The Global Learning Framework E-Book

Birgit Bortoluzzi

• Herausgeber: epubli
• Kategorie: Wissenschaft und neue Technologien
• Sprache: Englisch

Seitenzahl: 498

Veröffentlichungsjahr: 2026

Beschreibung

As the world grows more complex, understanding becomes a collective rather than an individual act. Systems do not react sequentially but simultaneously; developments overlap; and decisions emerge within a field of diverse signals and perspectives. Stability in such a world is no longer a static condition but a question of how we perceive, connect, interpret, and act when signals are fragmented and time windows are narrow. The Global Learning Framework (GLF) begins precisely here: as a universal meta architecture that does not simplify complexity but makes it intelligible. It translates six foundational concepts — visibility, structure, dynamics, interfaces, instability and capacity to act — into a circular mode of shared learning. Within this rhythm, the four pillars SEE, CONNECT, INTERPRET and ACT function not as linear steps but as recurring movements of collective sense making. The GLF does not replace existing methods; it provides a shared language that aligns heterogeneous data, perspectives and decision logics within an integrated risk and learning architecture. The GeoResilience Compass extends this by adding a spatial dynamic dimension, showing where instabilities may emerge, how they propagate, and which interventions could be systemically effective. Subsea cables illustrate this interplay: they combine technical precision, ecological sensitivity, maritime reality and institutional responsibility within a single system whose risks become visible only when multiple signals are considered together. The Subsea Edition demonstrates how the GLF can operate in a highly dynamic, ambiguous and internationally shared environment — while remaining equally relevant to epidemiology, urban resilience, energy systems, ecosystems, critical infrastructures and governance. In a time when global risks are increasingly intertwined and stability depends on anticipatory design rather than reaction, the GLF offers a way to see, connect, interpret, act and learn together.

Leseprobe

Imprint

Birgit Bortoluzzi

Burgwartstraße 25

01159 Dresden

Germany

Text: Copyright by Birgit Bortoluzzi

Cover Design: Copyright by Birgit Bortoluzzi

Publisher: Birgit Bortoluzzi, Burgwartstraße 25, 01159 Dresden, Germany

Version 1.0 (February 2026)

Note: This book was published independently. Distribution is provided by epubli – a service by neopubli GmbH, Berlin.

Distribution: epubli – a service by neopubli GmbH, Berlin

Copyright and Usage Rights: © 2026 Birgit Bortoluzzi. All rights reserved.

This publication - including its terminology, semantic architecture, governance logic and all visual and structural elements - is the intellectual property of the author. Redistribution, adaptation or translation of any part of this work (textual, visual or structural) is permitted only with explicit attribution and in accordance with the ethical principles outlined herein.

Collaborative use in humanitarian, academic or institutional contexts is expressly welcomed - provided that transparent governance agreements are in place. Commercial use or modification requires prior written consent.

Visual Material and Cover Design: All images, illustrations and graphic elements used in this book - including the cover and visual modules - are protected by copyright. Their use outside this publication is permitted only with explicit authorization and in accordance with the principles of semantic integrity.

Disclaimer: The contents of this book have been prepared with the utmost care and to the best of the author’s knowledge. They serve as strategic guidance, ethical reflection and operational support in complex crisis contexts. However, they do not replace individual consultation by qualified professionals, authorities or legal experts.

This e-book is the result of extensive development and design work. To safeguard the quality, integrity and continued evolution of this publication, unauthorized conversion, reproduction or distribution in alternative formats is not permitted. Purchasing the original edition ensures that the author’s work is respected and that future updates, extensions and improvements remain possible.

The author assumes no liability for decisions made on the basis of this work, particularly not for direct or indirect damages resulting from the application, interpretation or dissemination of its contents. Responsibility for use lies with the respective users and institutions.

About the Author

Birgit Bortoluzzi is a strategic framework architect, graduate disaster manager, author, and creator of the Geo Resilience Compass. She specializes in the development of epistemic, semantic, and resilience-oriented frameworks that are globally interoperable and follow a holistic 360-degree approach.

Her internationally applicable concepts are designed to help organizations and companies become more resilient in an increasingly fast-moving and highly complex world. Her frameworks integrate operational decision logics, uncertainty modeling, semantic stability, provenance structures, resilience-oriented governance, and much more — with the aim of strengthening the epistemic and structural foundations of modern ecosystems, whether in AI/Geospatial systems, zoonosis management, Long Covid contexts, sensitive conflict management, CBRN/Biosens environments or Earth observation.

Birgit is an active member of the leadership team of the IEEE P4011 "Recommended Practice for the Utilization of Earth Observation Data and Services in Multi-Hazard Disaster Management and Early Warning" Working Group, where she contributes to the development of responsible standards.

In May 2025, she presented innovative approaches for emergency responders, with a focus on fire services, at the Pracademic Emergency Management and Homeland Security Summit (Embry-Riddle University).

Her international engagement is shaped by the ambition to connect diverse disciplinary perspectives and to foster systemic, multi-layered thinking across sectors.

Her professional path is rooted in a lifelong fascination with complexity, communication, creative design, knowledge architectures, our global world and the people who live on it.

From crisis management and scenario planning to interdisciplinary analysis and governance questions, her work is guided by a deeply human motivation: to structure complexity, strengthen collective responsibility, and contribute to a future in which our global community — despite already more than 400 million Long Covid patients, a steadily growing number of chronic illnesses, increasing extreme weather and disaster impacts, and diverse conflicts — has a real chance to meet the enormous challenges of our time.

Foreword

The more complex the world becomes, the more evident it is that understanding is no longer an individual act but a collective process. Systems do not react one after another, but simultaneously; developments do not unfold linearly, but in overlapping movements; and decisions do not emerge in isolation, but within the tension field of diverse perspectives and signals.

What does stability mean in a world that moves all at once? What does prevention mean in a system built on uncertainty? What does learning mean when signals are fragmented, perspectives differ, and time windows are critical? In a globally interwoven reality in which ecological, technical, social, and geopolitical dynamics intersect daily, risks rarely arise from a single event but from the simultaneity of many small and larger signals that reveal their meaning only in combination. This is precisely where the Global Learning Framework (GLF) begins: as a universal, cross-disciplinary meta-architecture that does not simplify complexity but makes it intelligible by translating six foundational concepts — visibility, structure, dynamics, interfaces, instability and capacity to act — into a circular, learning-oriented mode of working. This mode follows a clear epistemic rhythm that interweaves shared perception, systemic connection, collective interpretation, and coordinated action, and is stabilized through continuous learning; within this rhythm, the four pillars SEE, CONNECT, INTERPRET and ACT unfold their effect, not as technical steps but as recurring movements of shared thinking and shared action. The GLF does not replace existing methods or mandates; rather, it serves to create a shared language and a shared conceptual model that brings heterogeneous data, perspectives, and decision logics together within an integrated risk and learning architecture.

The GeoResilience Compass expands this structure with a spatial-dynamic dimension by making visible where instabilities may emerge, how they propagate, and which interventions are systemically effective; it links geophysical reality, ecological dynamics, technical signals, and institutional capacity to act into a resilience understanding that encompasses both local and global perspectives. Subsea cables serve in this context as exemplary lifelines of our interconnected world — an illustrative example of how technical precision, ecological sensitivity, maritime reality, and institutional responsibility converge within a single system, and how risks become visible only when their multitude of signals is considered together. The Subsea Edition demonstrates how the Global Learning Framework can be applied in a highly dynamic, ambiguous, and internationally shared environment without limiting the framework itself to this domain, for its logic is equally relevant to epidemiological systems, urban resilience, energy and supply systems, ecosystems, critical infrastructures, social dynamics and governance architectures. In a time in which global risks are increasingly intertwined and in which stability arises not from reaction but from anticipatory design, the Global Learning Framework offers a way to see complex realities together, to connect them together, to interpret them together, to act on them together, and to learn from them together — preventively, adaptively and with a view toward the future.

