Public and institutional policies in post-normal times: a framework for integrating post-normal strategiesinto the policy cycle

1. Introduction

We live in an era of accelerating uncertainties, where climate crises, technological advances, and social tensions challenge traditional governance models rooted in causality, instrumentation, and linear outcome assessment. The concept of Post-Normal Times emerges to describe contexts marked by complex, chaotic, and contradictory systems, demanding new logics for public policy.

Linear policymaking models, based on predictability and control, fail to address systemic crises. Rigid structures cannot absorb the impacts of unforeseen events such as pandemics or environmental collapses, resulting in inadequate responses.

Traditional futures research analyzes predictable patterns, such as megatrends (long-term transformations like digitalization or climate change), sectoral trends, weak signals (subtle signs of change), and wild cards (unlikely but impactful disruptions). These elements help design scenarios based on historical data and linear projections.

However, a new paradigm is emerging: scholars have begun to explore non-linear phenomena - such as abrupt discontinuities (e.g., pandemics), cascading effects (e.g., energy crises triggered by geopolitical conflict), and unpredictable events, integrating them into an expanded vision of future consciousness. This approach does not seek consensus on a single ideal future but instead organizes divergent visions into alternative scenarios, recognizing a plurality of possibilities.

Complexity (interconnected systems), Chaos (unpredictable changes), and Contradictions (unresolved tensions, such as economic growth vs. sustainability) are no longer seen as obstacles but rather as drivers of transformation toward post-normality. Similarly, Ignorance (what we don't know) and Uncertainty (what we can't foresee) become tools for anticipating risks and opportunities.

Within this context, the concept of “normality” is framed as an institutional and cultural construct that masks contradictions. Its breakdown reveals hidden vulnerabilities, such as structural inequalities or technological dependencies.

This shift demands that public policies and institutions move away from rigid and linear models. Instead of centralized control, it calls for anticipatory design of post-normal scenarios, capable of absorbing shocks, leveraging contradictions, and dynamically responding to the volatility of post-normal times.

Ignorance ("what we don’t know") and uncertainty ("what we can’t predict") are not limitations but strategic tools for identifying transitions to post-normal scenarios. Rather than failures to be corrected, they expose critical points where linear systems - such as traditional public policies and institutions with low anticipatory learning capacity - break down, revealing risks and opportunities in unstable contexts.

Thus, we begin with the premise that ignorance and uncertainty are not "enemies" to be eliminated, but vital signs that a system is entering a post-normal state. Mapping them helps avoid simplistic responses and build realistic strategies for an increasingly unpredictable future. Ignorance and uncertainty can help us identify future risks and opportunities in unstable systems.

Different levels of ignorance and uncertainty can help us anticipate Post-Normal Times, as shown in Table 1 below, adapted from Tõnurist & Hanson (2020, p. 438-446, apud Sardar & Sweeney, 2016, 75, p. 1-13).

Table 1 – Post-Normal Times and Types of Ignorance and Uncertainty

Adapted from Ferraz & Lourenço (2025), based on Tõnurist & Hanson (2020, pp. 438–446, as cited in Sardar & Sweeney, 2016, p. 1–13)

Post-Normal Times (3Ts)	Characteristics	Time Horizon	Type of Ignorance	Type of Uncertainty
Extended Present	Systems with post-normal potential. Normality is fabricated by institutions, culture, and our own perceptual resistance to change (post-normal lag). Dominated by entrenched megatrends, weak signals, and emerging issues.	5–10 years*	Simple Ignorance – Lack of information or awareness.	Surface Uncertainty – The direction of change is known, but its magnitude and likelihood cannot be estimated.
Familiar Futures	Shaped by projections and collective imagination, ranging from data-based forecasts to science fiction. These are extensions of the Extended Present.	10–20 years*	Solvable Ignorance – We don’t even know the right questions to ask.	Shallow Uncertainty – Limited knowledge about the general direction of change; complexity, chaos, and contradictions are emerging but not fully visible.
Unthought Futures	Infinite, previously unconsidered alternatives. Often rooted in earlier futures but now fully surfaced with deep implications.	Beyond 20 years*	Invincible Ignorance – Beyond current epistemologies and tools; rooted in blind spots of our worldviews.	Deep Uncertainty – Nothing is known about direction, magnitude, or impact. Our worldview is fundamentally inadequate to grasp what’s occurring.

Note: Time horizons are elastic and vary depending on thematic context.

