The Value of Uncertainty is the Cost of Biased Decisions
Bias toward experience feels rational in high-stakes decisions, but it often blinds us to the very value that uncertainty makes possible.
Making decisions under uncertainty is tough. One widely accepted approach to decision making is to evaluate choices based on how well they align with the best available information at the time. This approach is based on epistemic rationality. Epistemic rationality favors choices towards alternatives that are more familiar, well-known, widely understood, and documented than those that are not. In other words, it favors the familiar over the novel.
What Do We Know — Value of Expertise
Faced with a complex problem, organizations instinctively seek out someone who has solved a nearly identical problem before. The perceived safety of “been there, done that” often outweighs curiosity about alternative perspectives.
Overreliance
In hiring, most organizations will instinctively choose a candidate with direct experience in an identical project over someone with adjacent or transferable skills. The logic is simple: hire the person who’s “done it before.”
As a result, experts command significantly higher fees than newcomers. The experience is treated as capital that allows for predicting or, even better, guaranteeing future success.
Yet in practice, this often isn’t the case. Once experts begin working, they quickly realize the current problem is similar but meaningfully different from the problems they have solved in the past. The nuance matters. The context changes. The system constraints, the incentives, and the human dynamics are different in ways that matter. What worked before led you down the entirely wrong path.
Overfitting
The value of prior experience is frequently overstated. It can lead to overconfidence, blind spots, and a reliance on legacy thinking. From a decision science standpoint, this is a classic case of overfitting—generalizing too confidently from past data to a new context. It mirrors a problem in machine learning: the model trained on one dataset performs well on familiar tasks but struggles when faced with even slightly different input.
Expertise can narrow your thinking. Experienced professionals may rely too heavily on frameworks that served them well in the past. Meanwhile, a blood might be better equipped to innovate or ask the right questions. They are not anchored to "what worked before."
This is not to say expertise is irrelevant. It is relevant. It is vital. And it is overpriced. Expertise is a double-edged sword: it provides shortcuts for understanding, but it also carries hidden costs.
A more nuanced approach would be to ask not just what someone has done, but how they think, especially in unfamiliar or evolving contexts.
As complexity increases across industries and disciplines, the ability to navigate ambiguity may prove more valuable than an impressive past experience. This reframes uncertainty not just as a risk to be minimized, but as a source of value that traditional bias often fails to capture.
What We Don’t Know — Uncertainty Bias
What makes uncertainty costly is also what makes it valuable. It creates room for multiple futures. In stable and well-known environments, the future is relatively fixed. There is little to gain by thinking differently because the outcomes are predictable. But in uncertain or fast-evolving environments, the cost of being wrong is higher, and so is the potential upside of getting it right. Uncertainty creates leverage. It amplifies the consequences of the choices we make.
Bias is an Ineffective Hedging Strategy
This is where bias enters the picture, not as a cognitive flaw but as a form of pragmatic solution. When outcomes are uncertain, we reach for familiarity not just because it feels safe but because it lowers the perceived cost of decision-making. Bias acts as a hedge against the unknown. It collapses the range of possibilities into something more manageable and more actionable. That has practical value, but it also carries a cost.
The true cost of this bias lies in what it excludes. When we rely too heavily on familiar patterns, we close ourselves off to outcomes that exist outside those patterns. We miss the unexpected breakthroughs, the emergent insights, and the novel but untested approaches that uncertainty makes possible. In this sense, the cost of uncertainty is what gives diversity of perspective and new thinking their value.
Velocity
A volatile landscape rewards adaptability over repeatability. Decision-making in uncertain contexts must prioritize cognitive flexibility instead of historical success. That means hiring for learning velocity rather than for a specific résumé. It means investing in people who ask better questions, not just those who have already answered familiar ones.
In complex systems such as markets, organizations, or social structures, where cause and effect are tangled and non-linear, past experience is only one data point. A more robust model of value accounts for how people perform when the future is uncertain, not just when it resembles the past.
Seen this way, bias toward experience is not just a preference. It is a discount. We devalue the unknown because it is harder to measure. But what if we treated the ability to operate under uncertainty not as a liability but as an asset? What if we paid for adaptability, not just familiarity?
The practical value of bias lies in reducing uncertainty. But the cost is that it may prevent us from realizing the much greater value that uncertainty makes possible.
Ultimately, in a world where the future is increasingly unpredictable, the smartest bet may be on those who know how to think, not just those who know what worked before.