rather than bonus skills. In class and on rounds, GIM leaders make it safe to disagree and to say I do not know. Differing points of view help improve diagnosis, enhance cooperation, and strengthen team resilience. Generalist leaders often take an asset-based approach, which reframes learners and colleagues as reservoirs of capacity rather than deficits to be fixed. The GIM educator can recognize and reward synthesis, such as reconciling conflicting guidelines and aligning plans with social context, rather than focusing solely on medical knowledge as fact-based. These leaders can foster metacognitive skills and resilience by acknowledging the cognitive and emotional demands of the profession. Educational leaders should measure success by what and how learners adapt to uncertainty, not only by what is taught.

GIM research is inherently integrative, spanning clinical epidemiology, patient safety, health services research, implementation science, health equity, informatics, and community-engaged initiatives. It includes bringing in collaborators who emphasize shared purpose and respect in research and system design. It may mean inviting patients, community partners, and frontline staff to help define questions, test ideas, and judge what success looks like. Today’s scientific challenges rarely fit neatly within a single discipline. In an era of increasing funding pressures and team-based research, GIM research leaders connect experts across fields, bridge research and practice, and help ensure that scientific advances improve the health of patients and communities. Generalists thrive by taking undifferentiated problems, reconciling discordant data and guidelines, aligning questions with context, and engaging stakeholders. The GIM leader uses LEAD: learn, elevate, adapt, drive. They learn broadly, elevate others through sponsorship, adapt through disciplined experiments and pilots, and drive change with a clear vision.

Comprehensivists, whether general internists, pediatricians, or family physicians, frame context, define the questions that guide action, and communicate uncertainty honestly. These shared ways of working create a foundation for collaboration and leadership across generalist specialties, for example advocating for high-value primary care. In a period of significant change in healthcare and academic medicine, the comprehensivist approach brings together whole-person thinking and the ability to move forward amid volatility, uncertainty, complexity, and ambiguity. Generalist leadership is the discipline of building iterative connections while working through uncertainty. Both GIM and generalist leadership build a habit of mind that turns ambiguity into coordinated, meaningful action in clinics, classrooms, and research. When leaders name the kind of VUCA they are facing and LEAD, they help their teams move with clarity and courage, even when certainty is not on the table.

Subject of Research: Generalist leadership and VUCA in internal medicine

Article Title: A Heuristic for Generalist Leadership in Times of Volatility, Uncertainty, Complexity, and Ambiguity (VUCA)

Article References: Lypson, M. L. (2026). A Heuristic for Generalist Leadership in Times of Volatility, Uncertainty, Complexity, and Ambiguity (VUCA). Journal of General Internal Medicine. https://doi.org/10.1007/s11606-026-10772-1

Image Credits: AI Generated

DOI: 10.1007/s11606-026-10772-1

Keywords: VUCA, generalist leadership, internal medicine, healthcare leadership, complexity science, medical education, health services research, clinical decision-making, systems thinking, academic medicine, Heuristic, Generalist

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 case centers on a 60-year-old Italian woman, a retired hairdresser, who arrived at an osteopathic clinic carrying a decade of accumulated suffering. Her low back pain had begun roughly ten years earlier, coinciding with a sacral trauma from a fall, and had since spread its misery outward—radiating to her coccyx, entangling her shoulders, and triggering recurrent cervicogenic headaches. But her clinical picture was far more tangled than a simple spine problem. She lived with type 2 diabetes managed by metformin, essential hypertension treated with valsartan and aspirin, pre-existing depression dating back to 1994 and managed with sertraline, and sleep disturbances requiring a CPAP machine. She was, in the language of modern medicine, a textbook case of multimorbidity—precisely the kind of patient for whom standard protocols so often fall short.

