Risk vs. Uncertainty: Why Entrepreneurs Are Playing the Wrong Game

Most business textbooks are written as if the future can be calculated. Five-year revenue forecasts, IRR, NPV, business plans with three scenarios. These tools work—but only for a specific class of problems. And an entrepreneur is almost never operating within that class.

This article is about the distinction between risk and uncertainty. The difference may seem academic, but it underpins almost everything practical: which decisions you can actually optimize, which you can’t, and which strategies truly work when the future is, in principle, unpredictable.

The distinction Knight made a hundred years ago

In 1921, economist Frank Knight published the work Risk, Uncertainty and Profit. The book is considered a classic, but its key idea remains fuzzy in practical discourse. The idea is simple.

Risk is a situation where outcomes are unknown, but their probabilities are known. A classic example: car insurance. The insurer doesn't know if a specific client will get into an accident, but they know the statistics for hundreds of thousands of clients and can calculate expected losses with decent accuracy. This is a manageable situation—it can be optimized.

Uncertainty (often referred to in literature as "Knightian uncertainty") is a situation where not only are the outcomes unknown, but the probabilities themselves are as well. There is no historical data, no distribution, and no standard way to calculate "expected value." Most decisions an entrepreneur makes in the early stages of a business fall into this category.

Knight took another important step. He argued that entrepreneurial profit is neither a wage for labor nor a return on capital. It is compensation for bearing uncertainty. If the future were statistically predictable, competition would have long ago eroded the margins of any business. Profit exists because someone makes decisions where calculation is impossible—and occasionally, they turn out to be right.

This leads to one important conclusion that is rarely spoken aloud. When an entrepreneur tries to apply tools designed for risk (such as a detailed business plan, a five-year revenue forecast broken down by quarter, or a calculated ROI for a product in a non-existent market) to a situation of uncertainty, they are not managing risk—they are creating an illusion of control. And this illusion is often worse than the honest admission: "I don't know."

What Prospect Theory Reveals

In 1979, Daniel Kahneman and Amos Tversky published "Prospect Theory: An Analysis of Decision under Risk" in the journal Econometrica. This empirical study examines how people actually make decisions when presented with choices involving varying probabilities. In 2002, Kahneman received the Nobel Prize in Economics for this and related work.

Here are a few takeaways relevant to entrepreneurs.

First, the asymmetry of losses and gains. The pain of losing 1,000 units is subjectively felt about twice as intensely as the joy of gaining the same 1,000. This is not a moral stance; it is a stable property of human perception. In 2019, a global study involving more than 4,000 people confirmed that the findings from 1979 are reproducible across different countries.

Second, a shift in risk attitude depending on how the choice is framed. When dealing with gains, people tend to prefer a smaller guaranteed win over a larger, riskier one (risk-averse behavior). When dealing with losses, the opposite occurs: people are willing to take greater risks just to avoid a guaranteed loss. The same person, when faced with the same objective situation presented through two different frames, will make opposing decisions.

Third, the overweighting of small probabilities and the underweighting of large ones. People systematically exaggerate the odds of rare events (which is why they buy insurance against unlikely catastrophes and play the lottery) and underestimate the odds of moderately probable ones (which is why they don't prepare as seriously as they should for a 30 percent chance of product failure).

What this means for an entrepreneur in practice: your decisions regarding product investment, hiring, or entering a new market do not pass through a rational calculation of expected utility. Instead, they pass through a distorted system of perception, in which losses weigh twice as much as gains, and your attitude toward risk shifts depending on how you frame the situation to yourself. This isn’t a flaw—it’s human hardware. But if you don’t account for it, you’ll be left wondering why you aren’t moving in the obviously right direction, or conversely, why you’re taking risks where you should have hit the brakes.

What experienced entrepreneurs do: the Sarasvathy study

The most practical research on this topic is the work of Saras Sarasvathy, a professor at the Darden School of Business at the University of Virginia. In the late 1990s, she conducted a study in which 27 experienced entrepreneurs and 37 MBA students were asked to think aloud while solving standard problems related to starting a new business. The results were published in 2001 in the Academy of Management Review and later expanded into the book Effectuation: Elements of Entrepreneurial Expertise (2008).

