Carolyn Geason-Beissel/MIT SMR | Getty Images
“What’s the best-case scenario that you can logically describe?” As an academic and adviser who has spent more than 30 years forecasting future intersections of people and the political economy through the lens of technological change, I’m often asked that question. My answer is always the same: Best-case scenarios do not exist. Neither do worst-case scenarios. Human-powered futures are always a mix of misunderstood and unimagined downsides and upsides. What’s different from one era to another? The range and breadth of uncertainty that decision makers must navigate as they try to amplify the upsides and reduce the downsides of change.
That’s important because politicians, CEOs, financial markets, and everyday people have one thing in common: They abhor radical uncertainty. It makes their purpose and mission seem impossible.
Radical uncertainty is entirely different from conventional risk, which positively powers markets and creates opportunities for leaders to make decisions with potentially outsize political and economic payoffs. Risk has boundaries and relevant data; it can be priced and hedged because it represents a partially knowable probability distribution among outcomes. That’s why the saying “no risk, no return” makes sense and why we comfortably talk about and understand “risk-on” and “risk-off” behavior in markets and politics.
Radical uncertainty has none of those positive characteristics. When events move far outside the boundaries of what we know from the past, and when the shape of the probability distribution can’t be mapped, business and political systems tend toward paralysis. People become deeply anxious. Markets gyrate. C-suite confidence corrodes and decision-making freezes up.
There is clear evidence for each of those effects right now in the United States’ political economy. Consumer confidence has been volatile in 2025, including some lows not seen since the 2008 financial crisis. During the second week of April, the S&P 500 rose 9.5% in one day and dropped 3.5% the following day, transiting from bear to bull market in less than two months in the spring. Walmart declined to provide earnings guidance for the first quarter, and United Airlines offered two separate and entirely different profit forecasts for the second quarter.
The common thread is radical uncertainty engineered by the Trump administration’s notable first six months. Whether you support or oppose the Trump agenda, the fact is that no American administration of at least the past century, and possibly ever, has created such radical uncertainty around such a broad swath of the American political economy, even within the first 100 days. That includes President Franklin D. Roosevelt’s first 100 days, as remarkable as they seemed in 1933. Again, that is no judgment on the agenda but simply an observation of a reality experienced by supporters and opponents of the new administration alike.
Two Key Scenario-Planning Lessons
I’ve spent much of my academic career studying what happens in moments like these — times of high-stress decision-making, when people and organizations are functioning (often badly) under radical uncertainty. It’s been a natural extension of that research to advise companies and governments about how to better navigate these moments and deploy and refine tools and methods that can help them make better choices. There are two important lessons about scenario planning to share from that experience.
1. Beware Overindexing on Short-Term Signals
First, certain identifiable and dysfunctional patterns of information processing and decision-making are painfully common. The most visible is overindexing on very short-term signals, whether it be a single day’s news headlines or a social media post. People tend to chase what looks (for a short time) like a high-salience signal or interpretation, which leads them to oscillate too fast and too far between optimism and pessimism.
Instead of incorporating a new piece of evidence as simply the next data point in a longer series (in technical language, Bayesian updating), the person gives in to the anxiety of radical uncertainty — and magnifies the meaning of what might have been just a blip, a quip, or the musings of a particular pundit.
If you watch your own mind over the course of a day, you’ll probably see exactly this tendency. And if you look a bit deeper, you’ll see that overindexing on one signal engages both “hot” and “cold” elements of your experience — emotions and cognition. If you were to make decisions on this basis right now, you’d almost certainly make bad decisions that wouldn’t stand the test of much time. (If you find it hard to see this tendency in yourself at first, look for it in others — say, your collaborators or competitors. Then look back in the mirror one more time.)
This kind of oscillation or ping-ponging isn’t just bad for decision-making; it is utterly exhausting to both individuals and groups.
Another dysfunctional tendency often creeps in to compensate when people feel overwhelmed: the search for false certainty that may stop the exhausting struggle. An article with a bold assertion or advice from a consultant with a strong point of view, for instance, may get passed around as “the answer we’ve been looking for,” given people’s desire to anchor on a point prediction about what it all means.
Within your team, you can almost hear the sigh of relief: What were confusing and discrepant signals yesterday now suddenly look like a puzzle with all of the pieces in place. Decision makers feel that they can work from that anchor point to define that supposed best-practice answer that everyone is hungry for and then execute on it. Contingency plans for being wrong are sometimes part of the answer, but surprisingly often, companies create no such plans or largely ignore them. Newly emerging evidence is interpreted to be consistent with the point prediction rather than evaluated on its own terms.
