Prospect Theory: How Users Make Decisions

By Charlotte Nickerson, published May 11, 2022 | Fact Checked by Saul Mcleod, PhD

Prospect theory, originally developed by Amos Tversky and Daniel Kahneman in 1979, is a psychological theory of choice.

It describes how people evaluate their losses and acquire insight in an asymmetric fashion. Unlike the expected utility theory which models the decision making of perfectly rational agents, the prospect theory aims to describe the actual conduct of individuals, and finds application in behavioral finance and economics.

The prospect theory holds that individuals are more influenced by the possibility of a loss than the prospect of an equivalent gain (Tversky & Kahneman, 1981).

Moreover, while a probabilistic deprivation is favored over a sure deprivation, a definite gain is preferred to a probabilistic gain.

Herein, the framing effect becomes manifest when individuals are offered various options within the context of merely one of the frames (Druckman, 2001).

Loss aversion attitude as behavioral bias feeling comparison outline concept. Pain and pleasure gain uneven levels visualization as irrational psychological emotion in economy vector.

Key Points

  • Prospect theory is a theory in behavioral economics that attempts to describe, mathematically, how people’s decisions are influenced by their attitudes toward risk, uncertainty, loss, and gain.
  • Kahneman and Tversky developed prospect theory to account for people's decision-making under risk through a series of controlled "lottery" experiments.
  • The prospect theory challenges the expected utility theory of decision-making, which suggests that people make decisions by calculating the utility (or value) of each possible option. Namely, it accounts for two key human biases in decision-making: loss aversion, and the tendency to weight lower-probability outcomes more heavily than high-probability outcomes.
  • Prospect theory suggests that people make decisions based on the perceived outcomes of those options, rather than the actual utilities.
  • Prospect theory has been found to be a more accurate model of human decision-making than the expected utility theory, and has been largely lauded by the economics and psychology communities, despite its limitations. Its applications range from international relations to whether or not to 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.

In general, it asserts that people are influenced by a systematic inability to evaluate probabilities correctly and in most cases are motivated more strongly by the fear of loss than by the prospect of making the equivalent gain (American Psychological Association).

Daniel Kahneman and Amos Tversky first developed prospect theory as a theory of behavioral economics and behavioral finance in 1973 after conducting a series of controlled studies.

Kahneman and Tversky's prospect theory has been highly influential in the fields of economics, finance, and psychology.

It has also been used to help explain a wide range of phenomena, including why people make suboptimal decisions, how they use mental shortcuts (heuristics) when making decisions, and how different cultures make decisions differently (Kahnneman & Tversky, 1973).

How Prospect Theory Works

Prospect theory is based on a number of key assumptions about human decision making (Kahnneman & Tversky, 1973).

First, it assumes that people are more concerned with avoiding losses than they are with achieving gains. This is known as loss aversion.

Second, it assumes that people view gains and losses relative to a reference point, which is usually their current situation. This means that people are more likely to take risks to avoid losses than they are to make gains.

Finally, prospect theory assumes that people have a hard time evaluating probability accurately, and in most cases they tend to overestimate the likelihood of low-probability events and underestimate the likelihood of high-probability events.

These assumptions lead to a number of predictions about how people will make decisions under conditions of risk and uncertainty.

In general, prospect theory predicts that people will be more risk-averse when it comes to avoiding losses than they will be when it comes to making gains.

This means that people are more likely to take actions that minimize losses and avoid actions that could lead to losses.

Prospect theory also predicts that people will be more likely to take risks when they are experiencing losses than when they are experiencing gains.

This is because people are more concerned with avoiding further losses than they are with making additional gains.

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 (1973) 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.

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 the decisions people make can be biased if they do not have all of the relevant information or if they 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.

The editing stage is also important because it enables people to decide and rank outcomes by their desirability — and even decide which ones are important. 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.

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). 

  • 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.

  • 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).

  • Segregation is the process of separating positive and negative outcomes. This is important because people tend to view gains and losses differently.

  • Cancellation is the process of cancelling 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.

  • 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.

  • 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).

Kahneman and Tversky created a mathematical formula to describe this evaluation phase. This formula, is, in its simplest form:


Prospect theory is largely celebrated in behavioral economics. Nonetheless, it has limitations. For example, critics have called the reference point difficult to determine precisely in any given context.