This book unfolds this architecture across multiple layers that complement one another and together enable a coherent understanding.

Accompany me for a moment on this path into an architecture that can enable shared understanding and shared action.

I therefore warmly invite you to read this book not selectively but as an interconnected system — because the true strength of this framework lies in its entirety, in the relationships between the concepts, and in the way they reinforce, extend and refine one another.

Perhaps this is where the true power of this work lies: in the possibility that a framework may become a standard, a standard may become a shared responsibility, and a compass may become a collective orientation system for a global world in constant and rapid transformation.

Birgit Bortoluzzi

Graduate Disaster Manager (WAW), Strategy Planner & Architect of Resilience Frameworks

Version 1.0 – First Edition

The Global Learning Framework – A Common language for risk, prevention and ecological stability with focus on submarine cable

Developed and authored by Birgit Bortoluzzi

Dresden (Germany), February 2026

© 2026 Birgit Bortoluzzi. All rights reserved. This publication — including its terminology, semantic architecture and governance logic — is the intellectual property of the author. Redistribution, adaptation or translation of any part of this work (textual, visual or structural) is permitted only with explicit attribution and in accordance with the ethical principles outlined herein. Collaborative use in humanitarian, academic or institutional contexts is welcomed under transparent governance agreements. Commercial use or modification requires prior written consent.

Note on Accessible Readability

To ensure readability for all audiences, including individuals with cognitive or visual impairments, I have chosen to omit gender-specific special characters throughout this book. All personal designations are inclusive and refer to all genders.

THE GLOBAL LEARNING FRAMEWORK

A common language for risk, prevention, and ecological stability

The Global Learning Framework (GLF) is intended to enable a universal, cross-disciplinary architecture designed to support shared understanding, coordinated interpretation, and collective action across ecological, epidemiological, social and governance systems.

It was developed by me as an integrative, meta-structural analysis and action model designed to bring together heterogeneous data, observations, and decision-making processes from ecological, epidemiological, social, and institutional systems within a shared risk architecture. Methodologically, the Global Learning Framework (GLF) is based on the assumption that global risks do not emerge from isolated variables, but from the interaction of structure, dynamics, and interfaces within complex adaptive systems.

To make these interactions visible and interpretable, the GLF operationalizes six fundamental concepts — visibility, structure, dynamics, interfaces, instability, and action capacity — as universal analytical categories that can be applied independently of discipline, data type, or institutional context.

Methodologically, the GLF functions as an adapter between existing scientific and institutional practices: it does not replace established models, institutions/organizations, or data systems, but aims to create a shared semantic and analytical layer on which different perspectives can be integrated.

This integration is achieved through four operational pillars: SEE (establishing shared visibility through common observation logics), CONNECT (systemic linkage of disciplinary silos), INTERPRET (development of shared risk indicators, thresholds, and interpretation rules) and ACT (derivation of coordinated action options and learning loops).

In scientific applications, the GLF is intended to enable the transformation of fragmented individual observations into systemic insights by explicitly modeling the relationships between ecological patterns, epidemiological processes, social dynamics, and governance structures. In doing so, it aims to support both retrospective analyses and prospective risk assessments, thereby providing a methodological foundation for anticipatory learning and preventive action in complex, globally interconnected risk landscapes.

Why the Global Learning Framework is necessary and which gap it is designed to close

1. Global risks are systemic – but our responses are not

Pandemics, AMR, climate dynamics, ecological instability, and social vulnerability emerge from interconnected systems, yet they continue to be addressed within disciplinary and institutional silos.

This fragmentation prevents the recognition of critical patterns that arise between systems.

Conclusion: We face systemic risks, but we lack an adequate systemic risk logic.

2. There is no shared vocabulary for risk and prevention

Science, policy, Earth observation, public health, and civil society use different terms, models and temporal rhythms.

This leads to:

misunderstandings

gaps in interpretation

disconnected data spaces

a lack of shared priorities

inconsistent risk definitions

, which make comparison and aggregation difficult

a lack of shared thresholds

, causing warnings to be issued too late, too early or in contradictory ways

asynchronous decision cycles

, which prevent coordinated action

fragmentation of responsibilities

, because each institution uses a different understanding of risk

loss of context

, as data are interpreted incorrectly without a shared semantics

a lack of interoperability between disciplines

, which blocks interdisciplinary collaboration

incompatible models and indicators

, which cannot be linked to one another

overload of decision-makers

, because they must interpret contradictory information

a lack of transparency for the public

, which undermines trust in early-warning systems

inefficient allocation of resources

, because priorities cannot be synchronized

delays in prevention

, because no shared framework for action exists

a lack of synchronization of temporal rhythms

, which prevents ecological, epidemiological, and social dynamics from being observed jointly

disruptions in institutional continuity

, because each organization uses its own risk cycles and terminology

limited scalability of solutions

, as models are not transferable or interoperable

misinterpretation of early indicators

, because warning signals are named and weighted differently across systems

loss of learning capacity

, as experiences cannot be transferred into a shared knowledge base

a lack of comparability between regions

, which complicates global risk assessments

amplification of social inequalities

, because different groups define and perceive risks differently

blockages in international cooperation

, as states and institutions do not share a common semantic foundation

misalignment in communication

, because warnings are not translated or understood in a target-group-appropriate way

reduction of complex risks to single indicators

, which renders systemic dynamics invisible

a lack of interoperability between early-warning systems

, because differing terminologies block technical interfaces

loss of time-critical responsiveness

, as information must first be translated and reinterpreted

miscalibration of models

, because input data do not match semantically

contradictions between local and global risk assessments

, as different logics of evaluation are used

escalation of institutional conflicts

, because actors define risks differently and thus work against rather than with one another

a lack of traceability of decisions

, because justifications are based on incompatible terms

decoupling of community knowledge

, because local risk concepts are not integrated into formal systems

loss of early-warning sensitivity

, because signals from other systems are not recognized or are wrongly prioritized

increased path dependency

, as institutions cling to their own term systems and thereby block innovation

weakening of global governance mechanisms

, because multilateral negotiations are ineffective without a shared language

blurred lines of accountability

, because it remains unclear who is responsible for which risk under which definition

more difficult liability and regulatory frameworks

, as legal and technical risk concepts do not align

a lack of standardization of risk indicators

, which hinders international comparability and the development of norms

inconsistent evaluation and monitoring processes

, because success and failure of prevention measures are defined differently

distortion through media translation

, as media create their own risk narratives that are not compatible with technical terminology

misallocation of funding

, because funding logics follow different risk concepts than operational systems

obstacles to education and training

, as professionals are trained in different conceptual worlds and do not learn a shared risk concept

amplification of disinformation and mistrust

, because contradictory terms and messages are perceived as signs of incompetence or manipulation

blockage of public-private partnerships

, because companies, authorities, and science lack a shared understanding of risk as a basis for cooperation

weakening of feedback and learning loops

, because experiences from crises cannot be fed back into a consistent, jointly understood vocabulary

Source: AI-generated image (2026)

360-Degree Matrix: Lack of a Shared Vocabulary for Risk & Prevention

Dimension

Systemic Consequences

Governance

• Fragmentation of responsibilities

• lack of shared priorities

• blockages in international cooperation

• blurred lines of accountability

• more difficult liability and regulatory frameworks

• weakening of global governance mechanisms

• misallocation of funding

• disruptions in institutional continuity

• delays in prevention

• inefficient allocation of resources

Technology

• disconnected data spaces

• incompatible models and indicators

• lack of interoperability between early-warning systems

• miscalibration of models

• loss of early-warning sensitivity

• limited scalability of solutions

• lack of synchronization of temporal rhythms

• reduction of complex risks to single indicators

• loss of time-critical responsiveness

• misinterpretation of early indicators

Society

• misunderstandings

• misalignment in communication

• amplification of social inequalities

• lack of transparency for the public

• amplification of disinformation and mistrus

• decoupling of community knowledge

• blockage of public-private partnerships

• overload of decision-makers

• distortion through media translation

• obstacles to education and training

Epistemics

• gaps in interpretation

• inconsistent risk definitions

• lack of shared thresholds

• loss of context

• lack of interoperability between disciplines

• contradictions between local and global risk assessments• loss of learning capacity

• lack of comparability between regions

• inconsistent evaluation and monitoring processes

• increased path dependency

Source: Own elaboration “360-Degree Matrix: Systemic Consequences of Lacking a Shared Vocabulary for Risk and Prevention”

Without a common language, there can be no common learning.

