Towards a mock quantum resilience theory for disaster risk reduction Open Access

Resilience, defined as the capacity of a system to “develop in dynamic environments faced with true uncertainty and the unexpected, like steering a vessel in turbulent waters” (Folke et al., 2010), has become a cornerstone of disaster risk reduction (DRR) and sustainability science (Alcántara-Ayala et al., 2022). International frameworks like the Hyogo Framework for Action and the Sendai Framework for Disaster Risk Reduction cement resilience as a guiding principle in DRR. Yet despite its popularity, resilience remains an evolving and contested concept (Alexander, 2013). Scholars debate whether a resilient system resists change and returns to equilibrium, or one that transforms to a new stable state, and whether resilience is an outcome (bouncing back) or a process of adaptive change (Pimm, 1991; Manyena, 2006; Alexander, 2013). These ambiguities underscore the need for more nuanced theoretical frameworks to better conceptualize and operationalize resilience in complex, real-world systems.

This concept paper proposes a “mock Quantum Resilience Theory” as a novel theoretical lens for understanding resilience in complex adaptive systems. We draw disciplined analogies (Weick et al., 1989) from quantum mechanics, including contextuality, superposition, interference, and entanglement, to reimagine how resilience behaves in socio-ecological and socio-technical systems. Importantly, this approach is analogy-based: we do not claim disaster-affected communities are literally quantum systems. Rather, inspired by developments in quantum social science and cognition (Busemeyer and Bruza, 2012; Haven and Khrennikov, 2013), we employ quantum principles as heuristics or metaphors (in the spirit of “disciplined imagination”) to address known challenges in resilience thinking such as vagueness and lack of operational precision. Complex Adaptive Systems (CAS) theory is the natural home for this integration, as CAS already emphasises uncertainty, non-linearity, emergence, and cross-scale interactions (Holland, 1992; Levin, 1998; Folke, 2006; Walker et al., 2004). Quantum-like models resonate with these features, potentially offering a richer, context-sensitive, and probabilistic toolkit to capture aspects of resilience that classical linear models struggle to express.

In what follows, we outline how four core quantum concepts can be mapped as analogies to resilience phenomena. We then discuss the theoretical and practical significance of this framework for disaster risk reduction, including how it addresses current limitations in resilience thinking. We conclude with remarks on future research directions, as this concept note is intended to be the first in a series of contributions on quantum-inspired resilience.

We identify four quantum-inspired concepts that together form the basis of the mock Quantum Resilience Theory. Each is defined in simple terms and then translated into a resilience context in complex systems.

In quantum physics, the outcome of a measurement can depend on the context, specifically the other measurements being performed, and their order (Pothos and Busemeyer, 2013). By analogy, resilience is contextual: the assessed resilience of a system depends on how and when it is measured (Manyena, 2006). Different assessment frameworks, indicators, or timing can yield divergent resilience evaluations, much as a quantum property can seem to change with different measurement lenses (Aharonov and Vaidman, 1990). This highlights that resilience is observer-dependent or “in the eye of the beholder.” There is no single, objective metric of resilience, rather, multiple perspectives and path-dependent histories must be acknowledged (Elmqvist et al., 2019). For DRR practice, contextuality suggests that evaluating and building resilience should be a pluralistic exercise. It requires examining a system's robustness, adaptability, and transformative capacity from various angles (social, economic, infrastructural, spatial, and temporal) instead of relying on a single static score (Alcántara Ayala et al., 2022). It also implies the sequence of events matters: the impact of a shock and a system’s resilience to it may differ if conditions or prior disturbances change, an idea akin to path-dependence in CAS (Kuosa, 2007).

