The war in Iran has upended the global economic outlook overnight. Oil supply routes have been shut down and prices have surged. Every central bank, finance ministry, and economic forecaster is scrambling to answer the same question: What happens next?

But before rushing to produce new numbers, we need to know what kind of uncertainty we are facing. Are our standard forecasting tools up to the task, or do they offer false comfort?

From the mid-1980s until the pandemic, the global economy was remarkably stable. Inflation was low and predictable. Central banks had credible targets. Supply chains functioned smoothly. Geopolitical arrangements, while never static, evolved gradually. The mechanisms by which disruptions propagated through the economy remained roughly constant.

In that world, the standard approach to forecasting worked well: produce a single baseline projection and wrap it in a fan chart showing the range of uncertainty around it. The fan chart said, in effect: we know how the economy works, we just don’t know the exact numbers. The future would resemble the past, give or take some noise.

That world is gone.

The pandemic disrupted supply chains in ways no fixed model anticipated. The post-pandemic inflation surge was the most significant forecasting failure in central banking in decades. And now, the war in the Middle East is wreaking havoc with the energy sector.

Each of these events is not just a large disturbance hitting an otherwise stable system. They herald a change in how the system works.

The economist Frank Knight grasped the implications of this distinction in 1921. Risk, he argued, is uncertainty that you can quantify: while you can’t predict the outcome, you know the range of possibilities and can assign probabilities to them. True uncertainty — nowadays called Knightian uncertainty — is different: you cannot assign it a probability, because the situation itself may be changing in ways you have not seen before. The economy undergoes structural shifts that, because they are genuinely novel, rule out using the past to estimate future outcomes.

The Iran war is a case in point. We knew a Middle East conflict was possible, and previous oil crises and energy-price surges offer some guidance about the consequences. But the specific combination of actors, escalation dynamics, and consequences for global energy markets has no precedent — and the differences from previous episodes are precisely what matters most.

A fan chart cannot capture this. It assumes that future uncertainty can be quantified by past forecast errors. It says: I know the story, but not the numbers. But when the story itself is in question, a wider fan chart is not the answer.

New approach

If we cannot reduce the uncertainty to a single probability distribution, what should we do instead? The answer is not to abandon economic forecasting and seek an oracle. It is to change the format. Roman Frydman of New York University and I recently proposed a rigorous approach that aims to guide forecasting under Knightian uncertainty.

Instead of one baseline with a fan chart, forecasters should present a small set of scenarios — not as hedging, not as decoration, but as structured reasoning about genuinely different futures. Each scenario should include a narrative explaining the economic logic, a conditional forecast showing what follows if that scenario plays out, and a specification of what incoming data would shift the assessment toward one scenario or another. Designing good scenarios is the hard part — it requires practical economic judgment, not just statistical technique.

For the Iran war, this could mean two or three scenarios. In a containment scenario, for example, the conflict remains localised, oil supply disruption is temporary, and energy prices return toward pre-war levels within months. In an escalation scenario, the war spreads, energy infrastructure is damaged, oil prices settle at permanently higher levels, and second-round effects on wages and inflation expectations change the policy calculus — or even trigger a fundamental restructuring of global energy trade.

Each of these implies different trajectories for inflation, interest rates, and growth dynamics. No single baseline with a fan chart can capture these qualitatively different futures. Scenarios can.

Some of the world’s major central banks are already moving in this direction, driven by the hard lesson of the post-pandemic inflation surge, which blew through every fan chart they published. Ben Bernanke’s landmark review of the Bank of England recommended eliminating fan charts and publishing scenarios instead.

In March, the European Central Bank did just that, replacing its fan charts with three alternative scenarios and explicitly stating that its standard probabilistic tools would not “provide a reliable indication of the high uncertainty.” As ECB President Christine Lagarde put it: “We find ourselves yet again in a different world, whose contours are not yet clear.”

The ECB is not alone. Sweden’s Riksbank now publishes alternative scenarios without attaching probabilities. The Bank of Canada has gone further, dropping its baseline forecast altogether in early 2025, when no single projection was credible.

The tools for forecasting under Knightian uncertainty exist. Scenarios are not a second-best substitute for a single forecast with a fan chart. When the economy is undergoing structural change, they are the intellectually honest format — able to represent what we can and cannot know about the future.

In a changing world, credibility does not come from being right all the time — no one will be. It comes from being transparent about the possible futures you consider relevant, adjusting your views as new data arrives, and doing what you said you would do when a specific scenario materialises.

While some central banks have led the way, much of the forecasting profession — and the economic theory that underpins it — still defaults to single baselines that treat the future as a probabilistic replica of the past. But when the structure of the economy changes in unforeseeable ways, forecasters must acknowledge that many futures are possible.

The right response to Knightian uncertainty is not wider fan charts, but scenarios that take seriously that we can know only that the world has changed, not how.

The writer is Co-Founder and Director of the INET Center on Knightian Uncertainty. Copyright: Project Syndicate, 2026.

Published on April 10, 2026