Prospect theory, developed by Daniel Kahneman and Amos Tversky in 1979, is a psychological theory of how people actually choose between risky options.
It replaces the classical assumption of a perfectly rational decision-maker with a description of how real people weigh gains, losses, and probabilities.
The theory holds that people are more influenced by the possibility of a loss than by the prospect of an equivalent gain (Kahneman & Tversky, 1979).
This asymmetry, known as loss aversion, means that losing feels roughly twice as powerful as gaining the same amount (Kahneman, 2011).
Key Takeaways
- What it is: prospect theory is a theory in behavioral economics that describes, mathematically, how people’s decisions are shaped by their attitudes toward risk, uncertainty, loss, and gain.
- Origins: Kahneman and Tversky developed prospect theory to explain decision-making under risk through a series of controlled “lottery” experiments.
- Two key biases: the theory names two biases: loss aversion (losses feel about twice as powerful as gains) and probability distortion, weighing unlikely outcomes too heavily.
- Perceived, not actual: people decide based on how outcomes are perceived and framed, not on their objective utility.
- Wide application: prospect theory is now widely accepted across economics and psychology, with uses ranging from international relations to whether people buy insurance.
Overview and History
Prospect theory is a theory of decision-making that attempts to explain how people’s decisions are influenced by their attitudes toward risk, uncertainty, loss, and gain.
Kahneman and Tversky spent the early 1970s studying the biases behind everyday judgement, including the availability heuristic and the anchoring bias (Tversky & Kahneman, 1974).
After roughly five years testing choices between gambles, they published prospect theory in the journal Econometrica in 1979 (Kahneman & Tversky, 1979).
Kahneman later said the choice of journal was deliberate. An identical paper “would likely have had little impact on economics” in a psychology journal (Kahneman, 2011).
The theory grew in two further steps.
- 1981: Tversky and Kahneman showed that re-describing identical choices as gains or losses changes what people pick, the framing effect (Tversky & Kahneman, 1981).
- 1992: Tversky and Kahneman extended the theory to cover gambles with any number of outcomes, fixing a technical flaw in the original version.
It has since been highly influential across economics, finance, and psychology, helping explain why people rely on mental shortcuts ( heuristics ) and often make suboptimal decisions.
How Prospect Theory Works
Prospect theory rests on three assumptions about how people actually judge risk (Kahneman & Tversky, 1979).
- Loss aversion: people care more about avoiding a loss than about making an equal-sized gain, so losses carry roughly twice the emotional weight of gains.
- Reference dependence: gains and losses are judged against a reference point, usually the current situation, not against total wealth.
- Probability distortion: people overweight small probabilities and underweight moderate-to-high ones, rather than weighing outcomes by their true likelihood.
Together, these assumptions predict a distinctive pattern: people are risk-averse when protecting a gain but risk-seeking when trying to escape a loss.
The Reference Point
The reference point is a key concept in prospect theory.
It is the starting point from which people make decisions about gains and losses. The reference point can be either an actual or an imaginary starting point.
Kahneman and Tversky (1979) proposed that the reference point is determined by a number of factors, including:
- past experiences
- current circumstances
- cultural norms
- individual preferences
Kahneman and Tversky also suggested that the reference point is not always static. It can change over time in response to new information or new experiences.
Kahneman (2011) illustrates why the reference point matters with Anthony and Betty, who both end up with the same final wealth. For Anthony, who started with less, that outcome is a gain. For Betty, who started with more, it is a loss, so the two feel, and choose, very differently.
Classical economics ignored this “moving part” for 250 years. Kahneman (2011) calls that blind spot theory-induced blindness: once a theory is accepted, its flaws become hard to notice.
The reference point remains prospect theory’s biggest open question, since the theory does not itself explain where a reference point comes from (Kőszegi & Rabin, 2007).
Decision Analysis: Phases of the Decision-Making Process
Prospect theory posits that people make decisions in two stages: editing and evaluation.
In the editing stage, people simplify complex situations by ignoring some information and by using mental shortcuts (heuristics).
In the evaluation stage, people use their attitudes toward risk and uncertainty to choose between different courses of action (Levy, 1992).
The Editing Stage
The editing stage is important because it determines what information will be used in the evaluation stage.
This means that people’s decisions can be biased if they do not have all of the relevant information or are using simplifying heuristics.
For example, people may make suboptimal decisions if they only consider a small number of options or if they only focus on the most likely outcomes. Not every option gets weighed equally.
The editing stage is also important because it enables people to decide and rank outcomes by their desirability, deciding which ones matter most.
They can then consider the lesser outcomes as losses and the greater ones as gains (Levy, 1992).
The editing phase aims to alleviate any framing effects, which is a positive bias stemming from whether the outcomes are presented to someone positively or negatively. Wording alone can shift the choice.
It also attempts to resolve isolation effects stemming from a person’s bias toward isolating probabilities instead of treating them together.
The substages of this editing process are called coding, combination, segregation, cancellation, simplification, and detection of dominance (Levy, 1992).