Sarasvathy discovered that experienced entrepreneurs and beginners use fundamentally different logic.

Beginners and MBA students used predictive logic (Sarasvathy calls this "causal"). First, you define a goal, then you calculate the resources needed to reach it, then you build a plan, then you execute. This is a normal way to think under risk—it works when you are opening another branch of a well-known franchise. But under uncertainty, it breaks down because the goal, resources, and plan are constantly changing.

Experienced entrepreneurs used effectual logic—the logic of non-predictive control. They didn't start with a goal, but with what they already had: their skills, their network, and the money they were prepared to lose. Next, they looked at what possible outcomes could be achieved with those resources, chose a direction, took a step—and reassembled their strategy based on the results.

Sarasvathy highlights several principles of effectuation, three of which are particularly important.

The affordable loss principle. An experienced entrepreneur doesn't optimize for expected profit. They determine how much they are willing to lose without significantly damaging their life and act within those limits. This shifts optimization from the upper bound (maximum profit) to the lower bound (survival), which works much better in conditions of uncertainty.

The Leverage of Contingency. In predictive logic, surprises are obstacles to a plan. In effectual logic, they are raw material. An experienced entrepreneur treats an unexpected event—a client requesting something you’ve never done, a partner suggesting a pivot you hadn't considered, or a market segment collapsing while another emerges—as a component to be integrated into their strategy, rather than trying to force a return to the original plan.

The Principle of Building Partnerships. Instead of competing for a hypothetical share of a hypothetical market, the experienced entrepreneur builds a network of stakeholders willing to commit now (with capital, time, or contracts). These partnerships simultaneously reduce uncertainty and define the market itself.

Subsequent research has confirmed that this distinction between novices and experts is consistent. A study by Dew, Read, Sarasvathy, and Wiltbank, published in the Journal of Business Venturing in 2009, empirically demonstrated that experimentation (a sub-category of effectuation) is positively correlated with the level of uncertainty in a task, while a causal approach is negatively correlated. The higher the uncertainty, the more likely experienced entrepreneurs are to abandon planning in favor of experimentation.

Optionality and Asymmetric Bets: Taleb

The fourth foundational body of work consists of Nassim Taleb’s books, The Black Swan (2007) and especially Antifragile (2012). Taleb has not published in academic journals in the same way as Kahneman or Sarasvathy, but his applied framework aligns perfectly with this subject.

The core idea is payoff asymmetry. In a world of uncertainty, you cannot predict which event will occur. However, you can structure your position so that any outcome results in either a moderate loss or a significant gain. This is called optionality.

Taleb proposes a "barbell strategy": keep the bulk of your resources in the safest possible assets (low fixed liabilities, cash, diversified income streams) and allocate the remainder to a few bets with limited risk and unlimited upside potential. The middle ground is the worst place to be because it offers average gains while failing to protect you against catastrophic losses.

Practical implications for entrepreneurs:

— Avoid betting everything on a single scenario. It is better to make several small bets on different scenarios, each of which you can afford to lose.

— Debts, expensive offices, and a large team at launch are what turn asymmetry into its opposite. You lose flexibility and are forced to win on every single bet. In conditions of uncertainty, this is a mathematically losing position.

— Experimentation is more important than forecasting. A real-world test with a small audience provides more information than an Excel model—because a model is based on assumptions about an unknown future, while a test is based on observed reality.

Taleb’s concept of optionality and Sarasvathy’s principle of affordable loss are essentially the same idea viewed from two different angles: one from the perspective of a theorist, the other from the perspective of a researcher studying entrepreneurial practice.

The Big Picture

When you combine these four sources, a clear framework emerges.

Most of the decisions an entrepreneur makes in the early years of a business are made under conditions of uncertainty, not risk. These cannot be optimized using standard financial tools, because those tools require knowing probabilities that do not exist.

Human perception systematically distorts the evaluation of such decisions: losses weigh heavier than gains, the way a problem is framed dictates risk appetite, and low probabilities are consistently overestimated. Knowledge alone doesn't fix this—it runs as a background process even for those who have read Kahneman.

Experienced entrepreneurs don't overcome this by making more accurate forecasts; they do it by using a different strategy. They optimize for survival rather than for maximum gain. They start with what they have, not what they think they should have. They treat randomness as a resource, not a nuisance. They build asymmetric bets where the downside is limited and the upside is uncapped.

