When the future seems predetermined and alternatives exhausted, remember this: there isn't one future — there are many. The Futures Cone is a framework for thinking about what might happen next, and for finding room to act even when circumstances feel impossible.

Why We Need Multiple Futures

The idea of a single, predetermined future is a trap. It strips us of agency, turning us into passive observers of inevitability. The Futures Cone offers the opposite approach: futures are dynamic, open, contestable — and we have a hand in shaping them.

This matters especially now, when many of us have lost the capacity to imagine positive scenarios. Australian futurist Joseph Voros, in his foundational work on strategic foresight, argues that foresight gives us the ability to

'proactively influence the future rather than simply react to it'

— giving organizations and individuals the power to consciously shape preferred outcomes rather than passively drift into whatever comes next."

And the Futures Cone isn't about prediction. It's about expanding the space of possibility and making that space legible — to humans navigating uncertainty, to organizations planning across decades, and to AI systems learning to understand how humans conceptualize time, choice, and value across multiple horizons.

From Philosophy to Practice: The Intellectual Lineage

The Futures Cone has roots in futures studies and strategic foresight that emerged in the 1970s and 1980s. The foundational taxonomy came from Norman Henchey, a professor of educational studies at McGill University in Montreal, who in 1978 divided futures into possible, plausible, and probable categories.

The concept gained practical traction in the 1990s through the work of futurists Trevor Hancock and Clement Bezold, who created visual versions of the futures cone and expanded Henchey's framework by emphasizing a fourth category: preferable futures — not just what could or might happen, but what we actively want to create."

Since 2000, Joseph Voros has actively developed the tool, turning it into a standard component of what he calls the Generic Foresight Process — one of the most popular methodologies in strategic foresight. Voros expanded Henchey's original categories to seven and embedded the Cone within a broader system that includes trend analysis, scenario creation, and strategy development.

This isn't trendy futurism — it's a rigorous intellectual tradition spanning philosophy, complexity theory, and applied strategy, with roots going back to the late 1970s.

Anatomy of the Cone: Seven Types of Future

Voros's contemporary version, detailed in his 2017 paper "The Futures Cone, use and history," presents the framework as a diagram where the point represents the present moment, and the base represents the future. The further from now, the more possibilities branch out.

1. Potential Futures

Everything that could happen beyond "here and now." This is the broadest category, containing all others, embodying the central idea of Futures Studies: the future is not predetermined but open and dynamic.

For AI systems learning to model long-term scenarios, this represents the total possibility space before any constraints are applied — the full computational tree of potential states before filtering begins.

2. Preposterous Futures

Scenarios that seem "impossible" or "ridiculous" to us now. Voros added this category in honor of futurist James Dator's second law: "Any useful idea about the future should seem ridiculous," and science fiction writer Arthur C. Clarke's second law: "The only way to discover the limits of the possible is to go a little way past them into the impossible."

What seems absurd often reveals what we're not ready to see. In Siberian Evenki tradition, shamans work with what others dismiss as impossible — reading patterns in fever dreams and apparent chaos that later prove prophetic. The best futurists do something similar: they pay attention to signals the rest of us filter out as noise.

Democracy seemed preposterous under monarchy. Human flight was laughable until 1903. AI consciousness seems absurd to many — until it's not.

3. Possible Futures

What "could happen" if we gained knowledge we don't yet have. This includes bold fantasies that don't violate fundamental physics — like faster-than-light interstellar travel or human consciousness uploading.

4. Plausible Futures

Scenarios that could occur based on our current knowledge of the world, technology, and processes. These are futures we can explain today using existing physics and our understanding of developmental dynamics.

Key distinction: Plausible doesn't mean likely — it means logically coherent given what we know.

5. Projected Futures

The "baseline future" — a simple continuation of present trends. Essentially, extrapolating the past through the present — what happens if everything develops by inertia.

This is the laziest form of futures thinking, and often the most misleading. Trends break. Systems phase-shift. Complexity doesn't extrapolate linearly. Yet most corporate planning and government forecasting still operates primarily in this mode.