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Coding is the process of transforming outcomes into numerical values. This enables people to compare different outcomes and makes it easier to combine them into a single value.
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Combination is the process of combining multiple outcomes into a single value. This can be done in two ways: by adding the values together (linear combination) or by taking the average of the values (weighted combination).
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Segregation is the process of separating positive and negative outcomes. This is important because people tend to view gains and losses differently.
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Cancellation is the process of canceling out equivalent but opposite outcomes. For example, if someone has a 50% chance of winning $100 and a 50% chance of losing $100, then these two outcomes cancel each other out, and the person is left with no expected gain or loss.
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Simplification is the process of reducing the number of outcomes that need to be considered. This can be done by ignoring irrelevant outcomes or by grouping together similar outcomes.
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Lastly, the detection of dominance is the process of choosing the best option from a set of options. This can be done by considering only the most likely outcomes (maximizing) or by considering all of the possible outcomes (satisficing).
The Evaluation Stage
The evaluation stage is where people make their final decisions.
In this stage, people weigh the potential gains and losses of each option and choose the option that they believe is most likely to lead to the best outcomes.
This weighing of outcomes is computed as a “utility,” which is mathematically based on potential outcomes and their respective probabilities.
Prospect theory predicts that people will be more risk-averse when the stakes are high and more risk-seeking when the stakes are low. This means that people are more likely to take actions that minimize losses (Levy, 1992).
Critical Evaluation
Prospect theory is one of the most influential ideas in behavioral economics, but it is not beyond challenge.
Strengths
- Descriptive power: one compact theory explains anomalies that classical economics cannot, including the pattern of simultaneous gambling and insurance-buying (Kahneman & Tversky, 1979).
- Formal precision: unlike most psychological theories, prospect theory is mathematically specified, which let economists test its predictions directly (Tversky & Kahneman, 1992).
- Empirical robustness: its core patterns replicated across 19 countries four decades after publication, and its loss-versus-gain asymmetry shows up in brain activity, not just choices (see Contemporary Research, below).
Limitations
- The unspecified reference point: critics have called the reference point difficult to pin down in real settings, since it can shift with expectations or social comparisons (Kőszegi & Rabin, 2007).
- As-if, not process: Berg and Gigerenzer (2010) argue the theory repairs classical economics’ predictions without describing the actual cognitive steps decision-makers go through.
- A contingent centrepiece: the loss-aversion ratio, long treated as a fixed constant, turns out to depend on stake size and context (see Contemporary Research, below).
- Missing emotions: the theory values outcomes at the moment of choice, with no place for anticipated regret or disappointment (Kahneman, 2011).
Contemporary Research
Recent work asks two questions. Does the theory’s basic architecture hold up under modern scrutiny, and is loss aversion itself a fixed constant?
A Large-Scale Replication
The clearest answer to the first question comes from a 2020 multinational study.
- Aim: to test whether the patterns reported in the original 1979 paper hold up today, after replication failures among other classic findings in psychology.
- Method: Ruggeri et al. (2020) gave the original 1979 choice problems, adjusted only for local currency, to 4,098 participants across 19 countries and 13 languages.
- Results: 94% of the original findings replicated, with twelve of thirteen theoretical contrasts confirmed overall, though effect sizes and country-level results varied.
- Conclusion: the empirical foundations of prospect theory replicate well beyond reasonable doubt, even though the exact strength of the effect differs by culture.
Is Loss Aversion Real in the Brain?
Brain-imaging evidence suggests the asymmetry between losses and gains is not just a convenient equation. When people evaluated gambles in a brain scanner, activity in the brain’s valuation regions rose with the size of a potential gain.
It fell more steeply for an equivalent potential loss, an asymmetry that tracked each person’s own behavioural loss aversion (Tom, Fox, Trepel, & Poldrack, 2007). This asymmetry can also be dialled down.
Telling participants to “think like a trader,” treating each gamble as one of many, reduced both loss aversion and stress responses to losing (Sokol-Hessner et al., 2009).
Is Loss Aversion a Universal Constant?
Not every study agrees that loss aversion is fixed.
Walasek and Stewart (2015) found that the usual loss-aversion ratio can shrink, disappear, or reverse simply by changing the range of gains and losses a person sees.
Yechiam (2019) reached a similar conclusion by re-reading the classic studies themselves, arguing that small losses were often not overweighted even where large ones were.
Taken together, the picture is more coherent than contradictory. The theory’s core architecture, reference dependence, the reflection of risk around that point, and the fourfold pattern, is well supported.
What the newer evidence removes is the idea that the loss-aversion ratio is a fixed law of human nature. It shifts with stakes and context instead.
Examples
Buying Phone Insurance
When people buy a new phone, they are often offered insurance against loss or theft. Prospect theory explains why people buy it.
People act to minimise losses. Someone who believes their phone will break or be stolen is more likely to take out cover.
But probability weighting matters too. People overweight small probabilities and underweight large ones, so even a low risk can feel worth insuring against.