A fundamental quantum idea is that a system can exist in a combination of states at once (until observed) (Wendt, 2015). In other words, a resilient system can harbour multiple potential states or futures simultaneously. For example, a community might be poised on the threshold of either collapse or recovery. It may have latent resilience that remains unexpressed until a disturbance “collapses” the system into a single outcome. This is analogous to Schrödinger's cat thought experiment (alive and dead, until the box is opened, at which point we observe one outcome) (Myhrehagen and Bungum, 2016) and reflects the ambiguity before a crisis outcome is realised. The superposition analogy provides a language for understanding uncertainty and latent capacities in complex systems. Therefore, a community can exist in a superposed state of multiple trajectories (e.g. decline and renewal potential) until stressors or interventions force a resolution. In practical DRR terms, embracing superposition involves preparing for multiple possible futures instead of relying on a single expected scenario (therefore asking the “when?” and not only “what if?” questions) (Van Niekerk, 2020). Scenario planning and adaptive management are informed by this view. We treat the system as if it “exists” in several states (best case, worst case, status quo) and keep options open until events unfold. This mindset guards against tunnel vision and encourages early warning and monitoring, since a small trigger can tip the balance between potential states near a critical threshold (Scheffer et al., 2001).

In quantum mechanics, interference refers to the phenomenon where probability waves overlap, producing outcomes that are not simply the sum of individual effects (Sinha and Ghosh, 2025). They can amplify (constructive interference) or cancel each other (destructive interference). The analogy in resilience is that multiple disturbances or interventions can interact nonlinearly, leading to outcomes that defy linear addition. In DRR, we already observe this: two moderate stressors together might produce catastrophic impacts far beyond each alone (synergistic failure), or occasionally one shock might diminish the effect of a subsequent one (e.g. a small fire preventing a larger wildfire – a destructive interference in risk). Likewise, policy interventions can interfere: a well-meaning economic programme and a social initiative might either reinforce each other's benefits or undermine each other if misaligned. Recognising interference compels us to move beyond assessing risks in isolation. It underscores the importance of systemic risk assessments that capture various vulnerabilities, hazard interactions, cascade effects, and policy trade-offs. Rather than assuming the whole equals the sum of parts (Coetzee et al., 2016), we look for overlapping effects, analogous to “phase alignment” of waves, where timing and compatibility of multiple actions determine resilience outcomes. For practitioners, this means coordinating interventions to avoid negative interference (e.g. avoid simultaneous measures that clash) and planning for compound events (like the joint occurrence of pandemic and tropical cyclones) in an integrated way.

Entanglement describes a condition where parts of a system are so deeply interconnected that their states are inseparable (Horodecki et al., 2009). Measuring one immediately affects the other, no matter the distance. In resilience terms, components of a socio-ecological system can be entangled, exhibiting strong correlations that traditional independent analysis cannot explain (Zurlini et al., 2006). For instance, the resilience of a local community may be entangled with the resilience of its environment: an indigenous community's social resilience (cultural integrity, livelihoods) is inextricably linked to the health of local ecosystems. Similarly, critical infrastructure networks (power, water, communication) often fail or recover together due to tight interdependencies. The entanglement analogy reinforces holism in resilience assessment. It urges caution against examining subsystems in isolation or assuming risks can be treated sector by sector. Instead, it highlights that the true “state” of resilience belongs to the joint system, not to individual parts considered separately (Coetzee et al., 2016). Highly entangled systems can propagate shocks rapidly (cascading failures), so resilience building might aim to introduce some modularity or intentional “disentanglement” of critical connections to prevent systemic collapse (Shai et al., 2014). On the other hand, entanglement also presents opportunities for synchronous risk reduction. Improvements in one area can positively impact the other. Overall, this analogy advocates for integrated strategies (e.g. combining social and ecological resilience efforts, or coordinating multi-infrastructure safeguards) and the development of holistic indicators that capture the interdependence of system components.

Through these four analogies, the mock Quantum Resilience Theory provides a cohesive conceptual framework. It synthesises several ideas familiar in complexity science, such as path dependence, multiple equilibria, nonlinear effects, and cross-scale linkages, under a new interdisciplinary vocabulary. Crucially, it also generates testable propositions. For example, we can hypothesise that resilience outcomes will show order-dependence (contextuality), or that combined stressors produce non-additive impacts (interference), which future research can investigate empirically. In this way, the framework not only enriches theory but points to concrete research questions (e.g. how to detect “quantum-like” patterns in resilience dynamics).