Table 1 helps us better understand the relationship between ignorance and uncertainty: each type of uncertainty is linked to a specific form of unknown. Neither is a mere “gap,” but rather a phenomenon that exposes the fragility of linear systems (such as economic models or traditional public policies).

The analysis of post-normal time horizons based on Table 1 positions decision-makers in the present, but broadens their perspective beyond the traditional boundaries of risk management. It allows the construction of an ecosystemic anticipatory vision, capable of integrating the volatility of complex systems and guiding collective actions toward shared diagnostics and adaptive solutions.

Rather than simply prioritizing the "most likely" scenarios, this approach identifies potential discontinuities (abrupt changes), inflection points (critical moments of transformation), and rupture signals, exploring alternative futures—whether desirable, surprising, or disruptive.

These elements help us anticipate risks and opportunities in the transition toward post-normality, replacing the rigidity of linear models with the ability to navigate ambiguity and interdependence. They serve as anticipatory analytical gateways: by mapping them, we can identify the degree of post-normality within a system and prepare appropriate responses for each context.

2. Design and Strategies for Anticipatory Policies in Post-Normal Times

In post-normal times, effective governance requires replacing the pursuit of stability with the capacity to co-evolve with uncertainty. This means treating policy as a living ecosystem where institutions learn, citizens participate and the future is collectively shaped - not as a destination, but as a continuous process of discovery. The only certainty is change, and the best policies are those that learn to dance with it, not freeze it in time.

Key premises for designing public and institutional policies in post-normal times include:

• The Future as a Field of Potentialities: The future is not linear, but a dynamic field of possibilities where crises and innovations can redirect social, economic and environmental trajectories.

  
  

• Complexity, Chaos, and Contradiction as Catalysts of Post-Normality: The interplay of complex systems (e.g., the global economy), chaos (e.g., unpredictable effects of social networks), and contradictions (e.g., economic growth vs. sustainability) generates instabilities that disrupt traditional models.

  
  

• Abandoning the Illusion of Control: In nonlinear systems, total control attempts often fail (e.g., rigid lockdowns triggering economic crises). Rather than managing predictable risks, it's crucial to manage perceptions and reactions to uncertainty.

  
  

• Ignorance and Uncertainty as Strategic Resources: Ignorance (what we don't know) and uncertainty (what we can't predict) are maps to navigate the unknown, not failures.

  
  

• Epistemic Humility and Plurality of Knowledge and Action: No single group or discipline holds all answers. Epistemic humility acknowledges that scientific knowledge, while essential, is incomplete and must engage with traditional, local, and intuitive ways of knowing. Plurality of action means integrating marginalized voices (farmers, artists, community leaders) into the policy cycle, as diverse groups identify risks and opportunities invisible to experts.

  
  

• Antifragile Institutions with Continuous Learning: Traditional public policies and institutions with low anticipatory learning capacity fail, exposing risks and opportunities in unstable contexts. Antifragile institutions learn from crises and evolve through them. They are not “fail-proof,” but obsolescence-resistant, replacing the pursuit of control with the capacity to co-evolve with uncertainty.

  
  

• Policies as Continuous Experiments: Public policy should be seen as prototypes in testing, not final solutions. The experimental approach recognizes that no policy is ever finished but evolves through testing, adjustments, and learning.

  
  

• Post-Normal Strategies Integrated into the Policy Cycle: Anticipation must be part of all stages of the policy cycle, not an isolated action/step, or an island of futurists. It must encompass the entire policy cycle, from agenda-setting to outcome assessment, integrating anticipatory governance and consideration of post-normal potentials in a dynamic and co-evolutionary process.

2.1 Anticipatory Systems for Inevitable Surprises

Traditional public and institutional policy formulation, even when evidence-based, tends to operate on linear cause-and-effect logic. This approach creates a false sense of security by ignoring the complexity of dynamic systems, limiting itself to predictable metrics in order to avoid arbitrary decisions or temporary political influences.

Instead of denying uncertainties and systemic contradictions, policymakers must integrate the unknown into the public and institutional policy cycle. This implies adopting flexible strategies that combine risk anticipation, continuous adaptation, and the inclusion of multiple perspectives to navigate post-normal scenarios.

To achieve this, we need a shift in the guiding metaphor of our anticipatory systems—from "Machine" (Command and Control) to "Living Ecosystem" (Coevolution, systemic resilience, and adaptation), along with a mindset change from linear thinking to post-normal thinking, and the development of new learning and social knowledge production models where ignorance and uncertainty become resources for anticipatory learning.

We must learn to use new anticipatory governance tools (such as complexity, chaos, contradictions, and archetype mapping) to complement traditional foresight and scenario-building methods.