Her prior encounters with the healthcare system had, if anything, deepened the problem. An orthopedic specialist had offered her two options: physiotherapy and glucocorticoid injections. She began only the structured exercise component—motor control and core strengthening work—and abandoned it after perceiving no benefit. When an acute flare-up struck, the specialist prescribed steroid injections and delivered what the report describes as alarming advice: stop working, avoid physical activity, give up even light exercise. She obeyed the activity-avoidance directive but refused the injections, worried about adverse effects and drug interactions with her existing medications. The result was eight months of physical inactivity, a spiral of kinesiophobia—the fear of movement itself—and a progressive erosion of her daily functioning, social participation, and sense of well-being.

What happened next is what makes this case scientifically fascinating. Rather than reaching for another standardized protocol, the treating osteopath turned to the Cynefin Framework, a sense-making tool derived from complexity science originally developed for organizational decision-making. The framework sorts problems into distinct domains—clear, complicated, complex, and chaotic—each demanding a different decision logic. At the first encounter, the researchers note, the patient’s entire clinical picture sat in the central domain of confusion: an entangled, undeciphered mix of persistent pain, movement fear, distrust born of failed treatments, an unaddressed decade-old trauma, and layered chronic disease. Neither practitioner nor patient could yet say which rules applied to which piece of the puzzle.

The first act of care, therefore, was not a technique but a decomposition. Together, osteopath and patient systematically reassigned each element of her story to its proper domain. The glucocorticoid injections and the rest advice were reframed as belonging to the clear domain—linear, pathophysiological if-it-hurts-rest logic that, however internally coherent, the 2023 World Health Organization guidelines explicitly reject for chronic primary low back pain. The generic physiotherapy she had abandoned was placed in the complicated domain: guideline-concordant in principle, requiring expertise, but delivered without individualization or graded exposure to the movements she feared. Her diabetes, hypertension, and sleep disorder were classified as complicated problems governed by known rules and specialist management. The truly stubborn elements—her nociplastic pain and her kinesiophobia—landed squarely in the complex domain, where cause and effect emerge unpredictably and can only be understood in retrospect.

This reclassification was not academic hair-splitting; it dictated entirely different treatment strategies. For the chaotic phase, where no exploratory touch could yet reproduce a workable movement pattern, the osteopath proposed functional neuromyofascial activity—an active-participatory approach in which the patient performs fundamental movements like rolling, squatting, and walking under gentle guidance, allowing a clinical pattern to gradually surface. Once patterns emerged, care shifted into the complex domain’s probe-sense-respond logic: osteopathic manipulative treatment, including myofascial release applied to the sacral region and balanced ligamentous tension applied to the cervical spine, right hip, and right knee, was iteratively adjusted to the patient’s real-time response. Every technique’s endpoint was the palpated tissue release itself, not a fixed timer—a response-led philosophy in which dose was co-constructed moment by moment between practitioner and patient.

The results, documented across five encounters spanning roughly 10.6 months, were striking. Her Visual Analogue Scale pain score plummeted from 7 out of 10 at baseline to 2 by the second encounter, just seven days later, with no changes to her medication or any co-interventions in between—and it held steady at 2 through the final follow-up in January 2026. Her RAND-36 Health Survey scores, measured before the first treatment and again at the third session, showed dramatic numerical improvements across all eight domains: physical functioning leapt from 25 to 85, physical limitations from 0 to 75, social functioning from 25 to 80, and emotional role limitations from 33 to a perfect 100. Her functional neuromyofascial activity screening score climbed from 4—general motor dysfunction—to 13, firmly in the functional motor ability range. Perhaps most tellingly, she resumed walking and dancing, activities she had surrendered years earlier.

Yet the authors are refreshingly, rigorously honest about what this case can and cannot prove. In a single, uncontrolled report, clinical improvement cannot be attributed to the intervention alone. Regression to the mean, natural symptom fluctuation, expectancy effects, and the therapeutic alliance all remain plausible contributors—particularly given the low baseline scores and the rapid early change. The RAND-36 scores at later encounters were confirmed by patient recall rather than independently re-administered, making the long-term maintenance descriptive rather than a formal re-measurement. The Cynefin mapping itself was assembled retrospectively from brief contemporaneous notes, and the authors explicitly caution that domain assignments reflect clinical judgment, not a validated classification. Even the somatic dysfunction findings rest on TART criteria—tissue texture, asymmetry, restriction, tenderness—whose inter-examiner reliability remains debated in the literature.