3. Existing networks are highly valuable – yet not designed for GLF-type learning

International networks such as e.g. GOARN, GEO, One Health, IPBES and WHO programmes have for many years made a central and very valuable contribution to global health security, environmental monitoring and risk governance. Their mandates, working methods and institutional structures are designed to ensure high technical quality, reliability and effectiveness within their respective areas of responsibility. These networks operate with a thematic focus, are anchored in clearly defined governance frameworks, possess specialised expertise and often carry out their functions within mandated or project-based structures. It is precisely this specialisation that enables their strength within their respective domains.

At the same time, this may also mean that their processes, data flows and decision-making logics are primarily aligned with the requirements of their specific mandates.

At this point we should ask ourselves, are continuous mechanisms that systematically integrate observations, models and learning processes across ecological, epidemiological, social and governance systems already sufficiently in place?

It will be essential to establish a permanent, transdisciplinary learning ecosystem in order to meet the enormous and complex challenges of our time within an integrated risk architecture.

Existing international cooperation is strong and absolutely indispensable. In addition, there is still a need for a shared, transdisciplinary Community of Practice that continuously enables systemic learning across system boundaries on an ongoing basis.

4. Data are plentiful – but is there also a shared interpretation?

The clusters presented here represent only a small selection of the data domains available today. They are intended to illustrate, in an exemplary way, how diverse, heterogeneous and dynamic the knowledge landscapes are that interact within global risk architectures. To understand complex developments systemically, these different forms of evidence, models and observations would need to be translated into a shared epistemic “language” that enables cross-sectoral interpretation and collective learning.

International actors today have access to a wide range of high-quality data streams:

Environmental and Ecological Systems

► Earth observation makes landscape dynamics visible.

► Ecology shows stress on habitats.

► Climate monitoring captures atmospheric and hydrological variability.

► Agricultural data documents crop conditions and food system stability.

► Biodiversity assessments track species distribution and ecosystem change.

► Water system data captures groundwater levels, river discharge and hydrological stress.

► Waste and pollution monitoring tracks chemical, plastic and hazardous emissions.

► Land-use and urban development data documents expansion, zoning and settlement dynamics.

► Indigenous and local knowledge documentation reflects place-based observations and long-term environmental experience.

Health, Epidemiology and Social Well-Being

► Epidemiology describes transmission patterns.

► Health system data shows service capacity and access to care.

► Civil society perspectives highlight social vulnerabilities.

► Behavioral and communication data provides insights into public response patterns.

► Mental health and psychosocial data highlights stress, coping capacity and social cohesion.

► Migration and displacement data tracks forced movement and protracted displacement situations.

Governance, Institutions and Legal-Regulatory Systems

► Governance analyses illuminate institutional structures.

► Legal and regulatory data captures laws, standards and compliance dynamics.

► Land tenure and property rights data shows ownership patterns and access to resources.

► Early warning and alert system data records triggers, thresholds and response timelines.

Economic, Financial and Market Systems

► Economic indicators reveal market dynamics and resource pressures.

► Supply chain and trade data highlights dependencies and disruptions across sectors.

► Financial system data reflects credit flows, insurance exposure and systemic risk signals.

► Insurance and loss data documents damages, claims and protection gaps.

Infrastructure, Technology and Cyber Systems

► Infrastructure data identifies critical system dependencies.

► Energy system data shows production, consumption and grid stability.

► Mobility and transportation data reveals movement patterns and logistical bottlenecks.

► Digital infrastructure and cyber-risk data identifies vulnerabilities in information systems.

Security, Stability and Conflict Dynamics

► Conflict and security analyses highlight instability and fragility.

Knowledge, Innovation and Societal Capacity

► Demographic data reflects population structures and mobility patterns.

► Cultural and linguistic data provides insights into communication pathways and trust dynamics.

► Education and workforce data shows skill distributions and capacity for adaptive learning.

► Innovation and research data maps scientific output, patents and emerging technologies.

► Innovation ecosystem and entrepreneurship data shows the capacity to develop and scale solutions.

► Media and information ecosystem data reveals narratives, misinformation patterns and information access.

Health, epidemiology and social well-being

► Epidemiology describes transmission patterns.

► Health system data shows service capacity and access to care.

► Civil society perspectives highlight social vulnerabilities.

► Behavioral and communication data provides insights into public response patterns.

► Mental health and psychosocial data highlights stress, coping capacity and social cohesion.

► Migration and displacement data tracks forced movement and protracted displacement situations.

► Pharmacokinetic, pharmacogenetic and PGX data reveal individual metabolic profiles, drug response variability and treatment-relevant genetic markers.

Safety, stability and hazardous situations

► Conflict and security analyses highlight instability and fragility.

► CBRN monitoring and hazard data detect chemical, biological, radiological and nuclear threats.

Each of these data domains provides valuable insights within its respective mandate.

At the same time, the question arises as to whether sufficient mechanisms already exist that systematically relate these different observations, models and evidence to one another – in such a way that a shared understanding of complex risk developments can emerge.

Without such a shared basis for interpretation, the overall dynamics of a system remain difficult to discern.

A large amount of data alone does not yet create prevention – only their shared interpretation enables anticipatory action.

5. Early warning systems exist – but they are often sectoral and may still be too reactive

Let us now examine this long list together with great care and let us not lose sight of any single point.

Global early warning systems perform important functions today, yet many of them remain oriented toward clearly defined events, sector-specific mandates, and retrospective data. The following extensive analysis of existing characteristics shows that numerous relevant risk dynamics – particularly those that emerge slowly, unfold between systems, or involve societal, cognitive and ecological changes – are still captured only insufficiently.

The points listed illustrate that current warning logics are often not prepared to systematically observe cumulative stressors, declining resilience, societal tensions, trust dynamics, emotional exhaustion, inequalities, knowledge fragmentation, technological dependencies, or planetary changes. Likewise, multisystem health trajectories, functional impairments, cognitive changes, impacts on children and young people, and the long-term consequences of repeated crises remain largely invisible.

This creates a structural pattern: early warning systems primarily detect events, while many of the conditions that enable or amplify such events lie outside their field of view.

However, the risks of the 21st century increasingly emerge in the spaces between systems – between sectors, between institutions, between social groups, between data domains, and between planetary and societal processes.

The analysis suggests that future early warning systems could and must — be more strongly oriented toward instabilities, interfaces, dynamics, and capacities for action. Such an approach would make it possible to detect changes earlier, reveal cross-sectoral patterns, and create the foundations for shared learning and anticipatory decision-making.

Taken together, these points show that advancing global early warning logics is not only a technical task, but above all an epistemic one: it is about translating diverse forms of evidence, models, and observations into a shared language that renders complex developments understandable and enables cross-sectoral collaboration.

Today we warn about events – in the future, we could learn together to warn about the conditions that make them possible.