This calls for a new model of governance oriented toward living and complex social systems, where managing reactions and perceptions of change is as important as managing risks. This involves articulating types of ignorance and uncertainty with flexible strategies (adaptive, anticipatory, and pluralistic), integrating post-normal strategies into the policy cycle in a way that combines risk anticipation, continuous adaptation, and inclusion of multiple perspectives to navigate post-normal scenarios.

For conceptual clarity, we adopt analytically "pure" ideal types below, which must ultimately be integrated in professional practice:

Table 2 – Anticipatory Systems for Inevitable Surprises

Adapted from Ferraz & Lourenço (2025), based on Slaughter & Hanes (2020, pp. 174–179, 440–443, as cited in Hiltunen, 2012)

System Dimension	Linear Systems	Post-Normal Systems
Time Horizon	Linear time	Post-Normal Times (Extended Present, Familiar Futures, and Unthought Futures)
Epistemology	Linearity – Assumes predictable cause-effect relationships	Non-linearity – Embraces uncertainty and ignorance as partners in governing complex systems
Knowledge Source	Normality manufactured by institutions and dominant narratives	Collective reinvention
Relationship to Ignorance	Ignored or treated as a lack of data	Recognized as a resource for learning and innovation
Relationship to Uncertainty	Denied or minimized	Accepted and integrated
Operating Model	Stability and order through command and control	Resilience and adaptation through flexibility; incorporates the 3Cs (Complexity, Chaos, and Contradictions) as drivers of change and the Post-Normal Menagerie (wild archetypes) as governance tools
Strategic Approach	Risk management based on linear systems	Managing perceptions and responses to change by aligning types of ignorance and uncertainty with adaptive, anticipatory, and pluralist strategies to navigate post-normal potentials
Analytical Tools	Wildcards	Archetypes of divergent future events with post-normal potential (Post-Normal Menagerie: Black Swan, Grey Rhino, Black Jellyfish, Black Elephant)
Futures Learning	Environmental scanning, forecasting, weak signal analysis, emerging issues, trends, and megatrends	Mapping degrees of real and perceived post-normality within specific systems, questions, or horizons; identifying post-normal explosion conditions
Core Metaphor	Machine (Command and Control)	Ecosystem (Co-evolution)
Futures Orientation	Predictable and shaped by technical intervention	Emergent and co-constructed through systemic interactions
System Functioning	Replaceable parts, centralized planning	Dynamic interdependence, continuous evolution
Failure Dynamics	Catastrophic breakdown due to rigidity	Crises as opportunities for systemic reorganization
Response to Disruption	Resistance: “Return to the original state”	Resilience: “Transform toward a new equilibrium”

2.2 Mapping Post-Normal Archetypes to Define Anticipatory Strategies

In addition to the original archetypal “Menagerie” categories of Post-Normal Potentialities - animal metaphors used to represent future events with post-normal potential (Black Jellyfish, Black Swan, and Black Elephant) - this article incorporates the Grey Rhino archetype. This metaphorical “zoo” of post-normal futures is currently used by the OECD, based on Tõnurist & Hanson (2020, p. 15, citing Sardar & Sweeney, 2016), and here we refer to them collectively as Post-Normal Archetypes. 

These archetypes are more than metaphors: they are conceptual tools that help identify and categorize emerging challenges in contexts marked by high complexity, uncertainty, and contradiction. Each archetype is associated with a specific temporal horizon and requires distinct strategies for risk management and crisis navigation.

To develop the capacity to question, adapt, and collaborate - skills essential for navigating a world in continuous collapse and reinvention - it is crucial to understand the dynamics of post-normal time horizons.

In this regard, Table 3 organizes the analysis across the three temporal dimensions of post-normality - Extended Present, Familiar Futures, and Unthought Futures - articulating types of ignorance, uncertainty, and key questions that guide anticipatory strategies.