What the case does offer is a testable hypothesis and a provocative conceptual bridge. The authors propose that improvement likely arose through interacting behavioral and non-specific pathways: reduction of movement fear through graded exposure and reassurance, restoration of self-efficacy via shared decision-making and self-screening, resumption of physical activity after prolonged deconditioning, and short-term pain modulation through the contextual effects of therapeutic touch. Mechanistic claims about fascial release, inflammation, or fibroblast function were deliberately not invoked, since none were measured. The neuroaesthetic-enactive paradigm—the idea that touch operates as an embodied perceptual dialogue—served as a framework for the clinical interaction rather than a demonstrated cause of recovery.

The broader implications reach well beyond one patient’s spine. The authors situate their findings within the WHO’s 2023 guideline calling for age-sensitive, multidisciplinary, non-pharmacological care for adults over 60, and within global frameworks like the UN Sustainable Development Goal 3 and the WHO Decade of Healthy Ageing. They argue that complexity-informed, person-centered approaches may support functional ability in older adults more precisely than strategies that treat them as a generic vulnerable group. But they are equally candid about implementation barriers: the framework demands extended consultation time, formal training in complexity science, and remains interpretive rather than standardized. Controlled, prospective, multi-centre studies remain indispensable to determine whether this approach is feasible, reproducible, and effective across larger populations. For now, this single woman’s journey—from eight months of immobility to dancing again—stands as a hypothesis-generating beacon, suggesting that when chronic pain refuses to obey simple rules, the answer may lie in learning to navigate complexity itself.

Subject of Research: Complexity-informed osteopathic care for chronic primary low back pain in an older adult with multimorbidity

Article Title: Complexity-informed osteopathic care for chronic low back pain in a 60-year-old woman: a case report

Article References: De Rosa, U., Cairoli, A., Conti, E., Marcucci, M., Baroni, F., & Lunghi, C. (2026). Complexity-informed osteopathic care for chronic low back pain in a 60-year-old woman: a case report. Frontiers in Musculoskeletal Disorders, 4, Article 1872226. https://doi.org/10.3389/fmscd.2026.1872226

Image Credits: AI Generated

DOI: 10.3389/fmscd.2026.1872226

Keywords: chronic low back pain, osteopathic manipulative treatment, Cynefin framework, complexity science, case report, kinesiophobia, biopsychosocial approach, myofascial release, healthy ageing, person-centered care, multimorbidity, non-pharmacological care

The story begins with Cordeschi’s original insight, which Gagliardi revisits and reinterprets. In the 1930s and 1940s, researchers in the emerging behavioral sciences began constructing machines—mechanical and, later, electronic artifacts—that could reproduce intelligent and adaptive behavior. These were not merely engineering curiosities. They functioned as scientific models: ways of testing hypotheses about how minds and nervous systems might work. Kenneth Craik’s 1943 proposal that organisms carry internal models of external reality, Warren McCulloch and Walter Pitts’s 1943 logical calculus of neural activity, and the later cybernetic machines of Norbert Wiener’s circle all exemplified what came to be known as the “Synthetic Method.” Rather than analyzing a natural system into its components and deriving its behavior from first principles, the synthetic method proceeds in the opposite direction: it assembles an artifact from simple parts and observes whether the target behavior emerges.

Gagliardi’s central move is to pair this familiar narrative with a less obvious one from physics. In 1920, Wilhelm Lenz proposed a radically simplified model of ferromagnetism, and his student Ernst Ising worked out its properties in 1925. The Lenz–Ising model represents a magnetic material as a lattice of spins, each of which can point up or down and interacts only with its nearest neighbors. Nothing about the model resembles a real magnet in its fine detail. Yet this deliberately crude artifact turned out to capture something essential about how collective phenomena—phase transitions, critical points, cooperative behavior—arise from local interactions. Gagliardi argues that the introduction of the Ising model in the 1920s represents a methodological turning point in the physical sciences directly analogous to the one occurring at the same time in the behavioral sciences: the embrace of artificial, simulative models as legitimate instruments of scientific understanding.