This, right here, is the primary takeaway for practice.

What to do about it

Here are a few things that follow directly from this research.

Before optimizing a solution, ask yourself what class it belongs to. If you can honestly calculate the probabilities, it’s a risk, and standard tools will work. If you can’t, it’s uncertainty, and trying to apply risk tools to it (especially forecasts and business plans) creates a false sense of security that eventually proves costly.

Explicitly define your "affordable loss." Not "how much I can earn," but "how much I can lose without facing a catastrophe." This number must be defined for every decision; otherwise, you’ll be deciding based on intuition, and intuition performs poorly under uncertainty (see Kahneman).

Build optionality instead of forecast accuracy. In practical terms, this means: fewer long-term contracts, fewer irreversible investments, and more small-scale experiments that can be halted.

Reframe the decision in two ways. If you’re hesitant, analyze the same decision from the perspective of "what I stand to gain" and "what I stand to lose." If your answer differs, you’ve fallen into the framing effect—stop and think again.

Treat surprises as data. A client asks for something you don’t have; a partner leaves; the market shifts—these aren't plan failures, but data on the real structure of the environment you were trying to model. Effectual logic dictates: integrate this data into your strategy rather than trying to force a return to the original plan.

What Doesn't Work

The idea that "you just need a solid business plan" doesn't work. A business plan is useful for documenting hypotheses, but in an environment of uncertainty, it becomes obsolete faster than you can write it. This is not an argument against planning—it is an argument against treating a plan like a map.

The idea that "experienced entrepreneurs have a 'gut feeling' for the market" doesn't work. Sarasvathy’s research shows that it’s not about intuition, but a different logic of reasoning. This logic can be described, it can be learned, and it doesn't require any special talent.

The idea that "you need to make one big bet and see it through to the end" doesn't work. This is a popular narrative, but it doesn't align well with the data. Most resilient businesses grew through a series of small experiments and pivots, rather than the heroic execution of an initial vision. Heroic stories are simply better for storytelling.

Bottom Line

The most important takeaway from these four studies is a question worth asking yourself before every major decision: "Am I in a situation of risk or uncertainty?" The tools you use, how you frame your decision, and the most likely pitfalls all depend on the answer to this question.

If you are in a situation of risk, then calculate, model, and optimize. If you are in a situation of uncertainty, switch gears: determine your affordable loss, build optionality, run small experiments instead of making big bets, and use randomness as a resource.

Most entrepreneurial decisions fall into the second category. Which means that spending your whole life playing by the rules of the first is playing the wrong game.

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Sources:

1. Knight, F. H. Risk, Uncertainty and Profit. Boston & New York: Houghton Mifflin Company, 1921. — https://papers.ssrn.com/sol3/papers.cfm?abstract_id=1496192

2. Kahneman, D., & Tversky, A. Prospect Theory: An Analysis of Decision under Risk // Econometrica, 1979, 47(2): 263–291. — https://www.econometricsociety.org/publications/econometrica/1979/03/01/prospect-theory-analysis-decision-under-risk

3. Sarasvathy, S. D. Causation and Effectuation: Toward a Theoretical Shift from Economic Inevitability to Entrepreneurial Contingency // Academy of Management Review, 2001, 26(2): 243–263. — https://papers.ssrn.com/sol3/papers.cfm?abstract_id=1505857

4. Dew, N., Read, S., Sarasvathy, S. D., & Wiltbank, R. Effectual versus Predictive Logics in Entrepreneurial Decision-Making: Differences Between Experts and Novices // Journal of Business Venturing, 2009, 24(4): 287–309. — https://www.sciencedirect.com/science/article/abs/pii/S088390260800027X

5. Sarasvathy, S.D. Effectuation: Elements of Entrepreneurial Expertise. Edward Elgar Publishing, 2008.

6. Taleb, N.N. Antifragile: Things That Gain from Disorder. Random House, 2012.

7. Ruggeri, K., et al. A global study confirms the influential theory behind loss aversion (replication of the 1979 prospect theory study) // Nature Human Behaviour, 2020. — https://www.publichealth.columbia.edu/news/global-study-confirms-influential-theory-behind-loss-aversion