A Professional Foresight Taxonomy for Strategic Foresight Practice

Strategic foresight does not attempt to predict a single future. Instead, it explores multiple possible futures and the forces that may shape them, enabling organizations and policymakers to anticipate uncertainty and design adaptive strategies (Miller, 2018; Wilkinson & Kupers, 2013). Unlike traditional forecasting, which extrapolates trends to estimate likely outcomes, foresight seeks to map the broader landscape of potential developments, including disruptions, emerging risks, and systemic transformations (Voros, 2003).

In practice, foresight initiatives conducted by institutions such as the OECD and the World Economic Forum rely on structured analytical frameworks to classify different types of futures and guide scenario exploration (OECD, 2019). These frameworks support decision-makers in navigating uncertainty by considering multiple dimensions of change, including values, trends, governance, systemic dynamics, and disruptive shocks.

The taxonomy illustrated in the figure reflects this multidimensional approach. It organizes futures into several analytical domains that capture different ways in which futures emerge and evolve.

Normative Futures: Value-Driven Visions

Normative futures represent value-driven visions of the future. Rather than describing what is likely to occur, they articulate what stakeholders believe should occur, reflecting societal values, ethical principles, or strategic objectives (Bell, 1997; Miller, 2018).

Normative thinking plays a central role in sustainability transitions and long-term governance because it links foresight exercises to collective aspirations and societal priorities (Bell, 1997; Voros, 2003).

Examples include:

• Ideal futures, representing theoretically optimal outcomes for society or systems (Bell, 1997).
• Visionary futures, long-term aspirational directions guiding strategic orientation (Wilkinson & Kupers, 2013).
• Normative futures, reflecting societal values or policy goals (Miller, 2018).
• Desirable futures, outcomes stakeholders actively aim to achieve (Voros, 2003).
• Preferable futures, scenarios considered superior to alternative trajectories (Bell, 1997).
• Acceptable futures, representing minimum tolerable conditions in governance planning (OECD, 2019).
• Strategic futures, which actors attempt to shape through deliberate policy or investment (Wilkinson & Kupers, 2013).
• Sustainable futures, aligned with ecological resilience and long-term societal stability (World Economic Forum, 2026).
• Regenerative futures, in which systems restore ecological and social resources rather than merely sustaining them (Raworth, 2017).

Normative futures therefore provide the directional orientation for strategic decision-making.

Forecast Futures: Trend-Driven Expectations

Forecast futures describe what is expected to occur if current trends continue. These futures are typically derived from predictive models, statistical analysis, and trend extrapolation (Makridakis et al., 2020).

Forecasting helps establish a baseline scenario by revealing the trajectory systems may follow in the absence of significant disruption or intervention.

Common categories include:

• Expected futures, representing outcomes decision-makers anticipate (Makridakis et al., 2020).
• Projected futures, generated through modeling or simulations (OECD, 2019).
• Forecast futures, derived from predictive analytics and trend analysis (Makridakis et al., 2020).
• Baseline futures, representing continuation of current trajectories (Wilkinson & Kupers, 2013).
• Business-as-usual futures, assuming no major policy interventions occur (World Economic Forum, 2026).
• Probable futures, representing the most likely developments given existing conditions (Voros, 2003).

These forecast-based scenarios provide the reference point against which alternative futures can be explored.

Exploratory Futures: Uncertainty-Driven Possibilities

Exploratory futures extend beyond forecasting by examining uncertainty and structural change. Scenario planning research emphasizes that organizations must consider multiple plausible pathways rather than relying on a single predicted future (Schoemaker, 1995).

Examples include:

• Plausible futures, consistent with current knowledge about systems (Voros, 2003).
• Alternative futures, structured scenarios that diverge from baseline expectations (Schoemaker, 1995).
• Possible futures, representing outcomes that could occur within physical and social constraints (Bell, 1997).
• Emergent futures, arising from complex system interactions and feedback loops (Meadows, 2008).
• Transitional futures, intermediate states during systemic transformation (Geels, 2002).
• Transformational futures, involving fundamental changes in socio-technical systems (Geels, 2002).
• Disruptive futures, where innovation reshapes markets or institutions (Christensen, 1997).
• Regime-shift futures, where institutional or technological systems undergo structural transformation (Geels, 2002).

Exploratory scenarios therefore enable organizations to anticipate uncertainty and systemic change.

Agency Futures: Governance and Influence

The agency dimension focuses on who has the power to shape the future. Futures are not determined solely by technological or economic trends; they are also influenced by policy decisions, governance systems, and institutional choices (Wilkinson & Kupers, 2013).

Agency-based futures include:

• Managed futures, shaped through deliberate policy or strategic action (OECD, 2019).
• Unmanaged futures, emerging without coordinated governance (Miller, 2018).
• Contested futures, where different actors promote competing visions (Wilkinson & Kupers, 2013).
• Negotiated futures, emerging from political compromise or institutional negotiation (OECD, 2019).
• Policy-driven futures, where regulation strongly influences outcomes (World Economic Forum, 2026).

This dimension highlights the importance of governance and institutional capacity in shaping future trajectories.