The theoretical significance of the quantum analogy framework lies in a more nuanced articulation of resilience that explicitly accounts for context, uncertainty, and interconnectedness. By legitimising multiple perspectives and potential states, it moves us away from seeking a single, one-size-fits-all definition of resilience. For example, the longstanding issue that resilience means different things to different scholars (bouncing back vs. transformative change) can be reframed. Instead of a weakness, this flexibility is an inherent feature of complex systems. Contextuality provides a principled way to incorporate diverse viewpoints and path dependencies as part of the resilience concept, rather than viewing them as unwanted ambiguity. Superposition helps reconcile seemingly contradictory aspects of resilience (stability and change) by allowing a system to embody both tendencies until circumstances “choose” an outcome. In other words, a community can be both robust and fragile in different respects, with the dominant trait emerging only in context. This perspective alleviates the conceptual tension noted by Alexander (2013) regarding whether resilience implies resisting change or adapting to change. Under the quantum view, it can imply either or both, depending on situational factors.

Practically, the quantum resilience framework suggests fresh strategies for DRR. If order and context matter (contextuality), then disaster risk reduction and resilience planning should consider the sequence of events and the frames of evaluation. For instance, preparing for an earthquake followed by a pandemic is not the same as preparing for a pandemic followed by an earthquake, nor is building capacities in agriculture before creating a local market the same as creating a market and then building capacities in agriculture. Each sequence poses unique challenges. This insight encourages scenario exercises that go beyond static risk checklists, considering how prior shocks and stressors impact of subsequent ones. If interference effects are real, it underscores the need for coordination. Interventions in isolation might fail if they inadvertently interfere destructively with other initiatives or stresses. DRR programs should be designed with synergy in mind (such as with climate change adaptation). For example, aligning sustainable development programmes with social cohesion efforts so that they reinforce each other rather than compete. Recognising interference also aligns with the shift in DRR toward managing compound hazards, cascading events, and various levels of vulnerability, providing a conceptual rationale for why the whole can be more (or less) than the sum of its parts. Lastly, entanglement highlights that building resilience in one domain (say, infrastructure) in isolation may be ineffective if another coupled domain (governance or environment) is left weak. It justifies integrated risk management and cross-sector partnerships. For example, strengthening a power grid's resilience should go hand-in-hand with water and communication systems’ resilience because of their entangled operation. This integrated approach is increasingly recognised in DRR best practices, and the quantum analogy provides a scientific metaphor to communicate and analyse such integration.

By embedding these principles, quantum resilience theory contributes to disaster risk management by not only identifying gaps in current thinking but also offering a unifying narrative to address them. It encourages the use of advanced modelling techniques (e.g. simulation of interference between hazards, vulnerabilities or network analysis of entangled infrastructures). It supports a culture of adaptive management, continually updating understanding as “measurements” of the system reveal new information, much as observing a quantum system yields new states. Although introducing quantum metaphors might seem to add complexity, it ultimately guides more realistic strategies by highlighting critical phenomena (like nonlinearity and feedback) that conventional models might oversimplify. In summary, the approach emphasises that resilience is a system's property with inherently probabilistic and relational characteristics. This can inspire more robust risk assessments and innovative interventions in practice.

Current resilience scholarship has faced criticism for conceptual vagueness and difficulties in application. The quantum-inspired framework directly addresses several of these limitations. First, it acknowledges multiple valid frames of reference for resilience (via contextuality), which transforms what was seen as a lack of consensus into a feature of the system. Different stakeholders or scales may indeed experience resilience differently, and our theory provides a way to systematically include that variability. Rather than forcing a single metric, the focus shifts to understanding how various definitions and measurements relate to one another within the entire system. Second, the apparent paradox between resilience as resistance versus transformation (stability vs. change) is mitigated by the notion of superposition. A resilient system can have the capacity for both continuity and innovation, whose capacities can manifest depending on contextual triggers, without invalidating the other capacity. This helps resolve what Manyena (2006) and others have noted as a conceptual muddle: we no longer need to choose one definition of resilience if we can view it as state-dependent and potential-rich.

