Fundamentals

Prospect Theory: How We Decide About Gains, Losses, and Risk

By Daniel Sardá · Published on

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Prospect theory explains why the same choice can change when we see it as a gain or a loss, even when the probabilities remain the same.

Prospect theory does not study how to imagine future scenarios, nor is it part of strategic foresight. It explains how people commonly make decisions when a choice involves gains, losses, and known probabilities.

Consider a simple decision. You can receive 900 monetary units for certain, or accept a 90% chance of receiving 1,000. Many people prefer the certain 900. Now change the framing: you can lose 900 for certain, or face a 90% chance of losing 1,000. Although the expected values are again the same, people become more willing to take a risk to avoid the certain loss.

Why does the attitude change if the mathematical structure is symmetric? In 1979, Daniel Kahneman and Amos Tversky proposed that we do not assess each outcome solely by the final wealth it leaves us with. It also matters whether we experience it as a gain or a loss relative to a reference point.

Key idea: Prospect theory is descriptive: it aims to explain observed patterns of choice. By itself, it does not tell us which decision is correct.

The problem the theory seeks to explain

Expected utility theory provides a powerful normative framework for choosing among risky alternatives. Under its assumptions, each possible outcome has a utility, and the choice takes that utility into account along with its probability. Yet some observed choices do not consistently conform to those assumptions.

Kahneman and Tversky began with those regularities to develop an alternative model. Their question was not whether people could calculate, but which psychological features entered into valuation. Their original answer combined two operations: first, organizing and simplifying the options; then, evaluating the outcomes through a value function and decision weights.

The reference point changes how an outcome is read

People do not usually experience an outcome in absolute terms. They compare it with a reference: their current situation, an expectation, a goal, or even a figure presented during the choice. Gaining 100 may feel like an improvement when zero was expected, and like a loss when 200 was expected.

For that reason, the theory works with changes around a reference point, not only with final levels of wealth. This intuition is compatible with the importance of subjective valuation, but it should not be confused with a general economic theory of value. Here, the model concerns how concrete outcomes are valued under risk.

The reference point is not set by a single rule, either. It can shift with expectations, context, or the way an option is presented. That flexibility helps explain decisions, but it also calls for caution: if we do not know which reference is relevant, an application of the model may be ambiguous.

Gains, losses, and diminishing sensitivity

The value function proposed in 1979 has three central features:

The last feature expresses loss aversion: losing an amount tends to have a greater impact than gaining the same amount. It does not mean that a loss “hurts exactly twice as much.” The estimated magnitude depends on the method, the person, and the context.

Its curvature represents diminishing sensitivity. The perceived difference between gaining 0 and gaining 100 is often greater than the difference between gaining 1,000 and gaining 1,100. Something similar happens with losses. As we move farther from the reference point, an equal increment can produce a smaller subjective change.

Useful distinction: Loss aversion compares the impact of losing and gaining. Risk aversion describes a preference for a safe option over a risky one. They are not the same, and a person does not avoid risk in every circumstance.

The opening example combines these mechanisms. In the domain of gains, securing 900 may be appealing compared with an equivalent gamble. In the domain of losses, accepting risk may seem preferable if it preserves some chance of losing nothing. This is a common pattern, not an inevitable reaction.

Probabilities do not always carry proportional weight

The second component is decision weights. They indicate how much an uncertain outcome influences a choice, but they do not replace the objective probability or necessarily represent a belief about it. A probability may be perfectly well defined and still not exert a proportional influence on the decision.

The certainty effect helps explain this. Moving from a very high likelihood to a guarantee can produce a larger shift in preference than a comparable reduction between two probabilities that remain uncertain. Certainty takes on special importance.

The isolation effect occurs when people discard elements that alternatives share and focus on what differentiates them. This shortcut can make comparison easier, but it can also allow two equivalent presentations to produce different choices.

None of this means that people change the actual probabilities. Probability answers, “How possible is the outcome?” A decision weight seeks to represent, “How much does that possible outcome influence the choice?” Confusing the two turns a psychological explanation into a mathematical error the theory does not make.

What the cumulative version added

In 1992, Tversky and Kahneman introduced cumulative prospect theory. This revision uses cumulative weighting and can address options with multiple outcomes, while also extending the analysis from risk—where probabilities are known—to uncertainty, where they are not always precisely determined.

The cumulative version formalized the so-called fourfold pattern. With high probabilities, risk aversion for gains and risk seeking for losses are commonly observed. With low probabilities, the pattern may reverse: a small chance of a large gain may attract people, while a small chance of a large loss may encourage the search for safety. The result depends jointly on the form of the value function and the weighting.

Key idea: There is no single attitude toward risk. Whether the outcome is a gain or loss, and the weight given to its probability, can change the choice.

What it helps us understand—and what it does not establish

Prospect theory provides a useful vocabulary for analyzing everyday decisions, contracts, insurance, or commercial messages. It invites us to ask what the reference point is, how the outcome is presented, and what role certainty plays. Those questions help uncover framing effects that might otherwise go unnoticed.

It also suggests a cautious institutional implication: clear rules, comparable information, and the freedom to examine alternatives reduce dependence on arbitrary frames. But the model does not automatically prove that a particular regulation, investment, or policy is advisable. Moving from a descriptive regularity to a recommendation requires further arguments and attention to costs.

Subsequent evidence has found broad support for several patterns, together with differences across countries, people, tasks, and moments. In addition, a simple experimental choice does not reproduce all the complexity of a real decision. Reference points can change, probabilities can be imprecise, and people can learn.

The theory's lasting contribution is not a recipe for making the right choice. It shows that deciding under risk involves more than multiplying outcomes by probabilities: it matters where the change is evaluated from, how losses are experienced, and what effective influence each possibility receives. Recognizing those layers does not eliminate risk, but it makes the decision more transparent.

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About the author

Daniel Sardá is an SEO Specialist, a university-level technician in Foreign Trade from Universidad Simón Bolívar, and editor of Libertatis Venezuela. He writes on liberalism, political economy, institutions, propaganda and individual liberty from an independent, non-partisan perspective.

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