System Dynamics Futures: Complex System Behavior

Many futures emerge from complex system dynamics, including feedback loops, nonlinear interactions, and path dependency (Meadows, 2008; Sterman, 2000).

Examples include:

• Adaptive futures, where systems evolve in response to changing conditions (Sterman, 2000).
• Path-dependent futures, shaped by historical decisions and infrastructures (Arthur, 1989).
• Lock-in futures, where technological choices become difficult to reverse (Arthur, 1989).
• Cascading futures, where one event triggers systemic chain reactions (Helbing, 2013).
• Tipping-point futures, where crossing thresholds leads to rapid transformation (Lenton et al., 2008).
• Phase-transition futures, where systems shift between structural states (Sterman, 2000).

Understanding these dynamics is particularly important when analyzing large socio-technical systems such as climate systems, energy infrastructures, and digital networks.

Shock and Disruption Futures

Foresight frameworks also incorporate low-probability but high-impact events capable of disrupting expected trajectories (Taleb, 2007).

Examples include:

• Wildcards, rare events with significant consequences (Petersen et al., 1999).
• Grey rhinos, highly probable risks that are often ignored (Wucker, 2016).
• Slow-burn risks, threats that accumulate gradually over time (OECD, 2019).
• Creeping crises, slowly escalating systemic disruptions (Boin et al., 2020).
• Black swans, rare and unpredictable events with transformative impacts (Taleb, 2007).

These categories help decision-makers prepare for unexpected disruptions and systemic shocks.

Negative Outcome Futures: Risk-Driven Scenarios

Risk-driven futures describe undesirable trajectories that actors seek to prevent (OECD, 2019).

Examples include:

• Undesirable futures, involving social or environmental harm.
• Adverse futures, characterized by systemic deterioration.
• Degenerative futures, involving gradual institutional or ecological decline.
• Crisis futures, requiring emergency intervention.
• Collapse futures, where institutions or infrastructures fail (Diamond, 2005).
• Catastrophic futures, involving large-scale systemic breakdown (World Economic Forum, 2026).

These categories help policymakers evaluate systemic vulnerabilities and resilience strategies.

Boundary Futures: Limits of the Future Space

Finally, boundary futures define the limits of what can be imagined or physically realized.

Two categories are typically distinguished:

• Impossible futures, which violate physical or logical constraints (Bell, 1997).
• Inconceivable futures, which lie outside current conceptual frameworks but may become imaginable as knowledge evolves (Voros, 2003).

Boundary futures therefore mark the edges of the scenario space explored in foresight analysis.

Conclusion

The professional foresight taxonomy demonstrates that the future should not be treated as a single predicted outcome but rather as a multidimensional landscape of possibilities. By combining normative visions, trend-based forecasts, exploratory scenarios, governance considerations, system dynamics, disruptive shocks, and risk-based outcomes, foresight frameworks allow organizations to design strategies that remain robust under uncertainty.

Such approaches are increasingly used in strategic foresight programs and anticipatory governance initiatives worldwide to support long-term decision-making in an era of complex global risks and systemic change.

References

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Bell, W. (1997). Foundations of futures studies: Human science for a new era. Transaction Publishers.

Boin, A., Ekengren, M., & Rhinard, M. (2020). Hiding in plain sight: Conceptualizing the creeping crisis. Risk, Hazards & Crisis in Public Policy, 11(2), 116–138. https://doi.org/10.1002/rhc3.12193

Christensen, C. M. (1997). The innovator’s dilemma. Harvard Business School Press.

Diamond, J. (2005). Collapse: How societies choose to fail or succeed. Viking.

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Lenton, T. M., Held, H., Kriegler, E., Hall, J., Lucht, W., Rahmstorf, S., & Schellnhuber, H. J. (2008). Tipping elements in the Earth’s climate system. PNAS, 105(6), 1786–1793. https://doi.org/10.1073/pnas.0705414105

Makridakis, S., Spiliotis, E., & Assimakopoulos, V. (2020). The M4 competition: 100,000 time series and 61 forecasting methods. International Journal of Forecasting, 36(1), 54–74.

Meadows, D. H. (2008). Thinking in systems. Chelsea Green Publishing.

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Petersen, J., Steinmüller, K., & Steinmüller, A. (1999). Wild cards: A tool for futures thinking. Futures, 31(2), 117–129.

Raworth, K. (2017). Doughnut economics. Chelsea Green Publishing.

Schoemaker, P. J. H. (1995). Scenario planning: A tool for strategic thinking. MIT Sloan Management Review, 36(2), 25–40.

Sterman, J. D. (2000). Business dynamics: Systems thinking and modeling for a complex world. McGraw-Hill.

Taleb, N. N. (2007). The black swan: The impact of the highly improbable. Random House.

Voros, J. (2003). A generic foresight process framework. Foresight, 5(3), 10–21. https://doi.org/10.1108/14636680310698379

World Economic Forum. (2026). The global risks report 2026 (21st ed.). https://www.weforum.org/publications/global-risks-report-2026/ World Economic Forum+1

Wucker, M. (2016). The gray rhino: How to recognize and act on the obvious dangers we ignore. St. Martin’s Press.

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