Additionally, by framing well-known ideas (path dependence, regime shifts, compound risks, etc.) in a quantum analogy language, the theory brings coherence and integration. What might appear to be disparate aspects of resilience are tied together: perspective-dependence, uncertainty, nonlinear interactions, and cross-scale linkages become four facets of a single framework. This integrative quality is an advance over fragmented lists of resilience attributes. It provides researchers with a common narrative to generate hypotheses and compare findings. Therefore, it stands to reason that one can investigate a case of community resilience through all four lenses, which might reveal insights that a narrower analysis would miss. There is also a didactic benefit. Complex systems’ behaviour can be abstract, but quantum metaphors (like Schrödinger’s cat or interference patterns) are vivid and may aid in communicating resilience ideas to wider audiences, as long as we clarify that these are analogies, not literal quantum effects.

We are cautious to avoid over-extending the metaphors. The framework is explicitly labelled “mock” to signal that these analogies are heuristic tools rather than strict physical descriptions. Some may argue that using quantum jargon to describe known complexity concepts is merely relabelling. Our counter-argument is that the quantum lens adds value by combining those concepts in a novel way and pointing toward new research questions. If an analogy does not yield additional insight or predictive power, then it should be refined or discarded. We also acknowledge that empirical validation of such analogies is challenging. Socio-ecological systems cannot be controlled in laboratories like particles. The true test of this theory's worth will be its usefulness: does thinking in these terms lead to better understanding, modelling, or building of resilience? We believe the early indications are positive, as this cross-disciplinary perspective has already sparked constructive dialogue between fields (e.g. psychology, ecology, physics) (Wendt, 2015; Haven and Khrennikov, 2013; Pothos and Busemeyer, 2013; Pasupuleti, 2025) and encouraged more holistic consideration of risk and resilience.

In this concept note, we have reinterpreted resilience through a quantum-inspired lens within the complex adaptive systems paradigm. The mock Quantum Resilience Theory reframes key attributes of resilience in terms of four quantum analogies, namely contextuality, superposition, interference, and entanglement. This is done to address the uncertainty, perspective-dependence, and interconnected nature of real-world resilience challenges. This cross-disciplinary approach enriches the vocabulary of resilience theory, offering a coherent framework to integrate multiple facets that classical approaches often treat separately. While metaphorical, the analogies are grounded in recognised complex system behaviours and respond to acknowledged gaps in current resilience thinking.

The ultimate value of this theoretical contribution will be measured by its impact on both research and practice. By viewing resilience through a new lens, we aim to inspire scholars and practitioners to spot novel patterns or solutions that were previously obscured. If this framework leads to improved modelling of disaster risk scenarios or more effective resilience-building interventions, it will have proven its merit beyond a purely conceptual exercise.

This article is intended as the first in a series exploring quantum-inspired resilience. In forthcoming works, we plan to operationalise and test these principles. One avenue is developing formal models or simulations embodying the analogies. For example, using quantum-like probabilities or network algorithms to simulate community development and observe whether “interference” or “entangled” effects emerge. Another avenue is empirical: identifying case studies or data sets that illustrate each analogy (such as instances of policy interference or cross-scale entanglement) and analysing them to see if the quantum lens yields fresh insights. We will also explore practical tools for practitioners, potentially translating quantum cognitive methods (e.g. quantum decision models) to socio-ecological contexts. Through these future studies focusing on operationalisation, empirical validation, and modelling, we aim to refine the quantum resilience theory and assess its real-world applicability. Our hope is that this ongoing research trajectory will build a robust evidence base for quantum-inspired resilience thinking in DRR, ultimately contributing innovative methods to the DRR toolkit and strengthening the resilience of communities facing an uncertain future.