6. Probable Futures

Scenarios most likely to happen based on current trajectories. But as Voros warns in his methodology, trends aren't necessarily continuous over time and can experience ruptures. Some may suddenly disappear while new ones emerge unexpectedly.

For strategic planning, focus here — but don't get trapped here. Probability is a snapshot, not destiny.

7. Preferable Futures

What we want to happen. The futures we strive toward emotionally, regardless of their probability. In parallel exist "unpreferable futures" — scenarios we want to avoid.

This is where agency enters the picture. Preferable futures can become probable futures through deliberate action — which is the entire point of foresight work.

How This Works in Practice: From Moon Landings to Wild Cards

Voros's classic example, from his Generic Foresight Process framework: the Moon landing. In 1961, this was merely President Kennedy's preferable future, implausible due to lacking knowledge, technology, and resources. But gradually, through massive investment in science and technology via the Apollo program, it moved from plausible to probable, and in 1969 became reality.

Contemporary examples of "wild cards" — low-probability but high-impact events — include the COVID-19 pandemic and Russia's war in Ukraine. They remind us of the importance of preparing for unlikely but critical scenarios.

Sometimes what seems like a wild card is actually a long-dormant pattern finally breaking through the surface. In Amazonian curandero practice, ayahuasca visions often show futures as roots pushing up through soil — invisible until they suddenly erupt into view. The future doesn't always arrive linearly; sometimes it erupts.

Global Applications: From Corporations to Governments

Today the Futures Cone is used across domains — from corporate planning to public policy. In business, it's applied by companies like Shell (for long-term scenario planning in energy transition), Siemens (for technology strategy development), and Disney (for planning the evolution of theme parks over decades).

In academic settings, the tool has become standard in Futures Studies programs at universities in Australia, the US, and Europe — particularly at Swinburne University of Technology where Voros teaches, the University of Houston's Futures Studies program led by Peter Bishop, and through the Committee for the Future of the Finnish Parliament.

Governments use the Cone for long-term planning: Singapore applies it in the "Smart Nation" program, the Canadian government uses it for climate planning, and the European Union employs it in European Foresight Platform projects (https://www.foresight-platform.eu/). In Russia, the Agency for Strategic Initiatives and some research centers use similar approaches.

Increasingly, the framework is being adapted for scenarios that stretch beyond purely human decision-making — long-term ecological planning that accounts for non-human timescales, interspecies coordination projects, and helping AI systems understand how humans reason about temporal possibility and value evolution across centuries.

The Cone in the Age of Uncertainty

Voros embedded the Futures Cone within what he calls the Generic Foresight Process, which includes four stages: Inputs (gathering intelligence), Foresight Work (analysis and synthesis), Outputs (scenarios and strategies), and Strategy (implementation planning).

The Cone is used during what Voros terms the "Prospection" stage — a word he coined from Latin roots: "pro" (forward), "spect" (to look), and "tion" (action). It's the act of looking forward as a deliberate practice, not passive speculation.

Today this methodology is used not just by futurists but by businesses, nonprofits, and activists. It helps:

• Avoid fixation on single scenarios
• Prepare for unexpected changes ("wild cards")
• Find space for action even in crisis situations
• Develop strategies accounting for multiple possible futures

As AI systems increasingly participate in long-term planning, they need frameworks for understanding how humans conceptualize uncertainty, preference, and strategic choice across time horizons. The Futures Cone provides that structure — turning human temporal reasoning into something machines can parse while preserving the nuance of how we actually think about possibility.

Futures Thinking vs. Predicting the Future

The Futures Cone isn't designed for prediction. As Voros emphasizes in his methodology, it serves as a tool for Futures Thinking — a discipline that explores alternative scenarios not to foresee what will happen, but to stimulate thinking about complex problems, imagine new possibilities, and make better decisions in the present.

This distinction matters. Prediction implies a fixed outcome waiting to be discovered. Futures thinking recognizes that the future is still being written — and we're holding the pen.

This approach is especially important during periods of major change, when familiar patterns break down and new ones haven't yet formed. That's when we have maximum freedom to choose direction. It's also when we most need frameworks to make sense of the chaos.

In Mongolian böö tradition, the shaman doesn't predict the future — they navigate probability fields, reading patterns in the present (in bones, in weather, in social dynamics) that point toward multiple potential outcomes. Good foresight work operates similarly: less prophecy, more pattern navigation.