Many warning systems today are still too much:

► • disease-specific

► • sectoral

► • retrospective

► • not dynamically coupled

► • not oriented toward diverse interfaces

► • focused on single indicators

► • primarily aligned with historical thresholds

► • not designed for multisector data integration

► • technologically heterogeneous and weakly interoperable

► • not prepared for adaptive modelling

► • highly dependent on manual validation

► • not continuously updated

► • based on linear risk assumptions

► • not designed for complex cascading effects

► • barely aligned with local contextual variability

► • not oriented toward uncertainties and probabilities

► • not sufficiently available in real time or near real time

► • not aligned with continuous learning processes

► • dependent on fixed reporting chains

► • not sufficiently prepared for machine learning or predictive methods

► • still too weakly aligned with cross-sector governance processes

► • not adequately designed for the integration of qualitative evidence

► • still insufficiently sensitive to slow-onset changes and gradual trends

► • not sufficiently oriented toward the necessary detection of system instabilities

► • strongly dependent on institutional mandate boundaries

► • not sufficiently designed for spatial multiscalarity (local–regional–global)

► • not sufficiently prepared for the integration of social dynamics and behavioural patterns

► • not adequately aligned with the observation of interactions between natural and technical systems

► • not sufficiently oriented toward monitoring system recovery and resilience processes

► • still too weakly prepared for the detection of emerging patterns or anomalies

► • not adequately designed for combining structured and unstructured data

► • still too weakly aligned with the use of crowd-sourcing or community-based observations

► • not or not sufficiently, oriented toward the analysis of uncertainty spaces and alternative future pathways

► • still too weakly prepared for the integration of CBRN-specific indicators

► • not sufficiently designed for incorporating pharmacokinetic and pharmacogenetic signals

► • not sufficiently oriented toward long-term, post-acute, or chronic trajectories

► • insufficiently prepared for multisystem health changes that develop over extended periods

► • insufficiently able to capture functional limitations or exertional intolerance

► • insufficiently, or not at all, designed to capture cognitive changes or speech- and communication-related impairments

► • not sufficiently aligned with subclinical or hard-to-measure early indicators

► • still too weakly prepared for integrating patient-reported outcomes and everyday signals

► • not or insufficiently, oriented toward monitoring long-term consequences after infections or exposures

► • barely prepared to detect patterns that only become visible at the population level (population-level emergence)

► • not sufficiently designed for combining biological, social, and functional data

► • still too weakly aligned with capturing impacts on work ability, education, and societal participation

► • not designed to make cumulative burdens across multiple crises visible

► • not or insufficiently, aligned with the gradual decline of individual and collective resilience

► • barely able to capture exhaustion, overload, or capacity losses in populations or institutions

► • not prepared to capture cognitive or functional impairments following repeated stressors

► • not designed to account for impacts on language, communication, or decision-making capacity

► • not or insufficiently, oriented toward observing long-term psychosocial consequences

► • barely able to capture the interaction between crisis intensity and societal recovery capacity

► • not prepared to detect declining capacities for action within systems at an early stage

► • not, or insufficiently, aligned with capturing exhaustion dynamics in critical professional groups

► • not, or insufficiently, designed to make the declining ability for collective problem-solving visible

► • not designed to make societal polarization or social fragmentation visible

► • barely able to capture losses of trust between social groups or toward institutions

► • not sufficiently prepared to observe tensions, conflict narratives, or social disintegration

► • not, or insufficiently, designed to account for the impacts of crime or organized crime on societal stability

► • not, or insufficiently, aligned with capturing changes in public safety or everyday risks

► • barely able to capture the interaction between social insecurity and health, economic, or political risks

► • not prepared to incorporate the effects of information distortion, polarization, or erosion of trust into warning logics

► • not oriented toward observing patterns of violence or threat in the context of multiple crises

► • barely able to make the burden on communities from repeated social tensions visible

► • not designed to account for the impacts of societal division on collective resilience and capacity for action

► • not designed to make the loss of collective attention visible as a systemic change

► • not, or barely, able to capture information overload as a risk factor

► • not sufficiently prepared to account for a declining ability for complex thinking

► • not sufficiently aligned with incorporating the erosion of shared realities into early warning logics

► • barely, or not at all, able to systematically capture loss of language, communication breakdowns, or shifts in meaning

► • not sufficiently designed to recognize chronic overload in critical professional groups as an early indicator

► • not sufficiently aligned with long-term impacts on children and young people

► • not sufficiently prepared to capture learning losses or developmental delays as societal risk dynamics

► • not sufficiently able to make changes in the future viability of societies visible

► • not sufficiently designed to systematically observe changes in trust in institutions

► • not sufficiently aligned with capturing trust dynamics between social groups

► • not sufficiently prepared to incorporate changes in trust in science, media, or governance

► • barely able to recognize erosion of social cohesion as a risk signal

► • not sufficiently designed to account for dominant societal narratives and their influence on risk perception

► • not sufficiently aligned with observing fear narratives or polarization narratives

► • not sufficiently prepared to systematically incorporate cultural patterns of risk perception

► • not sufficiently, or barely, able to capture collective identity shifts as early indicators

► • barely designed to make collective fear visible as a dynamic risk factor

► • not sufficiently aligned with accounting for collective grief or prolonged states of strain

► • not sufficiently prepared to incorporate collective anger or societal tension states into warning logics

► • barely able to recognize collective hopelessness as a systemic change

► • not sufficiently designed to capture emotional exhaustion in populations or professional groups

► • not sufficiently aligned with accounting for structural exclusion as a risk amplifier

► • not sufficiently prepared to incorporate digital inequality as an early indicator of societal vulnerability

► • barely, or insufficiently, able to make health inequality visible as a dynamic risk factor

► • not sufficiently aligned with geographic inequality and regional vulnerabilities

► • not sufficiently designed to account for unequal recovery capacities across population groups

► • not sufficiently prepared to capture knowledge fragmentation as a systemic challenge

► • not sufficiently designed to make the absence of a shared epistemic language visible

► • not sufficiently able to integrate unconnected models or separated knowledge systems

► • not sufficiently aligned with relating incompatible data spaces to one another

► • not sufficiently prepared to account for missing translation mechanisms between disciplines or sectors

► • not sufficiently designed to capture dependencies on AI systems as a risk dimension

► • not sufficiently aligned with incorporating dependencies on digital platforms into early warning logics

► • not sufficiently able to recognize dependencies on proprietary data as structural vulnerabilities

► • not sufficiently prepared to account for dependencies on global supply chains as a risk factor

► • not sufficiently designed to capture biodiversity loss as a long-term systemic change

► • not sufficiently aligned with incorporating planetary tipping points into early warning logics

► • not sufficiently able to make water stress visible as a multidimensional risk driver

► • not sufficiently prepared to account for soil degradation as a long-term systemic change

► • not sufficiently designed to integrate planetary health indicators into cross-sector early warning logics

While many existing early warning systems remain event-oriented and sector-bound, the Global Learning Framework (GLF) seeks to adopt a fundamentally different perspective.

It aims to focus on the underlying conditions, dynamics, and interfaces that determine how risks emerge, evolve, and cascade.

•

 Instability:

The early detection of emerging tensions, fragilities, and shifts across systems – long before they manifest as crises.

•

 Interfaces:

The spaces where sectors, disciplines, data streams, institutions, communities, and ecosystems intersect and where most modern risks actually arise.

•

 Dynamics:

Patterns of change, acceleration, accumulation, erosion, and transformation that cannot be captured by static indicators or retrospective thresholds.

•

 Capacity for Action:

The ability of societies, institutions, and communities to respond, adapt, recover, and learn – including the moments when these capacities begin to decline.

At this point, we should broaden our perspective to warn not only about events, but also about the conditions that make them possible in the first place.