Why did both fields converge on this strategy? Gagliardi’s answer lies in the mathematics of complexity. In the kinetic theory of gases, the nineteenth-century triumph of Ludwig Boltzmann and James Clerk Maxwell, it was possible to move analytically from the microscopic behavior of particles to macroscopic laws: the model could be solved in closed form, yielding the ideal gas law. But the Ising model resists such treatment. It has been formally proven, in work by Sorin Istrail published in 2000, that computing the ground state of the Ising model is computationally intractable—an NP-hard problem. Assuming the widely held conjecture that P does not equal NP, no closed-form analytical solution exists except in trivial cases. The same wall of intractability confronts cognitive modelers: Paul Thagard and Kevin Verbeurgt showed in 1998 that a connectionist model of coherence as constraint satisfaction is equivalent to the NP-complete Max-Cut problem. When exact analysis is impossible, the only route to understanding is simulation—running the model and observing its behavior.

This is where the concept of computational irreducibility enters. For complex systems, whether biological or material, there is often no shortcut from the model’s specification to its outcomes; one must simply let the dynamics unfold. Gagliardi contends that this epistemic necessity, rather than any mere fashion or convenience, explains why both cognitive science and statistical physics became what he calls “complexity sciences.” Both disciplines, in his phrase, “discovered the artificial”: they came to accept that understanding a system may require building an internal model of it whose behavior can be observed, even when no analytic derivation of that behavior is available. The artificial model becomes not a substitute for explanation but the very medium of explanation.

The philosophical stakes of this claim are considerable. Traditional accounts of scientific explanation, descending from Galileo’s famous declaration that the book of nature is written in mathematical language, privilege derivation: to explain is to deduce consequences from mathematical first principles. Gagliardi argues that the synthetic method extends rather than abandons this Galilean language. The new language of models and simulations adds to mathematical description the capacity to create and run artificial systems whose behavior can be studied. He points to Ernst Mach’s nineteenth-century observation that all science seeks to replace or economize experience through the mental reproduction of facts—reproductions that are easier to handle than experience itself and can stand in for it. Simulation, on this view, is the modern technological fulfillment of Mach’s epistemology of thought-economies, closer in spirit to a gedankenexperiment than to a laboratory measurement.

The article also engages a live debate in the philosophy of science about the epistemic status of computer simulations. Are simulations experiments, or are they theory? Gagliardi notes that observing the physical system under study belongs to the empirical verification phase of the scientific method, whereas observing a simulation pertains to theory and to the deduction of a model’s properties. He acknowledges that this distinction is contested: philosophers such as Anouk Barberousse, Cyrille Imbert and Sara Franceschelli, Claus Beisbart, and Judith Jebeile have argued for a genuine affinity between experiments and simulations, while others, including Julian McClelland and Darrell Rowbottom, treat simulations as instruments rather than faithful recreations of phenomena. Gagliardi’s historical framing gives this debate new context: the question of what simulations are is inseparable from the century-long process by which both physics and the mind sciences learned to trust artificial models.

Perhaps the most provocative element of the paper is its appeal to weak emergence, a concept developed by Mark Bedau, to characterize the new kind of scientific understanding that the synthetic method affords. In systems like the Ising model, there is no causal reductionism linking the internal model to macroscopic phenomena in the way that the kinetic theory analytically links molecular motion to the gas laws. Instead, macroscopic patterns depend on, but are not derivable from, the micro-dynamics—a dependence that must be exhibited through simulation rather than demonstrated through derivation. Gagliardi suggests that explanation centered on the internal functional organization of systems, in the tradition of Robert Cummins’s functional analysis, offers a shared explanatory idiom for both disciplines. Understanding becomes a matter of seeing how a system’s organization produces its capacities, a form of comprehension that simulation makes possible and that pure mathematics alone cannot deliver.