Table 3 – Post-Normal Times and Strategic Guiding Questions

Adapted from Ferraz & Lourenço (2025), based on Tõnurist & Hanson (2020, pp. 438–446, as cited in Sardar & Sweeney, 2016, pp. 1–13)

Post-Normal Times (3Ts)	Ignorance & Uncertainty	Uncertainty Level	Probability & Impact	Key Questions
Extended Present	Simple Ignorance & Surface Uncertainty	Known unknowns	Highly probable with high impact, but low credibility among non-expert stakeholders	- What trends are embedded in the Extended Present?- What don’t we know? (pure ignorance)- What are the surface-level uncertainties?- What obvious dangers are we ignoring?- Are there elements in denial of post-normal potential?- What are the Black Elephants we’re refusing to confront?- What public conversations are needed to explore their impact?
Familiar Futures	Solvable Ignorance & Shallow Uncertainty	Unknown unknowns	Highly unpredictable; not perceived even by experts; little to no prior knowledge of causes or consequences	- What imagined futures and trends are pulling us toward this horizon?- What must we learn that we don’t yet know? (solvable ignorance)- What are the shallow uncertainties?- Are there post-normal potentials within these futures?- What do people believe would “never happen”? (Black Swans)- What conversations should explore potential Black Swans?
Unthought Futures	Invincible Ignorance & Deep Uncertainty	Unknown unknowns	Huge impact on complex systems driven by positive feedback	-What assumptions underpin our forecasts in this horizon?- Are these assumptions valid under deep uncertainty and invincible ignorance?- What elements hold post-normal potential?- What could transform rapidly into high-impact phenomena? (Black Jellyfish)- Are conditions ripe for a post-normal explosion?- What would it take to trigger one?

Table 3 above provides a roadmap for transforming ignorance and uncertainty into strategic inputs, enabling governments and institutions not only to react to crises but to anticipate them and reinvent collapsing systems. It highlights the insight that, when it comes to anticipatory learning, questions are more important than answers.

In Table 4 below, we aim to correlate each archetype with the respective post-normal time horizons and appropriate anticipatory strategies:

Table 4 – Post-Normal Archetypes and Anticipatory Strategies

Adapted from Ferraz & Lourenço (2025), based on Tõnurist & Hanson (2020, pp. 438–443, as cited in Sardar & Sweeney, 2016, pp. 1–13)

Archetype	Post-Normal Time Horizon	Anticipatory Strategy
Black Jellyfish	Unthought Futures	- Overcoming invincible ignorance requires radically new ways of thinking.- Forces us to act with a false sense of confidence in existing paradigms of knowing, being, and doing.- Can only be addressed by questioning core axioms, challenging assumptions, and fundamentally rethinking our worldview.
Black Swan	Familiar Futures	- Cannot be fully addressed in the present but raises awareness about what we don’t know and need to explore.- The resulting uncertainty is complex but partially understandable.- Shallow uncertainty may evolve into surface uncertainty over time.
Black Elephant (In the Room)	Extended Present	- Can be managed with appropriate knowledge and foresight tools.- Simple ignorance can be overcome and surface uncertainty reduced through learning, research, and inclusive dialogue.- Requires confronting obvious but ignored contradictions.
Grey Rhino	Extended Present (resulting from inaction)	- Can also be managed with foresight and informed planning.- Simple ignorance can be addressed and surface uncertainty reduced through research, dialogue, and strategic questioning.- Note: If ignored, the Grey Rhino may escalate into an unthought future, generating complex crises. If addressed, it can become part of a familiar future and transform into a manageable challenge.

Note: If ignored, the Grey Rhino shifts into unthought futures, generating complex and unpredictable crises where past linear models no longer apply. If confronted, however, the Grey Rhino can be incorporated into familiar futures, becoming a manageable challenge.

Thereby, while the Black Swan demands preparation for the improbable, the Grey Rhino requires the courage to face the inevitable. The Black Elephant challenges institutional honesty, and the Black Jellyfish calls for humility in the face of the unknown. Governments that master this cartography of complexity will not only survive chaos—they will transform it into a lever for reinvention.

2.3 Integration of Post-Normal Strategies into the Policy Cycle

The integration of post-normal strategies into the public policy cycle requires a dynamic and adaptive approach, organized into three interdependent phases. Each phase corresponds to a stage in the transition of complex systems toward post-normality, combining specific actions to mitigate risks and seize opportunities in uncertain scenarios.

Table 5 below details these phases, their characteristics, and corresponding strategies:

Table 5 – Post-Normal Process and Anticipatory Policy Strategies

Adapted from Ferraz & Lourenço (2025), based on Slaughter & Hanes (2020, pp. 446–450, apud Sardar & Sweeney, 2016, pp. 1–13)