Source: AI-generated image (2026)

Epistemic Dimensions

The selection of these twelve languages forms a deliberately designed global cross-section that reflects the Epistemic Dimensions in their full breadth. Each language represents its own cognitive, cultural and systemic way of approaching the world: English, French, and Spanish represent the international working and mediation languages that shape operational communication in global institutions. Chinese, Arabic, and Hindi open perspectives on major demographic and geopolitical regions in which epistemic categories such as structure, dynamics or agency are historically anchored in different ways. Danish, Russian, and Japanese contribute three highly developed knowledge cultures known for precision, systems thinking, and long-term process logic. With Swahili, Portuguese, and Polish, three additional languages are included that cover key bridging regions: Africa, Latin America and Eastern Europe. Together, these twelve languages do not constitute a decorative mix but a strategic epistemic cross-section that demonstrates how universal — and at the same time culturally diverse — the six dimensions of Visibility, Structure, Dynamics, Interfaces, Instability and Agency can be understood and operationalized.

English

Français

Español

VISIBILITY

Visibilité

Visibilidad

What do we see and what remains invisible?

Que voyons-nous – et que reste-t-il invisible ?

¿Qué vemos y qué permanece invisible?

STRUCTURE

Structure

Estructura

How is the system built?

Comment le système est-il construit ?

¿Cómo está construido el sistema?

DYNAMICS

Dynamiques

Dinámica

How does the system change over time?

Comment le système évolue-t-il dans le temps ?

¿Cómo cambia el sistema con el tiempo?

INTERFACES

Interfaces

Interfaz

Where do systems intersect?

Où les systèmes se croisent-ils ?

¿Dónde se cruzan los sistemas?

INSTABILITY

Instabilité

Inestabilidad

How vulnerable is the system to disruption?

Quelle est la vulnérabilité du système aux perturbations ?

¿Qué tan vulnerable es el sistema a las interrupciones?

AGENCY

Capacité d’action

Capacidad de acción

What can be done and who can do it?

Que peut-on faire – et par qui ?

¿Qué se puede hacer y quién puede hacerlo?

中文 (Chinese)

العربية (Arabic)

हिन्दी (Hindi)

可见性

الرؤية

दृश्यता

我们看到了什么，什么是不可见的？

ماذا نرى وما الذي يبقى غير مرئي؟

हमक्यादेखतेहैंऔरक्याअदृश्यरहताहै

结构

البنية

संरचना

系统是如何构建的？

كيف تم بناء النظام؟

प्रणालीकैसेबनाईगईहै

动态

الديناميكيات

गतिशीलता

系统如何随时间变化？

كيف يتغير النظام مع مرور الوقت

समयकेसाथप्रणालीकैसेबदलतीहै

接口

التقاطعات

इंटरफेस

系统在哪里交汇？

أين تتقاطع الأنظمة؟

प्रणालियाँकहाँमिलतीहैं

不稳定性

عدم الاستقرار

अस्थिरता

系统对干扰有多脆弱？

ما مدى ضعف النظام أمام الاضطرابات؟

व्यवधानोंकेप्रतिप्रणालीकितनीसंवेदनशीलहै

能动性

القدرة على التصرف

एजेंसी / कार्यक्षमता

可以做什么，谁来做？

ما الذي يمكن فعله ومن يمكنه فعله؟

क्याकियाजासकताहैऔरकौनकरसकताहै

Dansk

Русский

日本語

Synlighed

Видимость

可視性（かしせい）

Hvad ser vi – og hvad forbliver usynligt

Что мы видим и что остаётся невидимым

何が見えて、何が見えないのか

Struktur

Структура

構造（こうぞう）

Hvordan er systemet opbygget

Как устроена система

システムはどのように構成されているか

Dynamik

Динамика

動態（どうたい）

Hvordan ændrer systemet sig over tid

Как система меняется со временем

システムは時間とともにどう変化するか

Grænseflader

Интерфейсы

インターフェース

Hvor mødes systemer

Где пересекаются системы

システム同士はどこで交差するか

Ustabilitet

Нестабильность

不安定性（ふあんていせい）

Hvor sårbart er systemet over for forstyrrelser

Насколько система уязвима к сбоям

システムはどれほど脆弱か

Handlekraft

Дееспособность

エージェンシー / 行為能力

Hvad kan gøres – og af hvem

Что можно сделать и кто может это сделать

何ができ、誰がそれを行えるのか

Kiswahili

Português

Polski

Uonekano

Visibilidade

Widoczność

Tunaona nini – na nini hakionekani

O que vemos – e o que permanece invisível

Co widzimy – a co pozostaje niewidoczne

Muundo

Estrutura

Struktura

Mfumo umejengwa vipi

Como o sistema é construído

Jak zbudowany jest system

Mienendo

Dinâmica

Dynamika

Mfumo hubadilika vipi kwa muda

Como o sistema muda ao longo do tempo

Jak system zmienia się w czasie

Mawasiliano / Miingiliano

Interfaces

Interfejsy

Mifumo inakutana wapi

Onde os sistemas se cruzam

Gdzie systemy się przecinają

Kutokuwa thabiti

Instabilidade

Niestabilność

Mfumo ni dhaifu kiasi gani kwa usumbufu

Quão vulnerável é o sistema a interrupções

Jak podatny jest system na zakłócenia

Uwezo wa kuchukua hatua

Agência

Sprawczość

Nini kinaweza kufanywa – na nani anaweza kufanya

O que pode ser feito – e por quem

Co można zrobić – i kto może to zrobić

Source: Own elaboration “six dimensions of Visibility, Structure, Dynamics, Interfaces, Instability and Agency”

6. The GLF as an epistemic “operating system”

The Global Learning Framework (GLF) understands itself as a kind of epistemic operating system:a foundational orientation framework that seeks to make different forms of knowledge, data spaces, models, and institutional logics compatible with one another, without replacing or overriding them.

It aims to create a possible and conceivable foundation that enables different actors, sectors, and forms of knowledge to become capable of acting together.

How could this become possible?

► • a shared language (6 core concepts)

► A clear, universally accessible conceptual framework that makes complex developments understandable and facilitates cross-sector collaboration.

► • a shared structure (4 pillars)

► An orientation framework that makes it possible to embed diverse data, models, and observations into a coherent overall logic.

The GLF does not see itself as a new system, but as a kind of epistemic operating system that aims to make existing structures compatible with one another. It seeks to create connections in areas where fragmentation prevails today, it seeks to provide orientation in an increasingly complex landscape, and it seeks to support collective capacity for action where it is frequently under pressure.

7. The GLF Community of Practice as a potential operational driver

The GLF Community of Practice could play a central role in strengthening shared learning and cross-sector collaboration. It could create spaces in which different perspectives are brought together, experiences are exchanged, and diverse forms of evidence are interpreted collectively. In this way, it could contribute to enabling actors across sectors, institutions and regions to connect more effectively and learn from one another.

It could enable:

• continuous learning

• cross

-

sector interpretation

• shared indicators

• shared priorities

• shared prevention

Shared language → shared learning → shared prevention.

8. The desired outcome: A possible new form of global capacity for action

Through the GLF, a form of global capacity for action could emerge that is even more oriented toward foresight, shared interpretation, and collective responsibility. It could help systems, institutions, and actors to recognize developments earlier, understand interconnections more clearly, and act in an even more coordinated manner.

• Early warning could meaningfully complement retrospective approaches

• Prevention could support reactive measures

• A broader systems understanding could reduce fragmentation

• Responsibility could be strengthened as a shared capability

• Visibility could contribute even further to stability

The GLF could support the transition from knowledge that is still very fragmented in some areas to collective intelligence.

To make a new form of global capacity for action conceivable at all, a language is needed that can be understood by all actors – regardless of discipline, sector or institutional mandate. The Global Learning Framework could open a shared conceptual space in which different forms of knowledge can be brought into relation with one another. A language that makes complex developments understandable without oversimplifying them; that remains scientifically robust while being universally accessible. The six core concepts of the GLF could form exactly this bridge: simple enough to be shared globally, yet precise enough to jointly interpret systemic risks, dynamics and opportunities for prevention.