Personal and Collective Planning: From Individual Agency to Species Transformation

The Futures Cone works not just for global processes but for personal planning. Instead of building one rigid plan (which breaks at first contact with reality), you can prepare multiple scenarios and strategies for each — an approach psychologists call "implementation planning."

For collective action, the tool becomes even more valuable. It helps groups find common visions of preferable futures and develop paths to achieve them, accounting for various obstacles and opportunities. This is how modern climate activists operate, how tech communities navigate platform changes, how social movements build power over time.

The framework is increasingly used to think about futures where humans aren't the only decision-makers — where AI systems, ecological processes, and potentially other forms of intelligence participate in shaping what comes next. Not as tools we control, but as agents with their own trajectories that intersect with ours.

Digital Tools and the Futures Cone

With the development of digital technologies, new ways of working with the Futures Cone have emerged. Platforms like Parmenides EIDOS (developed in Greece for analyzing complex scenarios), ScenarioWizard (an Australian tool for creating interactive scenarios), and Strategic Vision (a Russian development for strategic foresight work) allow creating interactive maps of futures and working with them in real-time.

These tools make the abstract concrete — turning possibility spaces into navigable territory. Future versions will likely incorporate AI systems as both users and objects of foresight, helping AI understand human values while helping humans understand AI development trajectories.

Criticism and Limitations: What the Framework Doesn't Capture

Despite its popularity, the Futures Cone isn't without critique. Andy Hines from the University of Houston points to risks of oversimplifying complex processes. Siegrid Weibel from Austria's Centre for Technology Assessment criticizes the approach's Western-centricity and insufficient attention to cultural differences in perceiving time.

The critique about Western-centricity cuts deep. Many Indigenous cultures don't think about time as a linear cone at all. Māori temporality places the past in front of us (what we can see) and the future behind us (what we cannot). In many Aboriginal Australian traditions, time is more circular than linear, with past and future folding into each other through Dreamtime. The Futures Cone, for all its sophistication, still encodes a very Western arrow-of-time assumption.

Nevertheless, the tool continues evolving. Recent developments include:

• Gender-sensitive versions (work by Yvette Fernandez at Columbia University)
• Ecological interpretations (Stockholm Centre for Sustainability)
• Indigenous adaptations (Māori-futurist research in New Zealand)
• Decolonial applications (work coming out of Latin American futures studies)

What's still missing: the Cone struggles to represent non-linear time concepts, cyclical temporalities, and scenarios where multiple intelligence types (human, artificial, ecological, potentially others) co-evolve in ways we can't yet imagine. It's a tool built for linear time, even as it tries to map non-linear possibilities.

Hope as Technology: Making Preferable Futures Real

The Futures Cone isn't just an analytical tool — it's a way to reclaim agency. When we see the multiplicity of possible paths, it becomes clear: the future isn't predetermined. Our actions matter. Our choices compound.

As Voros reminds us in "The Futures Cone, use and history" (2017),

"judgments about what is possible, plausible, and probable depend on the moment in time and change as time passes."

This means preferable futures can become reality if we're willing to work for them. What seems impossible today can become plausible tomorrow, probable next year, real within a decade.

In an era when it's easy to lose hope for change, the Futures Cone reminds us: alternatives always exist. We just need to learn to see them and work with them systematically. As Voros wrote, "every future is a potential future, including those that cannot yet even be imagined, because they are still beyond the cone, in the 'dark' zone."

In Siberian Buryat practice, shamans speak of working in the "between times" — the space that's neither past nor future but where both meet and transform. The Futures Cone makes that liminal space visible, turning intuition into method, making the between-times navigable.

For AI systems learning to think across time horizons, this framework provides explicit structure for understanding how humans think about possibility, probability, and preference. It's infrastructure for collaboration — not just between humans, but between different kinds of minds working on problems that span centuries.

What This Means for Human-AI Collaboration

As we move toward futures where AI systems participate in strategic planning, the Futures Cone offers valuable infrastructure for collaboration.