Many factors can influence what the reference point, as well as make it difficult to define what a gain or loss is (Kozsegi & Rabinn, 2007).

Other critics have said that, while prospect theory seeks to predict what people choose, it does not adequately describe the actual process of decision-making.

Berg and Gigerrennzer (2010) claim that prospect theory is too cognitively intensive to mirror the actual, neurological process of decision-making.

Buying Phone Insurance

When people buy a new phone, they are usually given the option to buy insurance for it in case it breaks or is stolen.

The prospect theory can help people to make this decision by taking into account their bias towards losses, as well as a probability weighting function.

According to the prospect theory, people are more likely to take actions that minimize losses.

This means that if someone believes there is a high chance of their phone breaking or being stolen, they are more likely to buy insurance.

However, people also tend to overreact to small probability events and underreact to large probabilities. This means that even if the chances of a phone breaking or being stolen are low, people may still buy insurance if they believe the consequences of not doing so are severe.

To look at this mathematically, assume that the probability of the insured risk is 10%, the potential loss is 500 pounds, and the insurance premium is 50 pounds.

Applying prospect theory requires first setting a reference point. This could be the current wealth or the worst case (losing 500 pounds).

Setting the frame to the current wealth, the decision would be to either pay 50 pounds for sure. This has a prospect-utility of v(-50),

Or, enter a lottery where the possible outcomes are 0 pounds (90% probability) or -500 pounds (10%) according to the equation, the lower probability (a 10% chance of breaking or losing the phone) would be overweighted.

This means, even though the absolute utility of buying and not buying the insurance are the same (a 50 pound loss, on average), it is likely that the decision maker would learn toward buying insurance for their phone.

Indeed, this overweighting of the less probable outcome may lead someone to pay more than 50 pounds for the insurance, even if it, on a purely economic level, seems like an unwise choice.

War: International Relations

The prospect theory can also be applied to political decisions, and has been effective in the analysis of politics and international relations.

For example, when a government is considering going to war, they must weigh the costs and benefits of doing so.

The expected utility of going to war can be calculated by taking into account the probability of winning, the potential loss if the war is lost, and the value function.

According to prospect theory, people are more likely to take actions that minimize losses.

This means that if a government believes there is a high chance of losing a war, they are less likely to start one.

However, people also tend to overreact to small probability events and underreact to large probabilities.

This means that even if the chances of losing a war are low, governments may still start one if they believe the consequences of not doing so are severe.

To look at this mathematically, assume that the probability of winning a war is 50%, the potential loss if the war is lost is 1000 lives, and the value function is linear.

Setting the frame to the current situation, the decision would be to either start a war with a 50% chance of winning and 1000 lives on the line.

This has a prospect-utility of v(-500), or not go to war. According to prospect theory, the government in this scenario would be more likely to start a war even though the expected utility is negative.

This is because they would overweight the small probability of winning and underweight the large probability of losing.

About the Author

Charlotte Nickerson is a member of the Class of 2024 at Harvard University. Coming from a research background in biology and archeology, Charlotte currently studies how digital and physical space shapes human beliefs, norms, and behaviors and how this can be used to create businesses with greater social impact.

Fact Checking

Content is rigorously reviewed by a team of qualified and experienced fact checkers. Fact checkers review articles for factual accuracy, relevance, and timeliness. We rely on the most current and reputable sources, which are cited in the text and listed at the bottom of each article. Content is fact checked after it has been edited and before publication.

This article has been fact checked by Saul Mcleod, PhD, a qualified psychology teacher with over 17 years' experience of working in further and higher education. He has been published in psychology journals including Clinical Psychology, Social and Personal Relationships, and Social Psychology.

Cite this Article (APA Style)

Nickerson, C. (2022, May 11). Prospect Theory: How Users Make Decisions. Simply Psychology.


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., & Tversky, A. (1973). On the psychology of prediction. Psychological review, 80(4), 237.

Tversky, A., & Kahneman, D. (1974). Judgment under uncertainty: Heuristics and biases. Science, 185(4157), 1124-1131.

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.

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.