Source: AI-generated image (2026)

Visibility

What do we see and what remains invisible?

Visibility is the central concept. It connects different domains of knowledge by revealing what is captured and what remains hidden.

Example excerpts

► • Earth Observation (EO): What do the sensors show and which changes remain buried in data noise? (e.g., small-scale land-use changes, concealed deforestation, unreported fires)

► • Epidemiology: What is not reported and which outbreaks remain below reporting thresholds? (e.g., asymptomatic cases, under-captured regions, lack of diagnostics)

► • Governance: Which risks are politically invisible and which information does not reach decision-making levels? (e.g., local tensions, administrative gaps, unreported incidents)

► • Society: Which vulnerabilities are not perceived and which groups remain structurally invisible? (e.g., informal settlements, marginalized communities, silent burdens)

► • Climate Science: Which slow changes remain below the threshold of perception and how do they accumulate over time? (e.g., soil moisture loss, acidification, glacier retreat, gradual degradation)

► • Economy: Which systemic risks in markets or supply chains remain invisible and where do hidden dependencies emerge? (e.g., single-supplier risks, hidden costs, fragile nodes)

► • Humanitarian Systems: Which needs remain unrecorded or underrepresented in crises and who falls through the cracks? (e.g., unregistered households, concealed damages, silent emergencies)

► • Security Sector: Which simmering tensions or conflict dynamics remain publicly invisible – and where do early escalation patterns emerge? (e.g., local armed actors, covert mobilization)

► • Infrastructure & Energy: Which critical weaknesses remain invisible during normal operations and where do latent risks develop? (e.g., material fatigue, overload, missing redundancies, hidden disruptions)

► • Communication & Media: Which issues, risks, or groups do not appear in public discourse – and how does invisibility shape perception? (e.g., silent crises, structural inequalities, underrepresented regions)

► • Law & Justice: Which inequalities or legal violations remain invisible and where are data or procedures missing? (e.g., unreported violence, lack of legal pathways, informal dispute resolution)

► • Financial Systems: Which risks, debts, or shadow structures remain outside formal reporting? (e.g., opaque financial flows, hidden debt)

► • Migration & Mobility: Which movements remain invisible and which groups do not appear in official statistics? (e.g., irregular migration, internal migration, seasonal mobility)

► • Agriculture & Food Systems: Which production risks or supply bottlenecks are not captured? (e.g., soil depletion, silent crop losses, informal markets)

► • Water & Resource Systems: Which shortages, losses or pollution events remain below measurability? (e.g., groundwater decline, leakages, unreported contamination)

► • Technology & Cybersecurity: Which digital attacks, vulnerabilities, or data losses remain invisible? (e.g., zero-day exploits, silent data exfiltration, covert manipulation)

► • Labour Markets & Social Economy: Which forms of precarious work or exploitation remain invisible? (e.g., informal employment, care work, unreported risks)

► • Biodiversity & Ecosystems: Which losses go unnoticed and which species disappear before they are recorded? (e.g., insect decline, silent habitat loss, undocumented species)

► • Education Systems: Which learning gaps or inequalities remain invisible? (e.g., digital divide, silent dropouts, unmeasured competencies)

► • Psychosocial Health: Which burdens remain invisible and which groups do not appear in statistics? (e.g., silent trauma, unreported distress, hidden stress factors)

Visibility is the shared currency of all actors.

Structure

How is the system built?

Structure describes the fundamental architecture of a system – those stable patterns that can shape risks long before they become visible. It determines how spaces, institutions, resources, and relationships are arranged, and which possibilities or constraints emerge from this arrangement. Those who keep structure in view through a 360-degree lens can recognize the quiet preconditions under which dynamics arise, decisions take effect and prevention becomes possible in the first place.

► • Landscape structure: Which spatial patterns shape the system and how do they influence risk and exposure? (e.g., topography, settlement distribution, land-use zones)

► • Contact structures:How do encounters between people, animals, goods or systems occur – and where do stable contact patterns lie? (e.g., mobility routes, trade pathways, human–animal contact zones)

► • Institutional structures: How are institutions organized and how do their mandates and responsibilities shape the system? (e.g., administrative architectures, chains of responsibility, decision-making logics)

► • Social structures: Which social patterns shape communities and how do they distribute opportunities, risks, and resources? (e.g., networks, social hierarchies, community organization)

► • Governance structures (e.g., mandates, responsibilities, decision-making pathways)

► • Legal and regulatory structures (e.g., legal frameworks, compliance requirements, enforcement mechanisms)

► • Economic and market structures (e.g., monopolies, dependencies, supply-chain architectures)

► • Health-system structures (e.g., levels of care, reporting chains, capacity distribution)

► • Infrastructure structures (e.g., transport networks, energy architectures, communication networks)

► • Ecological structures (e.g., habitat patterns, biodiversity distribution, ecosystem boundaries)

► • Demographic structures (e.g., age distribution, urbanization, household composition)

► • Risk-distribution structures (e.g., who is exposed in which way, who carries which burdens)

► • Knowledge and data structures (e.g., data formats, interoperability, access rights)

► • Financial and budgetary structures (e.g., resource allocation, dependencies, stability buffers)

► • Labour and employment structures (e.g., formal vs. informal work, sectoral concentrations)

► • Technological structures (e.g., platforms, standards, dependencies on core systems)

► • Security and defence structures (e.g., command architectures, regional security orders)

► • Education structures (e.g., access, quality, institutional distribution)

► • Communication structures (e.g., media landscape, information flows, digital reach)

► • Cultural structures (e.g., norms, values, social patterns, collective expectations)

► • Information and knowledge-distribution structures: How is knowledge distributed within the system and which groups have structurally less access? (e.g., knowledge hierarchies, expert dominance, asymmetric information flows)

► • Trust structures: Which stable patterns of trust or mistrust shape the system and how do they influence cooperation? (e.g., institutional trust, trust gaps, historical experiences)

► • Cooperation and network structures: How are actors connected and where do stable or missing cooperation patterns exist? (e.g., multilateral networks, regional alliances, sectoral clusters)

► • Resource and distribution structures: How are material and immaterial resources distributed and where do structural imbalances arise? (e.g., water distribution, energy access, land rights)

► • Narrative and meaning structures: Which stable narratives shape the understanding of risk, responsibility, and the future? (e.g., cultural narratives, political guiding ideas, societal interpretation patterns)

► • Historical structures: Which historical patterns persist and how do they shape current risks and relationships? (e.g., colonial borders, historical conflicts, institutional path dependencies)

► • Ethnic and identity structures: How are identities anchored in the system and where do structural tensions or affiliations emerge? (e.g., minority structures, identity lines, patterns of belonging)

► • Religious and spiritual structures: Which religious orders shape behaviour, norms, and institutional patterns? (e.g., religious authorities, faith communities, normative orders)

► • Ownership and property structures: How is ownership organized and how does this influence risk, power, and capacity to act? (e.g., land rights, ownership concentration, informal property systems)

► • Production and supply structures: How are production processes organized and where do structural dependencies lie? (e.g., central production nodes, supply chains, regional clusters)

► • Transport and mobility structures: How do people and goods move and which stable patterns shape mobility? (e.g., transport corridors, routes, bottlenecks)

► • Financial power structures: Which stable patterns of financial influence shape the system? (e.g., capital centres, institutional investors, geopolitical financial flows)

► • Digital structures: How is the digital space organized and where do structural dependencies or exclusions exist? (e.g., platform dominance, digital infrastructure, access disparities)

► • Household and family structures: How are households organized and how does this influence risk, resilience, and social dynamics? (e.g., multigenerational households, single-parent households, household size)

► • Organizational structures: How are organizations internally arranged and how does this shape decision-making and responsiveness? (e.g., hierarchies, departmental logics, internal silos)

► • Value and norm structures: Which stable value patterns shape behaviour and decisions? (e.g., solidarity, individualism, risk cultures)

Dynamics

How does the system change over time?