Phase	Characteristics	Strategies
Apparent Normality	The system is complex and interconnected but appears to function normally. However, any small disruption—such as ignoring certain levels of ignorance or neglecting uncertainty—can quickly lead to uncontrollable consequences and trigger post-normality.Any intervention, such as a poorly designed policy, a protest, conflict, gross injustice, or environmental degradation, may accelerate the system into a post-normal state.A Black Elephant or a Black Swan may also already be present in the system.	- Simplify the system.- Reduce redundant interconnections and sensitive dependencies between open systems to lower complexity.- Identify specific elements in the system with post-normal potential, such as overlooked contradictions.- Map Black Elephants: What are the elephants in the room that must be urgently addressed?
Systems with Post-Normal Potential	A positive feedback loop increases the instability of a dynamic system, potentially activating post-normality, and the system begins to show signs of chaos.	- Pay attention to attractors that intensify positive feedback, especially in areas of neglected uncertainty and deeper ambiguity or ignorance.- Identify and, if possible, block destructive feedback loops.- Decomplexify systemic interconnections and address contradictions.
Chaos	Chaos takes over, and the system becomes post-normal.	- Continue resolving deep-rooted contradictions in the system.- Seek to reduce or reconfigure positive feedback loops as much as possible.- Reexamine foundational assumptions, values, and core axioms.

The integration of post-normal strategies into the policy cycle does not aim to avoid chaos, but rather to transform it into an opportunity for systemic reinvention.

By recognizing that stability is temporary and uncertainty is structural, governments can abandon the illusion of control and adopt anticipatory, flexible, and collaborative management—prepared to navigate increasingly unpredictable futures.

2.4 Complementary Actions for Policy Cycle Integration

Integrating post-normal strategies into the policy cycle requires specific actions at each stage, combining anticipation, flexibility, and continuous learning.

Figure 1 below outlines practical examples of how these actions can be applied, aligned with the archetypes and post-normal time horizons.

Figure 1 - Complementary integration actions into the Policy Cycle

Adapted from Ferraz & Lourenço (2025)

In this regard, to reinforce the arguments and demonstrate feasibility, we present some recent global cases of complementary actions integrated into the Policy Formulation Cycle, as shown in Table 6 below:

Table 6 – Recent Cases of Complementary Actions in the Policy Cycle

Adapted from Ferraz & Lourenço (2025)

Archetype	Policy Cycle Phase	Concrete Action	Global Example
Black Elephant	Agenda	Map obvious contradictions	India prioritized basic sanitation after acknowledging that 60% of diseases came from contaminated water.
Grey Rhino	Policy Formulation	Develop contingency plans	The Netherlands created a national sea-level rise adaptation plan through 2100, updated every five years.
Black Swan	Decision-making	Allocate budget reserves for unpredictable crises	Norway maintains a sovereign wealth fund for technological and climate-related emergencies.
Black Jellyfish	Policy Evaluation	Revise paradigms after system collapse	After Hurricane Maria, Puerto Rico redesigned its energy matrix using decentralized solar microgrids.

3. Final Considerations and Key Benefits of Post-Normal Strategies Integrated into Public and Institutional Policy Cycles

In a world of increasingly accelerated change, the ability to navigate complexity—recognizing ignorance and uncertainty as anticipatory knowledge resources for learning and innovation, rather than denying or minimizing them—and to manage reactions and perceptions of change by articulating types of ignorance and uncertainty with adaptive, anticipatory, and pluralistic strategies, complementary to traditional policy design and linear risk management methods, will become the new standard for effective public and institutional policymaking.

This approach—integrating post-normal strategies into the policy cycle—offers four strategic advantages:

• Crisis Prevention – Identifying systemic contradictions and crisis amplification mechanisms before they lead to collapse or rupture scenarios, and acting preventively by combining traditional methods with foresight tools.

  
  

• Adaptive Systemic Resilience – Designing policies that evolve alongside change, even in chaotic or transitional contexts, by navigating deep uncertainties and ambiguities, while anticipating emerging challenges.

  
  

• Anticipatory Governance and Institutional Innovation – Implementing agile and inclusive anticipatory governance models; cultivating the capacity to foresee potential post-normal futures; and guiding policy evaluation through non-linear metrics, thereby complementing conventional approaches.

  
  

• Anticipatory Accountability – Ensuring integrity, transparency, and stakeholder or public participation in the policy cycle and in post-normal anticipatory strategies, with proactive communication and public oversight of preventive actions.

REFERENCES

SLAUGHTER, Richard; HANES, Andy. The Knowledge Base of Futures Studies. Washington, DC: APF – Association of Professional Futurists, 2020.

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TÕNURIST, P.; HANSON, A. Anticipatory Innovation Governance: Shaping the Future through Proactive Policymaking. OECD Working Papers on Public Governance, No. 44, OECD Publishing, Paris, 2020. Available at: https://doi.org/10.1787/cce14d80-en. Accessed on: October 30, 2024.