The convergence Gagliardi describes has contemporary resonance. The 2024 Nobel Prize in Physics, awarded for foundational work on artificial neural networks, underscored how deeply the physics of collective phenomena and the science of mind have become intertwined: John Hopfield’s 1982 neural networks drew explicitly on statistical-mechanical ideas, and Boltzmann machines, developed by David Ackley, Geoffrey Hinton and Terrence Sejnowski in 1985, carry the name of the great statistical physicist. Recent work by Iris van Rooij and colleagues has argued for reclaiming artificial intelligence, with its computational models and simulation techniques, as a theoretical foundation for cognitive science, with computational complexity theory playing a central role. Gagliardi’s historical analysis provides the deep background for these developments: they are not accidents of interdisciplinary fashion but expressions of a shared “Culture of the Artificial” that has been consolidating for a hundred years.

The paper, dedicated to the memory of Roberto Cordeschi, ultimately offers a vision of scientific unity that is neither reductionist nor pluralist in the usual senses. Cognitive science and statistical physics remain distinct disciplines with distinct subject matters, yet they are aligned at a deeper epistemic level: both have learned that the road to understanding complex, adaptive, collective phenomena runs through the construction of artificial models and the observation of their simulated behavior. In extending the Galilean language of mathematics into a language of model-building and simulation, twentieth-century science did not abandon rigor; it redefined what counts as explanation for a world whose systems are too complex to be solved, and rich enough to be understood only from the inside out.

Subject of Research: The historical convergence of the synthetic method and artificial modeling in cognitive science and statistical physics

Article Title: The discovery of the artificial and the use of the synthetic method in cognitive and physical sciences

Article References: Gagliardi, F. (2026). The discovery of the artificial and the use of the synthetic method in cognitive and physical sciences. AI & SOCIETY. https://doi.org/10.1007/s00146-026-03291-4

Image Credits: AI Generated

DOI: 10.1007/s00146-026-03291-4

Keywords: synthetic method, discovery of the artificial, cognitive science, statistical physics, Ising model, cybernetics, computer simulation, computational irreducibility, weak emergence, complexity science, Roberto Cordeschi, history of science

The central premise of the study is deceptively simple but consequential: cities do not function as islands. Labor markets, innovation networks, infrastructure systems, and digital services routinely spill over municipal borders, tying together core cities, suburbs, and smaller towns into integrated functional regions. If smart city performance—measured through indicators of digital infrastructure, technological innovation, human capital, and connected urban services—is produced within these wider regional systems, then assessments that stop at the city limit risk misreading both the sources and the consequences of urban smartness. The authors argue that regional scaling, the way performance changes with the size of a regional system, provides a crucial test of whether smart urban capabilities are driven by local assets or by broader structural dynamics.

To carry out this analysis, the research assembles regional-level data across European territories, combining indicators of smart urban development with measures of regional population, economic output, and spatial structure. The methodological logic draws on scaling analysis, an approach borrowed from complexity science that has previously been used to show how many socioeconomic outputs—from patents to wages to the incidence of certain social phenomena—tend to increase superlinearly with city size. Applied at the regional scale, the question becomes whether smart performance grows proportionally, sublinearly, or superlinearly as regions become larger and more densely connected, and whether that scaling behavior differs systematically across parts of Europe.

The study’s mapping of spatial distribution reveals a sharply uneven geography. Smart urban capabilities cluster in a limited set of regions, typically those anchored by large metropolitan areas with strong research institutions, dense producer-service sectors, and well-developed digital infrastructure. This pattern echoes long-standing observations about European spatial economics, including the persistence of a core-periphery gradient running roughly from the so-called blue banana of urbanization through southern Germany, the Low Countries, and parts of northern France, toward more sparsely provisioned peripheral regions in southern and eastern Europe. But the regional analysis adds an important nuance: proximity to a high-performing core does not automatically translate into high regional performance, and some regions without globally famous capitals nonetheless demonstrate robust smart urban profiles built on secondary cities and coordinated regional networks.