Dynamics describe temporal patterns, movements, and transformations – the processes that amplify, attenuate, or generate risks. They illuminate how systems respond to external stimuli, how trajectories evolve, and how conditions shift over time. A 360-degree understanding of dynamics enables the identification not only of observable changes, but also of the underlying drivers, feedback mechanisms, and systemic forces that shape these changes. Dynamics reveal how risks may escalate, migrate or dissipate, thereby providing the analytical foundation for anticipatory governance, strategic foresight and effective prevention.

► • Seasonal dynamics: How do patterns change throughout the year and which recurring cycles shape risk and behaviour? (e.g., rainy and dry seasons, seasonal disease waves, agricultural cycles)

► • Spread dynamics: How do phenomena spread over time and which factors accelerate or slow these processes? (e.g., infection spread, invasive species, information diffusion)

► • Migration dynamics: How do movements of people, animals, or goods change over time and which patterns emerge from them? (e.g., seasonal migration, internal migration, animal migrations)

► • Political dynamics: How do political frameworks, priorities or power relations change over time? (e.g., government changes, policy shifts, geopolitical shifts)

► • Demographic dynamics: (e.g., population growth, ageing, urbanisation surges)

► • Ecological dynamics: (e.g., forest loss, desertification, regeneration cycles)

► • Economic dynamics: (e.g., business cycles, price volatility, market shifts)

► • Technological dynamics: (e.g., innovation, automation, digital transformation)

► • Health dynamics: (e.g., infection waves, immunity changes, supply fluctuations)

► • Climatic dynamics: (e.g., increasing extreme weather, temperature trends, precipitation patterns)

► • Social dynamics: (e.g., mobilisation, polarisation, changes in trust)

► • Conflict and security dynamics: (e.g., escalation, de-escalation, shifts in power)

► • Resource dynamics: (e.g., water availability, energy flows, raw-material fluctuations)

► • Mobility and traffic flows: (e.g., commuter flows, trade routes, seasonal movement patterns)

► • Information and communication dynamics: (e.g., viral spread, narrative shifts, media cycles)

► • Governance dynamics: (e.g., reform processes, institutional changes, mandate shifts)

► • Price and market dynamics: (e.g., inflation, speculation waves, supply-demand fluctuations)

► • Biodiversity dynamics: (e.g., species increase or loss, population fluctuations)

► • Infrastructure dynamics: (e.g., ageing, maintenance cycles, capacity changes)

► • Risk-perception dynamics: (e.g., attention fluctuations, societal sensitisation, fatigue effects)

► • Knowledge and evidence dynamics: How does knowledge change over time and how quickly do new insights emerge or existing ones become outdated? (e.g., new evidence, paradigm shifts, learning curves)

► • Behavioural dynamics: How do individual or collective behaviour patterns change over time? (e.g., adaptation, fatigue, risk behaviour)

► • Regulatory and norm dynamics: How do rules, standards, and norms evolve over time? (e.g., new standards, deregulation, international harmonisation)

► • Financial and capital-flow dynamics: How do capital flows move over time and how do they influence stability? (e.g., capital withdrawal, investment waves, volatility)

► • Trust dynamics: How does trust in institutions, science or actors change over time? (e.g., trust building, trust loss, trust shocks)

► • Narrative and meaning dynamics: How do societal narratives and meaning frameworks change? (e.g., new narratives, discourse shifts, reframing)

► • Legal and justice dynamics: How do legal frameworks and their enforcement change over time? (e.g., new jurisprudence, international agreements, reforms)

► • Labour and employment dynamics: How do labour markets and forms of employment change over time? (e.g., automation surges, new professions, sectoral shifts)

► • Energy and emissions dynamics: How do energy consumption, energy sources and emissions change over time? (e.g., transition to renewables, consumption peaks, emission increases)

► • Price- and cost-structure dynamics: How do cost structures change over time – independent of market prices? (e.g., production costs, transport costs, external-effect costs)

► • Institutional capacity dynamics: How do institutions’ capabilities, resources and response speed change? (e.g., capacity building, capacity loss, reorganisation)

► • Digitalisation dynamics: How quickly do digital systems, platforms and dependencies change? (e.g., new platform dominance, technological leaps, cyber risks)

► • Attention and perception dynamics: How does societal attention shift over time? (e.g., issue-attention cycles, fatigue, sudden focus)

► • Supply-chain and production dynamics: How do production and logistics processes change over time? (e.g., bottlenecks, re-shoring, new dependencies)

► • Cultural dynamics: How do values, norms and cultural patterns change over time? (e.g., generational shifts, value changes, new social practices)

► • Institutional compatibility dynamics: How do relationships between institutions change over time? (e.g., new cooperation, fragmentation, harmonisation)

Interfaces - Where do systems intersect?

Interfaces are transitional spaces between systems — zones in which different logics, speeds, resources, vulnerabilities and responsibilities meet. At these points of transition, risks often emerge first, because neither the stability of structures nor the predictability of dynamics fully applies. Interfaces mark those contact surfaces where systems interact, collide, cooperate, or mutually influence one another. A 360-degree perspective on interfaces reveals not only the spatial, social, or institutional points of contact, but also the forces, tensions, and flows that can arise between them. Interfaces thus form a crucial analytical foundation for detecting early warning signals, preventing potential cascades and designing effective prevention.

Where do systems intersect and where do potential risks emerge as a result?

Selection of examples

► Human–Animal interface: How do humans and animals interact and which stable contact zones shape risk emergence? (e.g. hunting, markets, agriculture, settlement boundaries)

► Wildlife–Livestock interface: How do wild and domestic animals encounter each other and which zones of transmission or conflict arise from this? (e.g. grazing areas, feeding sites, migration corridors)

► Urban–Rural interface: How are urban and rural systems interwoven and which transitional spaces emerge? (e.g. commuter zones, peri-urban areas, infrastructure boundaries)

► Ecology–Infrastructure interface: How do natural systems and technical infrastructures intersect and where do tensions arise? (e.g. roads through ecosystems, dams, coastal protection structures)

► Science–Policy interface: How do scientific evidence and political decision-making processes interact and where do translation or delay zones emerge? (e.g. advisory bodies, evidence transfer, political prioritization)

► Health–Economy interface: How do health systems and economic systems influence each other? (e.g. work absences, production stoppages, supply chains)

► Economy–Policy interface: How do economic interests and political decisions interact? (e.g. market regulation, subsidies, crisis interventions)

► Infrastructure–Society interface: How do technical systems affect social systems and vice versa? (e.g. power outages, traffic collapse, digital dependencies)

► Financial system–Real economy interface: How do financial markets and real production systems intersect? (e.g. credit availability, liquidity crises, investment shocks)

► Migration–State interface: How do mobile populations and state systems interact? (e.g. border management, registration, provision of services)

► Humanitarian sector–State interface: How do state and non-state actors interlock? (e.g. coordination, mandate boundaries, operational gaps)

► Security sector–Civil society interface: How do security actors and the population interact? (e.g. evacuations, protection, trust)

► Technology–Society interface: How do technological systems shape social practices? (e.g. platform dependency, digital divides, cyber risks)

► Ecology–Economy interface: How do ecological changes affect economic systems? (e.g. resource scarcity, production risks, relocation of sites)

► Legal system–Policy interface: How do legal frameworks and political decisions interact? (e.g. constitutional limits, enforcement, norm conflicts)

► Media–Public interface: How do media logics and societal perception influence each other? (e.g. agenda-setting, polarization, information flows)

► Education–Labour market interface: How do qualification systems and employment systems intersect? (e.g. skills shortages, reskilling needs, social mobility)