People and organizations sometimes place enormous bets on these point predictions, as President Jimmy Carter did when he ordered the Iran hostage rescue effort to proceed without meaningful plans for what to do if it went wrong. It might appear courageous for individuals or smaller companies to bet the farm in this way, and it can certainly feel brave in the moment. But it’s not strategic, particularly for large companies: The chances of getting it wrong are vastly greater than the chances of getting it right.
2. Rethink Preparation
The second lesson I’ve learned is that there is a way to do better. Scenario thinking starts from a different premise: that we cannot predict the future and therefore shouldn’t try. It was pioneered at Royal Dutch Shell in the 1970s and developed for use outside the energy sector by my colleagues and me at Global Business Network and the Monitor Group in subsequent decades. Particularly during periods of high uncertainty, and by necessity under radical uncertainty, the guiding principle for strategic decision-making needs to be preparation, not prediction. The goal is robust strategy development that is designed to perform regardless of where reality lands on the map.
The key to scenario thinking is to render that landscape map of what is possible expansively and broadly enough to incorporate what is actually possible rather than what a person or an organization wishes were possible because it would be easier to handle. That means stretching the boundaries of the critical uncertainties — those factors that are at once both the most important and the most uncertain — a little bit further out than seems plausible now.
To make sense of today’s U.S. political economy, I recently built scenarios looking two years ahead that incorporate a Trump economy that could shrink by more than 6% (nearly Great Depression magnitude) or grow by the same percentage — greater than the best peacetime years in the 20th century.
My scenarios combine this range of economic possibilities with a second critical uncertainty, which is where the countervailing forces that support and oppose the administration’s agenda will arise and exert power in public and policy debate. On one side of the continuum, we may see a revitalization of traditional media and political institutions — The New York Times, the Democratic Party, Fox News. On the other end of the continuum, we may see the continued rise to dominance of new power centers that shape debate — podcasts, YouTube influencers, Discord server groups that seamlessly meld entertainment, information, commerce, and mobilization to action. For example, “cutefluencers” (social influencers who gain credence by trying to be cute) are a real force right now; so, what does it look like if they become even more influential on public and policy debate than any reasonable person might currently imagine possible?
Considering these two critical uncertainties, this scenario model yields four distinct futures that, as a set, map a possibility space for the U.S. political economy out to the 2026 midterm elections. Those futures range from a 1970s redux world but with profound American decline, deep stagflation, and a punishing trans-Pacific cold war that might not stay cold for the duration; to a techno-libertarian-rationalist political synthesis that leaves both Republicans and Democrats behind in an AI- and data-powered push for growth that has little to no room for ideology and culture wars.
Is this just undisciplined imagination or fantastical fiction? I’ve produced scenarios for organizations that needed to look much further out — 5, 10, or even 25 years into the future — and I’ve learned one simple lesson about the rate and magnitude of change: It’s generally faster and greater than the decision makers at “go time” expect.
You can’t and shouldn’t try to plan against science fiction. But the tendency of people and organizations to pull back toward the familiar and label what are plausible outcomes as “too far out” is powerful — and bad.
Build an Expansive Scenario Map
Good scenarios press back, gently but firmly, against the tendency to turn away from the unfamiliar. At moments of radical uncertainty like the present, the need to do so should come into useful contact with visible reality. In the past six months, the Trump administration has expanded the Overton window of what is possible from a policy standpoint beyond what almost any sober pundit or strategic planner was incorporating into their models a few months ago — to say nothing of a year or two ago. Why should we expect the rate of change to slow? The fact that decision makers would like that to happen or would find it easier to do their jobs if it did is not the foundation for a rational planning approach.
An expansive scenario map provides a better foundation. Such maps help leaders make sense of incoming evidence in a systematic way: They can place each piece of evidence on the map where it belongs and carefully watch the evidence accumulate over time so they can continually adjust their estimates of where the future is likely to land. Radical uncertainty will eventually begin to stabilize into something more akin to risk. Decision makers who can track that process accurately and not jump too soon (or too late) have the long-term advantage.
Scenario thinking is a model that generates a more accurate reflection of the world we’re living in right now. It is also less vulnerable to arbitrage by people who might want to exacerbate radical uncertainty in an intentional way, to advantage their goals at the expense of yours. And scenario thinking is manageable for human decision makers, who can’t hold an infinite number of possibilities in their minds at once and should not trust an opaque generative AI model to try to do so for them.
It isn’t easy for corporate leaders to say “We don’t know” out loud and to act with courage and conviction in the face of that fact. Scenario thinking uniquely works to enable both at the same time. Now is the moment to do it.