Strategic Foresight and Long-Term Thinking: How to Build Strategy for an Uncertain Future

Last Updated June 4, 2026

Strategic foresight and long-term thinking refer to the disciplined practice of anticipating change, exploring multiple possible futures, and designing strategies that remain robust under conditions of uncertainty. In complex environments, organizations cannot rely exclusively on short-term optimization or linear forecasting. Technological disruption, geopolitical instability, ecological stress, institutional change, demographic transition, infrastructure constraint, and shifting social expectations create conditions in which the future cannot be treated as a simple extension of the present.

Strategic foresight responds to this challenge by helping decision-makers widen their temporal horizon, question default assumptions, examine weak signals, explore alternative futures, and prepare for plausible developments before they become unavoidable. Long-term thinking complements this practice by shifting attention from immediate performance metrics to enduring trajectories, structural risks, path dependence, intergenerational consequences, and cumulative system effects.

Together, foresight and long-term thinking help organizations move from reactive adaptation to anticipatory strategy. They do not eliminate uncertainty. They create a more intelligent relationship to it. UNESCO’s Futures Literacy work is especially clear that future-oriented capability is not only about prediction, but about improving how people understand and act in the present as change unfolds. OECD, UNDP, and the UK Government Office for Science similarly frame foresight as a structured practice for exploring futures rather than forecasting one deterministic outcome.

At its deepest level, foresight is not merely a planning technique. It is a way of changing how the present is interpreted. Once multiple futures are brought into view, current arrangements begin to look less fixed and more contingent. Long-term thinking therefore matters not only because it extends the planning horizon, but because it widens the field of strategic imagination from which present action is chosen.

This article examines strategic foresight and long-term thinking as core practices in strategic ideation. It explores why long-term thinking matters, how foresight differs from forecasting, how horizon scanning and weak-signal interpretation work, how scenarios and alternative futures support strategy, how path dependence shapes future choice, why public institutions need anticipatory capacity, what organizational barriers prevent long-term thinking, and how futures literacy can change present judgment.

Why Long-Term Thinking Matters

Many institutions are structurally biased toward the short term. Quarterly reporting cycles, annual budgets, election calendars, funding rounds, performance dashboards, campaign timelines, and operational urgency all encourage immediate action. These structures are not irrational. They reflect real constraints. Yet they can also narrow strategic imagination. When organizations become overly focused on near-term efficiency, they often underinvest in resilience, ignore slow-moving risks, misread structural change, and fail to prepare for future conditions that do not yet impose obvious costs.

Long-term thinking matters because many of the most consequential strategic shifts are gradual before they become visible. Ecological degradation, demographic transition, technological diffusion, institutional erosion, infrastructure lock-in, changing public expectations, and geopolitical realignment may accumulate over years or decades before producing sudden strategic consequences. An organization that waits until such changes are fully obvious often discovers that its room for maneuver has already narrowed.

The UK Government’s Futures Toolkit frames futures tools as methods for building strategies that are robust to uncertainty rather than tied only to immediate conditions. OECD foresight materials similarly emphasize the anticipatory function of foresight: decision-makers need structured ways to prepare before change arrives in full force.

Long-term thinking matters because the future often becomes strategically decisive before it becomes institutionally convenient.

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Strategic Foresight as Structured Anticipation

Strategic foresight is not prediction in the narrow sense. It does not claim that a single future can be forecast with precision and then managed accordingly. Instead, it is a structured process for identifying signals of change, exploring critical uncertainties, constructing plausible scenarios, stress-testing assumptions, and developing strategies that can perform across different future conditions.

This distinction is essential. Forecasting tends to ask what is most likely. Foresight asks what is possible, what is consequential, what is changing, what remains uncertain, what should be monitored, and what present choices may shape future pathways. It broadens the strategic conversation by examining not only probability, but also preparedness, resilience, optionality, and consequence.

OECD describes strategic foresight as a structured and systematic approach to exploring plausible futures. Its foresight toolkit presents foresight as a practical process for challenging assumptions, creating scenarios, stress-testing strategies, and developing actionable plans. UNDP’s foresight manual likewise frames foresight as working with alternative futures rather than forecasting a single future. The UK Government toolkit similarly presents futures thinking as a practical way to build robust strategies under uncertainty.

Strategic foresight is structured anticipation: not knowing the future, but improving present judgment by exploring futures rigorously.

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The Intellectual Foundations of Foresight

Strategic foresight draws from several traditions, including systems thinking, scenario planning, futures studies, policy analysis, organizational learning, risk analysis, design thinking, resilience thinking, and strategic management. Its deeper premise is that the future should not be treated merely as a destination to be predicted, but as a field of possibility that helps decision-makers reinterpret the present.

In this sense, foresight is both analytical and imaginative. It combines evidence, structured exploration, disciplined narrative construction, uncertainty analysis, and practical decision design. It asks decision-makers to treat the present not as fixed, but as one possible pathway among many. Once alternative futures are made visible, current assumptions can be examined more clearly.

Futures-oriented work has long emphasized that the images societies hold about the future influence present behavior. If the future is imagined as fixed, institutions tend to become reactive. If it is imagined as open but structured, organizations can begin to act more deliberately in the present. This is one reason foresight is closely linked to anticipatory governance, resilience planning, long-term institutional design, and futures literacy.

Foresight is strongest when imagination is disciplined by evidence, systems awareness, and decision relevance.

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Strategic foresight becomes useful when temporal depth, signals, uncertainties, scenarios, stress testing, adaptive pathways, and present interpretation are connected to decisions.

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Foresight versus Forecasting

Forecasting is often useful in bounded domains where patterns are stable enough for extrapolation. Demand projections, budget assumptions, demographic estimates, and operational planning can all benefit from careful forecasting. But in complex systems, extrapolation becomes less reliable when feedback loops, threshold effects, political disruption, technological discontinuities, or ecological constraints reshape the environment.

Foresight differs because it does not assume that the future can be reduced to a single expected line. It is especially useful when the strategic task is not merely to estimate next-period values but to prepare for structural change, contested pathways, and emerging uncertainty. In that sense, foresight complements forecasting rather than replacing it.

UNDP’s foresight manual uses similar language, describing foresight as an umbrella for strategic planning and solution-design methods that do not predict or forecast the future, but work with alternative futures. OECD’s foresight materials also distinguish foresight from prediction by stressing alternative futures, strategic readiness, and resilience rather than point forecasts.

Forecasting helps estimate. Foresight helps prepare.

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Time Horizons and Strategic Depth

One of the most important contributions of long-term thinking is temporal depth. Strategic problems often look different depending on the horizon through which they are viewed. A decision that appears efficient over one year may generate fragility over five. A policy that looks expensive in the short run may reduce systemic risk over twenty. A technology that seems marginal today may become infrastructure tomorrow.

Time horizon therefore functions as a strategic filter. Short horizons privilege optimization and immediate return. Longer horizons bring into view durability, path dependence, externalities, capability development, institutional trust, intergenerational responsibility, and cumulative system effects. Effective strategic foresight does not ignore the short term. It situates the short term within a broader temporal architecture.

Temporal depth helps strategy see what short-term metrics hide.

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Scenario Planning and Alternative Futures

Scenario planning is one of the most widely used foresight methods because it translates uncertainty into structured alternatives. Rather than asking which future will occur, scenario planning explores multiple plausible worlds shaped by different combinations of drivers and uncertainties. This helps organizations identify assumptions embedded in their current strategy and discover where those assumptions may fail.

Scenarios also improve strategic conversation. They make long-term uncertainty discussable. Teams can examine which strategies remain effective across several scenarios, which strategies are brittle, which assumptions are fragile, and what early indicators might suggest that one future pathway is becoming more plausible than another.

Scenario planning also supports ideation. A future defined by regulatory disruption may generate ideas about transparency, governance, public trust, and compliance infrastructure. A future shaped by climate stress may generate ideas about redundancy, adaptation, energy systems, and distributed resilience. A future shaped by rapid automation may generate ideas about workforce transition, human oversight, data governance, and institutional accountability.

Scenario planning strengthens foresight by making uncertainty concrete enough to test strategy against it.

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Long-Term Thinking and Path Dependence

Long-term strategy must account for path dependence: the fact that present choices shape future options. Infrastructure, regulation, institutional design, technology standards, platform architectures, funding models, procurement choices, and cultural narratives all create trajectories that may become difficult to reverse later. A short-term decision can therefore have long-term lock-in effects.

This is especially important in sustainability, urban planning, digital governance, energy systems, education, health systems, logistics, public infrastructure, artificial intelligence, and social policy. A system that is cheap to build now may be expensive to redesign later. A platform architecture that accelerates deployment today may constrain future accountability. A policy designed for immediate efficiency may create long-term exclusion or fragility.

Strategic foresight helps decision-makers recognize these lock-in dynamics before they become entrenched. It encourages them to ask not only what is efficient now, but what future pathway is being built by today’s decision.

Long-term thinking matters because every strategy is also a pathway-building decision.

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Anticipation, Resilience, and Adaptive Capacity

Foresight is valuable not because it provides certainty, but because it improves preparedness. One of its main strategic functions is to strengthen resilience by helping organizations anticipate disruption, diversify options, avoid overcommitting to narrow assumptions, and build capabilities before crisis conditions force rushed action.

This can include investing in modular systems, developing contingency plans, building learning capacity, identifying strategic pivots, monitoring early signals, preserving option value, and developing governance systems that know when to revise strategy. Strategic foresight supports resilience by linking long-term awareness with present capabilities for monitoring, interpretation, and adaptation.

Adaptive capacity is central here. Organizations cannot prepare in detail for every possible future, but they can prepare structurally to learn, respond, and adjust. Foresight therefore does not ask institutions to know everything in advance. It asks them to become better prepared to act when conditions change.

Foresight improves resilience by helping organizations prepare before the future becomes urgent.

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Signals, Weak Signals, and Emerging Change

Not every future transformation begins with a dramatic event. Many begin as weak signals: small anomalies, marginal innovations, emerging behaviors, early policy experiments, fringe narratives, unexpected failures, local disruptions, or low-salience shifts that seem minor until they compound. Horizon scanning helps organizations pay attention to such developments before they become dominant trends.

The challenge is interpretive. Weak signals are ambiguous by definition. Most do not become decisive, but some indicate deeper structural change. Foresight helps institutions avoid dismissing all weak signals as noise while also avoiding overreaction to every novelty. It creates a disciplined way to interpret emerging change.

Weak signals matter because strategic change often appears first as something easy to dismiss.

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Strategic Foresight in Public Systems and Institutions

Public institutions often have a special need for long-term thinking because they govern infrastructures, environments, social systems, and public goods whose consequences unfold over long periods. Yet they also face short-term political pressures. Strategic foresight can help address this tension by providing structured methods for exploring emerging risks, long-range scenarios, anticipatory governance options, and future public value.

This is why foresight is increasingly associated with resilience, policy innovation, public-sector capacity, and future-fit institutions. Public organizations cannot be judged only by how efficiently they manage current conditions. They must also be assessed by how well they prepare societies for conditions not yet fully visible.

UNDP’s foresight manual and future-of-development materials frame foresight in developmental and governance terms. OECD’s work on anticipatory capacity in government similarly treats foresight as vital for effective decision-making on complex public issues.

Public foresight is not only about future risk. It is about institutional responsibility over time.

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Organizational Barriers to Long-Term Thinking

Despite its importance, long-term thinking is difficult to sustain institutionally. Several barriers recur. Immediate incentives often dominate deferred benefits. Decision-makers may be rewarded for short-term results rather than durable preparedness. Information systems may prioritize near-term metrics. Political or executive cycles may discourage investments whose value becomes visible only later. Organizational culture may also treat uncertainty as something to suppress rather than explore.

These barriers do not make foresight impossible, but they do mean that it must be institutionalized deliberately. It cannot remain a one-off workshop or symbolic exercise. It needs process, leadership attention, interpretive capacity, governance, and links to resource allocation and strategy review.

Long-term thinking must be designed into institutions, or short-term pressure will usually defeat it.

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Long-term foresight is serious when it changes what the organization monitors, protects, invests in, and is willing to question.

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The Limits of Foresight

Strategic foresight has limits, and acknowledging them makes the practice stronger. It does not eliminate uncertainty. It does not guarantee that the right scenario has been imagined. It can become overly speculative if not grounded in evidence, or overly abstract if not tied to decisions. It may also become performative if organizations use the language of foresight without allowing it to challenge present assumptions, budgets, priorities, or power structures.

Foresight can also suffer from participation bias. The people invited to imagine the future shape what futures are treated as plausible. Institutional interests may narrow the scenario set. Technical experts may overemphasize technological change while underemphasizing legitimacy, culture, equity, or implementation. Leaders may prefer scenarios that preserve current strategy. Stakeholders without institutional power may be excluded.

Foresight should sharpen decision quality, not become a substitute for decision.

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Ethics and Intergenerational Responsibility

Long-term thinking is ethically charged because present decisions affect people who may not be represented in current decision processes. Future generations cannot attend today’s strategy meetings. Communities affected by future infrastructure, climate exposure, automation, environmental degradation, or institutional failure may have little voice in the choices that shape their conditions.

Strategic foresight should therefore include ethical review. It should ask who benefits, who bears risk, whose future is being imagined, whose knowledge counts, and whether the organization is externalizing cost into the future. It should also examine intergenerational responsibility: the obligation to consider long-term consequences beyond current leadership, customers, voters, or investors.

Ethical foresight asks not only what futures are possible, but whose futures are being made more or less possible by present strategy.

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Futures Literacy and the Present

A deeper contribution of long-term thinking is that it changes how the present is understood. Considering multiple futures reveals that current systems are neither inevitable nor fixed. It becomes easier to see which arrangements are contingent, which assumptions are narrow, which pathways are being reproduced through habit, and which alternatives have been excluded by institutional routine.

UNESCO’s conception of Futures Literacy is especially important here because it presents future-awareness as a capability that enables people to rethink what they see and do in the present. Futures literacy is not merely a skill for imagining tomorrow. It is a way of improving present perception.

In this sense, futures thinking is not only about what lies ahead. It is also about what becomes visible now when the present is viewed through a wider temporal lens. That is one reason foresight is not just an adjunct to strategy, but part of how strategy becomes more reflective, less brittle, and more capable of confronting structural change early enough to matter.

Futures literacy matters because it helps decision-makers see that the present is not destiny.

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A serious foresight audit should leave behind decisions, signals, assumptions, pathways, and learning records—not only future-oriented discussion.

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Mathematical Lens: Foresight, Temporal Depth, and Strategic Robustness

A stylized representation of short-term optimization can be written as:

Strategic foresight instead treats decision quality as depending on performance across multiple plausible futures:

Temporal depth can also be represented conceptually as:

Path dependence can be represented as:

The mathematical lens clarifies a central foresight problem: strategies should be evaluated not only by present payoff, but by their robustness, future consequences, and effect on later choice.

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Advanced R Workflow: Comparing Long-Term Strategy Profiles

The R workflow below compares stylized strategies across short-term return, foresight depth, resilience, flexibility, and path-dependence risk. It is designed as an evergreen illustration of how long-term thinking changes strategic evaluation.

# Install packages if needed.
# install.packages(c("tidyverse"))

library(tidyverse)

# ------------------------------------------------------------
# R Workflow: Comparing Long-Term Strategy Profiles
# Purpose:
#   Build stylized profiles across strategies using
#   short-term return, foresight depth, resilience,
#   flexibility, and path dependence risk.
# ------------------------------------------------------------

strategies <- tibble(
  strategy = c(
    "Short-Term Efficiency Strategy",
    "Balanced Foresight Strategy",
    "Resilience-Biased Long-Horizon Strategy",
    "Lock-In Prone Growth Strategy"
  ),
  short_term_return = c(0.86, 0.72, 0.61, 0.83),
  foresight_depth = c(0.24, 0.79, 0.84, 0.31),
  resilience = c(0.32, 0.76, 0.88, 0.37),
  flexibility = c(0.28, 0.74, 0.79, 0.26),
  path_dependence_risk = c(0.71, 0.39, 0.34, 0.82)
)

strategies <- strategies %>%
  mutate(
    foresight_profile =
      0.16 * short_term_return +
      0.24 * foresight_depth +
      0.24 * resilience +
      0.20 * flexibility -
      0.16 * path_dependence_risk,
    short_term_bias =
      short_term_return - ((foresight_depth + resilience + flexibility) / 3),
    future_viability =
      0.30 * resilience +
      0.25 * flexibility +
      0.25 * foresight_depth -
      0.20 * path_dependence_risk
  )

print(strategies)

strategies_long <- strategies %>%
  pivot_longer(
    cols = c(
      short_term_return,
      foresight_depth,
      resilience,
      flexibility,
      path_dependence_risk
    ),
    names_to = "dimension",
    values_to = "value"
  )

ggplot(strategies_long, aes(x = dimension, y = value, fill = strategy)) +
  geom_col(position = "dodge") +
  labs(
    title = "Stylized Strategic Foresight Dimensions",
    x = "Dimension",
    y = "Value",
    fill = "Strategy"
  ) +
  theme_minimal(base_size = 12) +
  coord_flip()

ggplot(strategies, aes(x = reorder(strategy, foresight_profile), y = foresight_profile)) +
  geom_col() +
  coord_flip() +
  labs(
    title = "Stylized Long-Term Strategy Profile",
    x = "Strategy",
    y = "Profile Score"
  ) +
  theme_minimal(base_size = 12)

ggplot(strategies, aes(x = reorder(strategy, future_viability), y = future_viability)) +
  geom_col() +
  coord_flip() +
  labs(
    title = "Future Viability Profile",
    x = "Strategy",
    y = "Future Viability"
  ) +
  theme_minimal(base_size = 12)

write_csv(strategies, "strategic_foresight_profiles.csv")

This workflow is not a universal scoring system. Its value is methodological: it helps teams see how short-term return can conflict with future viability, resilience, flexibility, and lock-in risk.

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Advanced Python Workflow: Simulating Strategic Foresight Across Alternative Futures

The Python workflow below simulates stylized strategies across changing future conditions, showing how brittle short-term strategies can weaken as uncertainty unfolds while foresight-oriented strategies preserve viability more effectively.

# Install packages if needed:
# pip install pandas numpy matplotlib

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

# ------------------------------------------------------------
# Python Workflow: Simulating Strategic Foresight
# Purpose:
#   Compare stylized strategies across changing conditions
#   using return, resilience, flexibility, and lock-in risk.
# ------------------------------------------------------------

time_steps = np.arange(1, 41)

def simulate_strategy(return_fit, resilience, flexibility, lock_in_risk, foresight_depth, initial_state=1.0):
    state = np.zeros(len(time_steps))
    option_value = np.zeros(len(time_steps))
    state[0] = initial_state
    option_value[0] = 0.70

    for t in range(1, len(time_steps)):
        if t < 20:
            shock = 0.03
            gain = 0.18 * return_fit + 0.06 * flexibility + 0.04 * foresight_depth
        else:
            shock = 0.15
            gain = (
                0.08 * return_fit +
                0.18 * resilience +
                0.14 * flexibility +
                0.10 * foresight_depth -
                0.14 * lock_in_risk
            )

        option_value[t] = option_value[t - 1] + 0.05 * flexibility + 0.04 * foresight_depth - 0.08 * lock_in_risk
        option_value[t] = np.clip(option_value[t], 0, 1.2)

        state[t] = state[t - 1] + gain / 4 - shock / 5 + option_value[t] / 40
        state[t] = np.clip(state[t], 0, 1.8)

    return state, option_value

strategies = {
    "Short-Term Efficiency Strategy": {
        "return_fit": 0.86,
        "resilience": 0.32,
        "flexibility": 0.28,
        "lock_in_risk": 0.71,
        "foresight_depth": 0.24
    },
    "Balanced Foresight Strategy": {
        "return_fit": 0.72,
        "resilience": 0.76,
        "flexibility": 0.74,
        "lock_in_risk": 0.39,
        "foresight_depth": 0.79
    },
    "Resilience-Biased Long-Horizon Strategy": {
        "return_fit": 0.61,
        "resilience": 0.88,
        "flexibility": 0.79,
        "lock_in_risk": 0.34,
        "foresight_depth": 0.84
    },
    "Lock-In Prone Growth Strategy": {
        "return_fit": 0.83,
        "resilience": 0.37,
        "flexibility": 0.26,
        "lock_in_risk": 0.82,
        "foresight_depth": 0.31
    }
}

df = pd.DataFrame({"time": time_steps})
option_df = pd.DataFrame({"time": time_steps})

for name, params in strategies.items():
    path, options = simulate_strategy(**params)
    df[name] = path
    option_df[name] = options

print(df.head())

plt.figure(figsize=(10, 6))
for col in df.columns[1:]:
    plt.plot(df["time"], df[col], label=col)

plt.xlabel("Time Step")
plt.ylabel("Strategic Viability")
plt.title("Strategic Foresight Across Alternative Futures")
plt.legend()
plt.tight_layout()
plt.show()

summary = df.drop(columns=["time"]).iloc[-1].sort_values(ascending=False)
print(summary)

df.to_csv("strategic_foresight_simulation.csv", index=False)
option_df.to_csv("strategic_option_value_simulation.csv", index=False)

This simulation is intentionally stylized. Its value is conceptual: a strategy that performs well in the short run may become brittle if it lacks foresight depth, flexibility, resilience, and option value.

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GitHub Repository

The companion repository for this article will provide advanced strategist-facing workflows for strategic foresight diagnostics, horizon scanning, weak-signal interpretation, driver mapping, long-term strategy scoring, scenario stress testing, temporal-depth analysis, path-dependence review, option-value modeling, anticipatory governance review, futures ethics, and foresight learning memory.

The repository structure is designed to support professional strategic analysis rather than generic coding demonstrations. The python/ folder can model foresight depth, resilience, flexibility, lock-in risk, signal monitoring, scenario stress tests, and long-term strategic viability. The r/ folder can compare long-term strategy profiles and visualize future viability. The julia/ folder can support sensitivity analysis for time horizons, shocks, option value, and path dependence. The sql/ folder can define schemas for signals, drivers, scenarios, long-term strategies, path dependencies, adaptive pathways, governance reviews, ethics reviews, and learning memory.

Additional folders can support command-line diagnostics, lower-level scoring utilities, and reproducible documentation. The rust/ folder can provide a command-line foresight diagnostics scaffold. The go/ folder can provide long-term strategy comparison utilities. The cpp, fortran, and c folders can provide efficient scoring examples and low-level utilities. The docs, data, outputs, and notebooks folders can support article notes, modeling principles, synthetic datasets, generated outputs, and notebook placeholders.

This code should be understood as a transparent learning and modeling scaffold. It is intended for synthetic-data research, methods demonstration, institutional learning, strategic analysis, and reproducible workflow development. It is not a substitute for participatory foresight, ethical review, domain expertise, expert judgment, accountable governance, or responsible implementation.

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Conclusion

Strategic foresight and long-term thinking are essential for navigating uncertainty in complex systems because they expand the temporal horizon of strategy, challenge default assumptions, and prepare organizations to act under multiple plausible futures. They shift the focus from prediction alone to preparedness, from short-term optimization to durable resilience, and from reactive management to anticipatory design.

The future cannot be controlled, but it can be engaged more intelligently. Organizations that cultivate foresight are better positioned to identify emerging change, avoid brittle commitments, preserve future choice, and develop strategies that remain coherent under shifting conditions. In a world defined by interdependence, uncertainty, and accelerating transformation, that capability is not a luxury. It is a foundation of serious strategic thinking.

Used poorly, foresight becomes trend commentary, speculative theater, or long-range reporting detached from decisions. Used well, it becomes a disciplined practice for seeing present choices through the lens of future consequence.

Better strategies emerge when organizations stop treating the future as either predictable or unknowable and begin treating it as a disciplined field of strategic inquiry.

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Related Articles

• Strategic Ideation
• Scenario Planning and Futures Thinking
• Decision-Making Under Uncertainty
• Risk, Tradeoffs, and Strategic Choices
• Option Value and Strategic Flexibility
• Portfolio Thinking in Strategic Ideation
• Adaptive Strategy and Iteration
• Futures Thinking
• Systems Thinking
• Resilience Thinking

Further Reading

• Organisation for Economic Co-operation and Development (OECD) (no date) Strategic Foresight. Available at: OECD.
• Organisation for Economic Co-operation and Development (OECD) (2025) Foresight Toolkit for Resilient Public Policy. Paris: OECD Publishing. Available at: OECD.
• United Nations Development Programme (UNDP) (2018) Foresight Manual: Empowered Futures for the 2030 Agenda. New York: UNDP. Available at: UNDP.
• United Nations Educational, Scientific and Cultural Organization (UNESCO) (no date) Futures Literacy & Foresight. Available at: UNESCO.
• UK Government Office for Science (2024) The Futures Toolkit. London: Government Office for Science. Available at: UK Government.
• Voros, J. (2003) ‘A generic foresight process framework’, Foresight, 5(3), pp. 10–21.
• Wack, P. (1985) ‘Scenarios: Uncharted waters ahead’, Harvard Business Review, 63(5), pp. 73–89.

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References

• Organisation for Economic Co-operation and Development (OECD) (no date) Strategic Foresight. Available at: OECD.
• Organisation for Economic Co-operation and Development (OECD) (2025) Foresight Toolkit for Resilient Public Policy. Paris: OECD Publishing. Available at: OECD.
• United Nations Development Programme (UNDP) (2018) Foresight Manual: Empowered Futures for the 2030 Agenda. New York: UNDP. Available at: UNDP.
• United Nations Development Programme (UNDP) (no date) Future of Development. Available at: UNDP.
• United Nations Educational, Scientific and Cultural Organization (UNESCO) (no date) Futures Literacy & Foresight. Available at: UNESCO.
• United Nations Educational, Scientific and Cultural Organization (UNESCO) (no date) About Futures Literacy. Available at: UNESCO.
• UK Government Office for Science (2024) The Futures Toolkit. London: Government Office for Science. Available at: UK Government.
• Voros, J. (2003) ‘A generic foresight process framework’, Foresight, 5(3), pp. 10–21.
• Wack, P. (1985) ‘Scenarios: Uncharted waters ahead’, Harvard Business Review, 63(5), pp. 73–89.

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