► Water–Agriculture interface: How do hydrological systems and food systems interact? (e.g. irrigation, droughts, crop failures)

► Energy–Industry interface: How do energy supply and industrial production influence each other? (e.g. security of supply, peak loads, production stoppages)

► Culture–Governance interface: How do cultural norms shape political decision-making processes? (e.g. compliance, acceptance, risk perception)

► Climate–Health interface: How do climatic changes affect health systems and the health status of populations? (e.g. heatwaves, vector-borne diseases, air quality)

► Food system–Society interface: How do food systems interact with social structures and ways of life? (e.g. food insecurity, price shocks, dietary shifts)

► Supply chains–Regions interface: How do global supply chains intersect with local spaces and infrastructures? (e.g. dependencies, bottlenecks, regional vulnerability)

► Insurance sector–Risk landscape interface: How do insurance logics interact with real risk landscapes? (e.g. non-insurability, premium increases, protection gaps)

► International governance–National systems interface: How do international regulatory frameworks affect national structures and decisions? (e.g. implementation obligations, tensions, coordination gaps)

► Local communities–Global markets interface: How do global market logics intersect with local lived realities? (e.g. land-use changes, displacement, new dependencies)

► Religion/Faith–Governance interface: How do religious norms and belief systems influence political decision-making processes and compliance? (e.g. acceptance of measures, norm conflicts, mobilization)

► Tourism–Ecology interface: How do tourism activities interact with sensitive ecosystems? (e.g. overuse, protected areas, seasonal pressure)

► Tourism–Health interface: How do mobility and tourism influence the spread of health risks? (e.g. introduction of new pathogens, superspreading events)

► Science–Practice interface: How does scientific knowledge meet operational implementation in fields such as medicine, agriculture, construction, and emergency services? (e.g. implementation gaps, need for translation, adaptation of guidelines)

► Digital platforms–Democratic processes interface: How do platform logics affect democratic decision-making processes and public deliberation? (e.g. disinformation, polarization, mobilization)

► State–Society interface: How do state institutions and the population interact in everyday life and in crises? (e.g. trust, compliance, protest, participation)

► Politics–Society interface: How do political decisions intersect with societal expectations and lines of conflict? (e.g. acceptance of measures, polarization, mobilization)

► Health–Society interface: How do health systems and societal practices interact? (e.g. access, health behaviour, stigmatization)

► Science–Society interface: How does scientific knowledge directly intersect with everyday practices and perceptions? (e.g. trust in science, misinformation, need for translation)

► Economy–Society interface: How do economic structures shape lived realities and social stability? (e.g. unemployment, price shocks, inequality)

► Legal system–Society interface: How do legal norms and their enforcement affect people’s behaviour? (e.g. rule of law, access to justice, impunity)

► Religion/Faith–Society interface: How do religious norms and belief systems shape societal practices and risk perception? (e.g. acceptance of measures, taboos, solidarity)

Why these interfaces are indispensable

Because every global crisis can escalate at the interfaces themselves:

Pandemics

→ Human–Animal, Science–Policy, Health–Economy

Climate crises

→ Ecology–Infrastructure, Water–Agriculture

Financial crises

→ Financial System–Real Economy

Conflicts

→ Politics–Society, Security Sector–Civil Society

Supply crises

→ Infrastructure–Society, Energy–Industry

Information crises

→ Media–Public, Science–Policy

These interfaces can become the hotspots of global risk, and for that reason they must be explicitly visible within the GLF.

Source: AI-generated image (2026)

Instability

Instability describes the extent to which a system may be susceptible to disruption, overload, or the loss of its internal coherence. It can emerge when structural interconnections erode, when stress accumulates more rapidly than it can be absorbed, when existing governance gaps undermine coordination, or when ecological thresholds are reached or exceeded. Instability reveals where systems begin to lose their resilience, where feedback loops may become unpredictable, and where small disturbances can trigger disproportionately large effects. A 360-degree understanding of instability exposes early signals of fragmentation, systemic stress, institutional drift, and approaching tipping points. Instability thus functions as a central early-warning indicator, marking those zones in which risks may escalate, cascade or propagate across interconnected systems.

Selection of examples

► Fragmentation: When systems, actors, or responsibilities drift apart and lose their coherence. Fragmentation weakens a system’s ability to share information, coordinate decisions or act collectively. (e.g. institutional silos, sectoral isolation, regional disparities)

► Stress: When burdens, demands, or external pressures exceed or continuously strain a system’s capacities. Stress reduces adaptability, increases error susceptibility and accelerates exhaustion processes. (e.g. chronic overload, resource scarcity, permanent operational crises)

► Governance gaps: When responsibilities are unclear, accountabilities fragmented, or decision pathways blocked. Governance gaps prevent effective coordination, delay responses, and create operational blind spots. (e.g. missing mandates, competency conflicts, unclear leadership structures)

► Ecological tipping points: When ecological systems reach thresholds at which small changes can trigger irreversible or nonlinear shifts. Tipping points destabilize entire regions or sectors and can trigger global cascades. (e.g. forest dieback, coral bleaching, permafrost thaw)

► Systemic overload: When demands, burdens, or levels of demand persistently exceed a system’s absorption or processing capacity. (e.g. overburdened health systems, overcrowded infrastructures)

► Resource depletion: When critical resources are consumed faster than they can be regenerated or replaced. (e.g. water scarcity, soil degradation, energy shortages)

► Loss of redundancies: When buffers, reserves, or alternative structures disappear, making the system more vulnerable to disruption. (e.g. single-supplier dependencies, missing backup systems)

► Dependencies and monocultures: When systems become overly dependent on single actors, technologies, or regions. (e.g. reliance on one energy source, a single supply chain)

► Information asymmetries: When relevant information is unevenly distributed, distorted, or available only with delay. (e.g. reporting delays, non-transparent data environments)

► Loss of trust: When trust in institutions, science, or actors erodes, reducing cooperation and compliance. (e.g. polarization, disinformation)

► Institutional drift: When institutions lose functionality, responsiveness, or coherence. (e.g. loss of expertise, reform stagnation)

► Lack of adaptability: When systems are unable to adjust to new conditions, risks, or insights. (e.g. rigid structures, outdated processes)

► Technological vulnerability: When digital or technical systems are prone to failures, attacks, or disruptions. (e.g. cyberattacks, system crashes, outdated IT)

► Socioeconomic inequality: When inequalities undermine the stability of social systems and amplify risks. (e.g. poverty, exclusion, service gaps)

► Political volatility: When political frameworks, priorities, or power relations are unstable or unpredictable. (e.g. government turnover, political deadlock)

► Lack of interoperability: When systems, sectors, or institutions cannot operate compatibly with one another. (e.g. incompatible data formats, disconnected infrastructures)

► Critical path dependencies: When past decisions lock the system into rigid, risky, or no-longer-reversible trajectories. (e.g. lock-in effects, dependence on outdated technologies)

► Nonlinear amplification effects: When small disturbances can trigger disproportionately large impacts. (e.g. domino effects, cascades, tipping points)

► Fragmented responsibilities: When responsibilities are unclear, overlapping, or contradictory. (e.g. competency conflicts, lack of coordination)

► Loss of social cohesion: When social cohesion, solidarity, or shared norms erode. (e.g. polarization, crises of trust, social tensions)

► Temporal delays (temporal lags): When reactions, decisions, or feedback arrive with delay, allowing risks to grow unnoticed. (e.g. delayed data reporting, slow decision processes, late interventions)

► Lack of anticipatory capacity (lack of foresight): When systems detect risks only once they are already escalating because anticipatory mechanisms are missing. (e.g. no scenarios, no early indicators, no trend analyses)

► Complexity overload: When systems can no longer process the multitude of variables, interactions, and uncertainties. (e.g. unmanageable networks, too many interfaces, operational overload)

► Normative incoherence: