Wharton@Work

April 2025 | 

The Hidden Flaws in Your Decision Making

The Hidden Flaws in Your Decision-Making (And How to Overcome Them)

“Go with your gut,” “trust your instincts,” “follow your heart”: they’re the mantras of those who see the ability to make good decisions as a talent, not a learned skill. From Obi-Wan Kenobi advising Luke Skywalker to trust his feelings, to Warren Buffett going with his gut to make investments, our approach is often shaped by the idea that great decision makers don’t overthink — they just know. (For the record, Buffett is known for his methodical research before he consults that famous gut.)

These mantras reinforce the myth that decision making is an art, not a science — an instinctive skill honed through experience or intuition rather than something you can systematically improve. But decades of research and observation suggest there’s much more to making smart choices than instinct alone.

Every Decision Is a Forecast

The first step in improving your judgment and critical thinking skills — the ones you rely on to make decisions — is understanding that every decision is a forecast. In fact, Wharton professor Maurice Schweitzer, whose research centers on decision making and negotiations, says this is the way he conceptualizes decisions. “We're constantly making forecasts,” he says. “When I order the fish dish, I'm forecasting that I’m going to like it more than the chicken dish. When I buy a factory in Texas, I’m forecasting that it will take six months to get it up and running. Or when I set a project deadline or hire a new manager, I’m making a forecast about others’ future performance.”

The big problem with forecasting, according to Schweitzer, is our strong tendency toward overconfidence. Overconfidence skews our judgment, causing us to underestimate risks, ignore contrary evidence, overestimate our ability to predict outcomes, and commit resources to flawed decisions that could have been avoided with a more realistic assessment.

“When we're overconfident, the future surprises us more than it should,” he explains, “which should signal that we’re making more committed decisions than we should. So, if it's cheaper in the long run to buy rather than rent, we might choose to buy a factory, because we’re so sure a product is going to be a hit that we even configure the factory for that one product.”

“Instead, we should be more likely to rent. We should invest in flexible space. We should prepare for best- and worst-case scenarios that are far better and far worse than the range that we're expecting today. Factoring in overconfidence also means that we should be reliant on historical data more than just current forecasts. If you’re considering opening a new factory in Texas, what you should not do is start from scratch and make your own forecast. What you should do is gather information from people who have opened similar factories in Texas about how long it took them to get it running.”

Schweitzer cautions that overconfidence can still get in the way at this stage, because the information we get from others can be easily dismissed as unique and idiosyncratic. “If you’re betting on a project taking you six months to complete, and others’ experience with a similar project was two to three years, you should ask yourself why you think you’re special. It takes real hubris to be sure that the things that have impeded everybody else don't apply to you. And that hubris leads us to make forecasts that are often badly wrong.”

Not All Uncertainty Is the Same

Understanding that our forecasts are often clouded by overconfidence is the first step — but what happens when we face decisions where the future is inherently uncertain? In high-stakes situations where outcomes are unpredictable (which describes most decision environments), traditional forecasting methods often fall short.

To make better decisions under uncertainty, it’s crucial to figure out which of two types of uncertainty you’re dealing with. Epistemic uncertainty, what Schweitzer terms “knowable uncertainty,” stems from a lack of knowledge — things we could learn with more or better data or analysis. Aleatory uncertainty, on the other hand, is inherent randomness — factors beyond our control, like rolling dice or fluctuating markets. Effective decision making requires distinguishing between the two: reducing epistemic uncertainty where possible and managing aleatory uncertainty through risk mitigation and adaptability.

“Here's why that difference matters,” says Schweitzer. “If we think about uncertainty in terms of whether it’s knowable [epistemic] or unknowable [aleatory], it should cause us to search for information differently. It should cause us to delay decision making differently. If the situation is aleatory, it’s truly random, and no amount of information gathering will change that. In that case, we should take lots of small chances. We shouldn't delay decisions under that kind of uncertainty because it's not, and never will be, knowable.”

When things are epistemic, he advises, we should take a different strategic approach. “Delay decision making until you gather more information, and be particularly careful in competitive environments in which somebody else may have more information than you do.”

This key difference in types of uncertainty isn’t just important in terms of how we make a decision, but also in how we reward others for making uncertain decisions. “If the uncertainty was epistemic,” says Schweitzer, “we should give more credit and more blame to the decision maker. But if it was truly aleatory — that is, the decision was really a gamble — we shouldn't give people too much credit when they got it right, or too much blame when they got it wrong.”

The distinction is critical for leaders who want to foster innovation, agility, or both. When uncertainty is epistemic, reward employees for acquiring better information, refining their analysis, and making well-reasoned choices to reinforce smart decision making. But when uncertainty is aleatory — where outcomes are largely out of the decision maker’s control — tying rewards too closely to success or failure can discourage risk-taking. If leaders want teams to think creatively or move faster than competitors, they need to recognize effort, sound judgment, and strategic reasoning rather than just outcomes. Otherwise, employees may play it safe, avoiding bold moves that could drive innovation.

Complicated or Complex?

Finally, Schweitzer points to one more distinction that can be leveraged to make better decisions: complicated versus complex. “In complex environments,” he explains, “you're dealing with human interactions, people who might have strategic motives. But when something is complicated — like launching a rocket or landing a rover on the moon — it’s really hard, with many factors to consider and figure out. But despite their difficulty, these challenges follow predictable rules of physics, engineering, and mathematics, meaning that with enough expertise, data, and computation, they can ultimately be solved. And if I figured it out last year, it's going to work again this year if variables such as temperature remain the same.”

“But when the environment is complex, such as when you’re making decisions about things like a global supply chain or a social media strategy, the dynamics keep changing. That means something that worked last year is unlikely to work the same way this year,” says Schweitzer.

For example, social media marketing involves platforms that are constantly evolving, with new algorithms, features, and user behaviors emerging regularly. A strategy that successfully engaged users last year may not yield the same results today because the landscape is in a state of continuous flux. This requires leaders to be adaptable, stay informed about the latest trends, and be prepared to pivot their strategies in response to shifting dynamics.

Managing a global supply chain presents a similar challenge. One year, a certain supplier might be the most cost-effective and reliable option, but shifting trade policies, geopolitical tensions, or unexpected disruptions — like a pandemic or natural disaster — can suddenly make that same choice a liability. In complex environments, much like under aleatory uncertainty, decision making isn’t just about solving for the best answer; it’s about continuously adapting to new realities and experimenting by taking smaller chances.