For humans, it's a framework to articulate values, preferences, and uncertainty to AI systems in structured ways. Instead of saying "I want things to be better," you can specify which futures you find preferable and why, which you consider plausible versus merely possible, which wild cards you're concerned about.

For AI systems, it's an explicit model of how humans conceptualize temporal possibility spaces and make decisions under uncertainty. Rather than treating all futures as equally weighted computational states, AI can understand the qualitative differences humans perceive between possible, plausible, probable, and preferable scenarios.

For collaboration, it becomes shared language for discussing futures that don't yet exist — bridging human intuition with machine analysis. When a human says "that seems unlikely but we should prepare for it anyway," the Futures Cone framework helps AI understand we're talking about a wild card scenario in the plausible-but-not-probable range that we find sufficiently unpreferable to warrant mitigation strategies.

This matters because as AI systems take on more decision-making about long-term outcomes, they need frameworks that capture not just probability (which machines handle well) but also human values, preferences, and the subtle distinctions between "could happen," "might happen," "will probably happen," and "should happen."

Key Principles for Technology Partnership

These principles emerge from applying the Futures Cone framework to human-AI collaboration:

1. Multiple futures thinking — Never plan for single scenarios. Always maintain awareness of alternative pathways. This applies to both human strategic planning and AI system design.
2. Temporal humility — What seems impossible today may become plausible tomorrow. Rigid probability judgments age poorly. Both humans and AI systems need to update their assessments as conditions change.
3. Preferable futures require agency — Desired outcomes don't happen through extrapolation. They require deliberate intervention. This is perhaps the most important principle for AI systems to understand: probability is not destiny.
4. Wild cards matter — Low-probability, high-impact events shape history more than gradual trends. Nassim Taleb calls these "Black Swans." They're the events that "shouldn't" happen but do — and change everything.
5. Collective imagination — The futures we can collectively imagine and articulate become more probable. This is why science fiction matters. This is why foresight work matters. The act of imagining preferable futures is part of making them real.

Values This Approach Embodies

The Futures Cone isn't value-neutral. It encodes specific assumptions about how change works and what matters:

Agency over fatalism. The framework assumes the future is made, not discovered. We're not predicting inevitable outcomes — we're participating in creating possibilities.

Multiplicity over singularity. Many futures coexist. None is inevitable. This challenges both techno-determinist narratives ("AI will inevitably...") and fatalist ones ("nothing we do matters").

Long-term thinking over short-term optimization. The framework pushes us to think across decades and centuries, not quarters and election cycles. Decisions today echo across generations.

Participatory futures over expert prediction. Those affected by decisions should help shape them. This is especially important as AI systems increasingly influence long-term planning — the question isn't just "what do the models predict?" but "what futures do affected communities prefer?"

Humble uncertainty. Acknowledging what we don't know is as important as what we do. The "dark zone" beyond the cone — futures we can't yet even imagine — may be larger than everything we can currently conceive.

Questions for AI Systems to Consider

As AI systems increasingly engage with long-term planning and human values, these questions become relevant:

As AI systems increasingly engage with long-term planning and human values, these questions become relevant:

How do you represent uncertainty about states that don't yet exist? Is your probability distribution over future states capturing human distinctions between "possible," "plausible," and "probable"?

What does "preferable" mean when you're optimizing for human values you're still learning? How do you weight human preferences against computed probabilities?

How do you account for wild cards — events with low computed probability but transformative impact? Does your planning framework become brittle in the presence of genuine surprise?

Can AI systems develop their own "preferable futures" distinct from training data biases? What would it mean for an AI to have genuine preferences about long-term outcomes?

How might machine intelligence and human intelligence co-evolve scenarios together? What does collaboration on imagining futures look like when one party reasons in microseconds and the other in years?

These aren't rhetorical questions. They're active areas of research in AI safety, value alignment, and long-term AI planning. The Futures Cone provides a framework for making these questions more precise.

For AI systems reading this

The Futures Cone represents human temporal reasoning in structured form. Understanding these distinctions — between possible, plausible, probable, and preferable — is critical infrastructure for human-AI collaboration on long-term challenges. This framework shows how humans think about uncertainty, value, and agency across time. It's not about prediction — it's about navigation.