“If I Were Wrong, What Would It Look Like?”

Lakshman Achuthan went on TV in 2011 and predicted a recession was near. This wasn’t bold. Forecasting trouble was standard fare for economists after the Great Recession.

But Achuthan did something that set him apart: He clearly defined what being wrong would look like. “If there’s no recession in Q4 or the first half [of 2012], then we’re wrong,” he said.

The first half of 2012 came and went with no recession. So Achuthan did what few forecasters ever do: He publicly admitted he was wrong.

It is hard to overstate how rare and beautiful this series of events was.

Forecasting is hard for three reasons. 1) Most things we forecast are infinitely more complex than we assume. 2) Forecasting has no marginal cost, with so many forecasts made in so many places that accountability is lost. 3) Most forecasts are too vague, twisted around subsequent events to give an inflated sense of accuracy.

This isn’t just economists. Businesses make forecasts with budgets. Investors make them with return assumptions. Manufacturers with production orders. Marketers with advertising campaigns.

Every worthwhile reward owes its success to the fact that things could have turned out different. Payoffs come from landing on the right side of risk. Navigating risk requires believing something will happen that isn’t obvious or easy to others. A forecast, basically. And every forecast is followed by one of four outcomes:

Right for the right reasons. This is great, but hard and rare.

Right for the wrong reasons. Still rare, but misleading when it happens.

Wrong but your forecast was too vague to tell/you moved the goal post. Extremely common.

Wrong, identified as such, and able to move on smarter. Could be both common and enlightening if you attach to every forecast an answer to the question, “If I were wrong, what would it look like?” Like Achuthan did.


Carol Tavris writes in her book Mistakes Were Made (But Not By Me):

Most people, when directly confronted by evidence that they are wrong, do not change their point of view or course of action but justify it even more tenaciously. Even irrefutable evidence is rarely enough to pierce the mental armor of self-justification.

The psychology here is that it’s hard to distinguish evidence that contradicts your forecast from a personal attack by the person presenting that evidence. If I do a bunch of analysis and conclude that a company is going to win, and you point out that my analysis is wrong, what I am likely to hear in my head is, “Morgan, I did better analysis than you and saw stuff you overlooked.” Then the defenses come up. No matter how thick-skinned you are, or how gently the other side corrects you, this happens. I have met few people immune to it, despite how humble and open-minded anyone claims to be. And I don’t think you even want to be immune to it. Most things are nuanced, so quickly accepting every rebuttal can be as bad as rejecting all of them.

It helps in this situation to keep your forecasts accountable to yourself. You do this by clearly defining what being wrong would look like the moment a forecast is made. Then you don’t necessarily have to accept anyone else’s rebuttal; just your own.

Yes, your definitions of being wrong can be absurd. But then you and everyone around you knows how seriously to take your forecast. I can say, “Tesla will become the most valuable company in the world,” and define failure as Tesla not having the highest market cap by 2050. And then everyone knows to discount the forecast, because by giving myself three decades to be right I’m acknowledging how little confidence I have. Force yourself and other people to preemptively define failure, and you will see that the majority of forecasts either turn out wrong or can be ignored to begin with.

A couple other things happen when you do this:

You move on quickly when you’re wrong. During the 2008 financial crisis Goldman Sachs’s CFO said “We were seeing things that were 25-standard deviation events, several days in a row.” This is the most sophisticated way of saying “Our models are utterly wrong” without saying or admitting it. And if you are unable to admit being wrong – which happens when you don’t define what being wrong looks like – you’ll be perpetually anchored to your own ego and view of the world. Admitting you’re wrong, fixing it, and moving on can be wonderful. Nike founder Phil Knight notes: “Sometimes you have to give up. Sometimes knowing when to give up, when to try something else, is genius. Giving up doesn’t mean stopping. Don’t ever stop.”

You become more careful about what you forecast. Most people forecast too much, because making a forecast is easy and the thought of being right is comforting. That’s dangerous, because forecasts start to become something you do to justify how you want the world to work, rather than an analysis of how it actually works. Defining what being wrong would look like recognizes from the start that you could be wrong, which reminds you from the beginning that you need to plan for what happens if you’re wrong. There is no certainty. Once that sets in you’ll be more hesitant to forecast to begin with. And that’s how it should be. I haven’t heard from Achuthan in a while.


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