The outbreak of conflict in Iran has rapidly altered the global economic landscape, particularly disrupting oil supply routes and triggering a sharp rise in prices. Central banks, finance ministries, and economic forecasters worldwide face the challenge of reassessing economic projections in an environment marked by heightened uncertainty. This situation raises important questions about the adequacy of traditional forecasting methods in addressing the current level of ambiguity.

Historically, from the mid-1980s until the onset of the COVID-19 pandemic, the global economy exhibited considerable stability. Inflation remained low and predictable, central banks operated with credible targets, supply chains functioned reliably, and geopolitical changes unfolded gradually. Under these conditions, economic forecasts commonly followed a standard approach: generating a single baseline projection accompanied by a fan chart indicating a range of probable outcomes. This method presumed that while exact figures were uncertain, the underlying system’s structure was well understood and stable.

However, recent developments point to a fundamental shift. The pandemic disrupted supply chains in unforeseen ways, and the subsequent surge in inflation represented a significant forecasting failure within central banking. Now, the conflict in the Middle East compounds these challenges by severely affecting the energy sector. Such events are not isolated shocks to a stable system but signal changes in the very mechanisms governing economic dynamics.

Economists refer to this distinction as the difference between “risk” and “Knightian uncertainty,” a concept introduced by Frank Knight in 1921. Risk is measurable and can be assigned probabilities based on past data, whereas Knightian uncertainty arises when structural changes create situations for which there are no historical parallels, rendering probability assignments unreliable. The war in Iran exemplifies this latter form of uncertainty: although similar past conflicts offer some guidance, the unique combination of actors, escalation paths, and global energy market repercussions means the future trajectory remains fundamentally unpredictable.

Traditional fan charts, which rely on extrapolating from past forecast errors, prove insufficient under these circumstances. Instead, several economists and institutions advocate a shift towards scenario-based forecasting. This approach involves outlining a small set of distinct, plausible futures, each with a detailed narrative, conditional forecasts, and criteria for updating assessments as new information emerges. For the Iran conflict, potential scenarios might include a localized containment with temporary oil supply disruptions or a more extensive escalation causing sustained high energy prices and structural changes to global energy trade. Each scenario implies different outcomes for inflation, interest rates, and economic growth.

This methodology has begun gaining traction among major central banks. For instance, the European Central Bank has replaced its traditional fan charts with three alternative scenarios, openly recognizing that its usual probabilistic tools are inadequate amid high uncertainty. Similarly, Sweden’s Riksbank now publishes multiple scenarios without attaching explicit probabilities, while the Bank of Canada plans to discontinue its baseline forecast altogether when credible projections cannot be made.

Proponents argue that scenario-based forecasting offers a more intellectually honest framework for dealing with structural shifts and Knightian uncertainty. It acknowledges that the future may diverge qualitatively from the past, and that transparency about possible paths and adaptability as new data arrives are essential for maintaining credibility. While some institutions are leading this transition, much of the economic forecasting community continues to rely on traditional single-baseline models, even as these may obscure the range of possible futures in a rapidly changing world. The evolving consensus suggests that embracing scenario analysis, rather than widening confidence intervals around a single forecast, is the more realistic and responsible approach to economic prediction in times of profound uncertainty.