The scaling results carry particular significance for urban policy. When the researchers examine how smart performance varies with regional size, they find evidence that the relationship is not uniform across Europe. In some regional contexts, performance rises more than proportionally with size, consistent with agglomeration effects in which larger, denser regional systems generate disproportionate returns on digital and innovative activity. In others, the relationship flattens or weakens, suggesting diminishing returns or structural constraints—such as fragmented governance, uneven infrastructure investment, or dependency on a single urban core—that prevent large regions from converting size into smart performance. This heterogeneity undermines the idea that there is a single universal law of smart urban growth and instead points to regionally specific development regimes.

One of the study’s most provocative implications concerns the proliferation of smart city rankings and benchmarks. Municipal league tables, the authors suggest, can be misleading in two directions. A small city may appear exceptionally smart on a per-capita basis while depending heavily on regional universities, transport systems, and firms headquartered elsewhere; conversely, a sprawling polycentric region may look mediocre when judged by its largest municipality alone while its distributed network of towns collectively delivers strong digital and innovative capacity. By evaluating performance at the regional scale, the study provides a corrective that better reflects how the production of smart urban outcomes is actually organized in functional space.

The findings also speak to a central debate in regional science: the tension between concentration and dispersion of high-value activities. If smart capabilities reward agglomeration with superlinear returns, then market forces and even some policy interventions will tend to deepen regional inequality, funneling talent, investment, and infrastructure toward already advantaged metropolitan areas. The study’s evidence of heterogeneous scaling offers a more hopeful reading. Where regional systems—particularly polycentric ones with multiple cooperating cities—achieve strong performance, the lesson is that scale advantages are not the exclusive property of single dominant metropolises. Connectivity, complementarity, and coordinated governance among smaller cities can substitute, at least in part, for raw metropolitan size.

For European policymakers, the research lands at a moment when cohesion policy, digital transition funding, and smart city programs are being recalibrated. The European Union’s digital and green transitions explicitly target territorial balance, yet the study suggests that policy instruments designed around individual municipalities may systematically miss the regional systems in which smart performance is actually produced. Investment in broadband or innovation vouchers allocated city by city may underperform relative to instruments that reward inter-municipal cooperation, shared regional data platforms, and integrated transport-and-digital planning. The scaling evidence implies that the effectiveness of such interventions will differ by regional context, arguing for territorially differentiated strategies rather than one-size-fits-all smart city templates.

The study is candid about the limits of its evidence base. Regional indicators of smart urban performance remain imperfect proxies for the underlying phenomena they seek to capture, and data availability varies across countries, complicating pan-European comparison. Scaling relationships, moreover, are correlational: demonstrating that performance rises with regional size does not by itself identify the mechanisms—labor pooling, knowledge spillovers, infrastructure economies, or network effects—that drive the pattern. The authors call for finer-grained, longitudinal work that can trace how regional smart performance evolves over time and how specific policies alter scaling behavior. Still, the analysis marks a meaningful step in relocating the smart city debate from the showcase municipality to the regional systems in which urban intelligence is embedded.

In the end, the research reframes a familiar question with fresh analytical force. The smart city narrative promised that technology would make urban life more efficient, sustainable, and responsive; this study suggests that whether that promise is fulfilled depends less on any single city’s gadgetry than on the scale, structure, and connectivity of the regions those cities inhabit. As Europe confronts the twin challenges of digital transformation and territorial cohesion, the message is clear: the future of smart urbanism will be decided not city by city, but region by region, and policies that recognize this spatial reality are far more likely to deliver smartness that is both high-performing and broadly shared.

Subject of Research: Spatial distribution and regional scaling of smart city performance across European regions

Article Title: Beyond smart cities: spatial distribution and regional scaling performance in European regions

Article References: Dai, Y., Hasanefendic, S., & Bossink, B. (2026). Beyond smart cities: spatial distribution and regional scaling performance in European regions. npj Urban Sustainability. https://doi.org/10.1038/s42949-026-00474-2

Image Credits: AI Generated

DOI: 10.1038/s42949-026-00474-2

Keywords: smart cities, European regions, urban scaling, regional development, urban sustainability, digital infrastructure, spatial distribution, agglomeration, territorial cohesion, polycentricity, regional policy, complexity science