Suppose the risk is 10%, the potential loss is 500 pounds, and the premium is 50 pounds. Expected loss and premium are equal.
A purely rational agent would be indifferent. People are not.
Applying prospect theory starts with a reference point, either current wealth or the worst case of losing 500 pounds. Framed against current wealth, paying the premium is a sure loss, v(-50).
The lottery alternative is a 90% chance of losing nothing, or a 10% chance of losing 500 pounds. The equation overweights that 10% chance.
So the risk feels bigger than one-in-ten. That is why people often pay more than 50 pounds for cover they would refuse on a strict expected-value basis.
War: International Relations
Prospect theory also applies to political decisions, especially a government’s choice to go to war.
War is costly. The expected utility of fighting depends on the probability of winning, the potential loss if the war is lost, and the value function.
Prospect theory predicts that governments act to minimise losses, so a government facing a high chance of defeat is normally less likely to start a war.
But small probabilities are overweighted and large ones underweighted. So even a low chance of losing may not deter a government, if the consequences of inaction feel severe enough.
Suppose the probability of winning is 50%, the potential loss if the war is lost is 1000 lives, and the value function is linear.
Framed against the current situation, fighting means a 50% chance of winning against 1000 lives at stake. Not fighting has a prospect-utility of v(-500).
Prospect theory predicts the government may still fight, even though the expected utility is negative. It overweights the small chance of winning and underweights the large chance of losing.
Further Information
- Levy, J. S. (1992). An introduction to prospect theory. Political psychology, 171-186.
- Kahneman, D., & Tversky, A. (1982). The psychology of preferences. Scientific American, 246(1), 160-173.
- Shah, A. K., & Oppenheimer, D. M. (2008). Heuristics made easy: an effort-reduction framework. Psychological bulletin, 134(2), 207.
- Marewski, J. N., & Gigerenzer, G. (2012). Heuristic decision making in medicine. Dialogues in clinical neuroscience, 14(1), 77.
- Del Campo, C., Pauser, S., Steiner, E., & Vetschera, R. (2016). Decision making styles and the use of heuristics in decision making. Journal of Business Economics, 86(4), 389-412.
References
Berg, N., & Gigerenzer, G. (2010). As-if behavioral economics: Neoclassical economics in disguise. History of economic ideas, 18 (1), 133-165.
Tversky, A., & Kahneman, D. (1973). Availability: A heuristic for judging frequency and probability. Cognitive Psychology, 5 (2), 207-232.
Kahneman, D. (2011). Thinking, fast and slow. Farrar, Straus and Giroux.
Kahneman, D., & Tversky, A. (1973). On the psychology of prediction. Psychological review, 80 (4), 237.
Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. Econometrica, 47(2), 263-291.
Tversky, A., & Kahneman, D. (1974). Judgment under uncertainty: Heuristics and biases. Science, 185 (4157), 1124-1131.
Tversky, A., & Kahneman, D. (1981). The framing of decisions and the psychology of choice. Science, 211(4481), 453-458.
Kahneman, D., & Tversky, A. (1982). The psychology of preferences. Scientific American, 246 (1), 160-173.
Kőszegi, B., & Rabin, M. (2007). Reference-dependent risk attitudes. American Economic Review, 97 (4), 1047-1073.
Levy, J. S. (1992). An introduction to prospect theory. Political psychology, 171-186.
Ruggeri, K., Alí, S., Berge, M. L., Bertoldo, G., Bjørndal, L. D., Cortijos-Bernabeu, A., Davison, C., Demić, E., Esteban-Serna, C., Friedemann, M., Gibson, S. P., Jarke, H., Karakasheva, R., Khorrami, P. R., Kveder, J., Andersen, T. L., Lofthus, I. S., McGill, L., Nieto, A. E., … Folke, T. (2020). Replicating patterns of prospect theory for decision under risk. Nature Human Behaviour, 4(6), 622-633.
Sokol-Hessner, P., Hsu, M., Curley, N. G., Delgado, M. R., Camerer, C. F., & Phelps, E. A. (2009). Thinking like a trader selectively reduces individuals’ loss aversion. Proceedings of the National Academy of Sciences, 106(13), 5035-5040.
Tom, S. M., Fox, C. R., Trepel, C., & Poldrack, R. A. (2007). The neural basis of loss aversion in decision-making under risk. Science, 315(5811), 515-518.
Tversky, A., & Kahneman, D. (1983). Extensional versus intuitive reasoning: The conjunction fallacy in probability judgment. Psychological Review, 90(4), 293.
Tversky, A., & Kahneman, D. (1992). Advances in prospect theory: Cumulative representation of uncertainty. Journal of Risk and Uncertainty, 5
(4), 297-323.
Walasek, L., & Stewart, N. (2015). How to make loss aversion disappear and reverse: Tests of the decision by sampling origin of loss aversion. Journal of Experimental Psychology: General, 144(1), 7-11.
Yechiam, E. (2019). Acceptable losses: The debatable origins of loss aversion. Psychological Research, 83(7), 1327-1339.
