science

Pseudocertainty effect

Pseudocertainty effect is a science topic covered in the lgStudy science library. This page brings together a partial reference excerpt, illustrations, worked examples, real-world applications and a short study plan, so you can understand Pseudocertainty effect rather than just read about it. In short: In prospect theory, the pseudocertainty effect is the tendency for people to perceive an outcome as certain while it is actually uncertain in multi-stage decision making. The evaluation of the certainty of the outcome in a previous stage of decisions is disregarded when selecting an option in subsequent stages.

Key takeaways

Pseudocertainty effect belongs to science; place it in that map before memorising details.
Learn the definition first, then one example that makes the definition concrete.
Connect Pseudocertainty effect to a quantity you can measure, compute or draw — that is where exam questions come from.
Reproduce the core statement of Pseudocertainty effect from memory before moving on to harder problems.

Reference excerpt

In prospect theory, the pseudocertainty effect is the tendency for people to perceive an outcome as certain while it is actually uncertain in multi-stage decision making. The evaluation of the certainty of the outcome in a previous stage of decisions is disregarded when selecting an option in subsequent stages. Not to be confused with certainty effect, the pseudocertainty effect was discovered from an attempt at providing a normative use of decision theory for the certainty effect by relaxing the cancellation rule.

Background The pseudocertainty effect was illustrated by Daniel Kahneman, who received the Nobel Prize in economics for his work on decision making and decision theory, in collaboration with Amos Tversky. The studies that they researched used real and hypothetical monetary gambles and were often used in undergraduate classrooms and laboratories. Kahneman and Tversky illustrated the pseudocertainty effect by the following examples.

Problem 1 Consider the following two stage game. In the first stage, there is a 75% chance to end the game without winning anything and a 25% chance to move into the second stage. If you reach the second stage, you have a choice between: Which of the following options do you prefer?

A. a sure win of $30 B. 80% chance to win $45 Your choice must be made before the game starts, i.e., before the outcome of the first stage is known. Please indicate the option you prefer.

Problem 2 Which of the following options do you prefer?

C. 25% chance to win $30 D. 20% chance to win $45 Also, this time the participants had to make their choice before the game starts.

Significance Each problem was answered by a different group of respondents. In problem 1, people preferred option A with a rate of 74% over option B with 26%, even though the expected return of option B is higher. In problem 2, people preferred option D with a rate of 58% over option C with a rate of 42%. However, the discrepancy between the answers were surprising because the two problems were designed to have identical outcomes. The choices in problem 2 were designed to be compressed forms of the choices from the two stages of problem 1. (25% chance to move on x 100% = 25%) chance to win $30. The same $7.50 expected return in option A and option C. (25% chance to move on x 80% = 20%) chance to win $45. The same $9.00 expected return in option B and option D. Kahneman and Tversky referred to this incidence as a result of what they called the "pseudocertainty effect". They concluded that when people make choices at later stages of problems they often do not realize that uncertainty at an earlier stage will affect the final outcome. This was clearly observed in the two stage problem shown above in which the problem moved onto the second stage only if the condition of the first stage was met. In the second problem, since individuals have no choice on options in the first stage, individuals tend to discard the first option when evaluating the overall probability of winning money, but just to consider the options in the second stage that individuals have a choice on. This is also known as cancellation, meaning that possible options are yielding to the same outcome thus ignoring decision process in that stage.

See also Allais paradox Certainty effect Loaded question Loss aversion

References

Bibliography Tversky, Amos; Kahneman, Daniel (1981). "The framing of decisions and the psychology of choice" (PDF). Science. 211 (4481): 453–458. Bibcode:1981Sci...211..453T. doi:10.1126/science.7455683. PMID 7455683. S2CID 5643902. Archived from the original (PDF) on 2016-05-07. Tversky, Amos; Kahneman, Daniel (1986). "Rational Choice and the Framing of Decisions" (PDF). The Journal of Business. 59 (S4): S251. CiteSeerX 10.1.1.463.1334. doi:10.1086/296365. Archived from the original (PDF) on 2016-06-01. {{cite journal}}: Cite uses deprecated parameter |citeseerx= (help)

Worked examples

Example 1 — a first encounter with Pseudocertainty effect

Start with the simplest possible case. Write down what Pseudocertainty effect claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In science, the smallest case is usually a single object, a single equation or a single measurement. Check that every symbol or term in your sentence has a meaning in that case.

Example 2 — changing one variable

Take the situation from Example 1 and change exactly one quantity: double it, halve it, or set it to zero. Predict what should happen to Pseudocertainty effect before you calculate. Comparing your prediction with the result is the fastest way to find out whether you understand the idea or only the words.

Example 3 — an exam-style question

Typical questions about Pseudocertainty effect ask you to (a) state it precisely, (b) apply it to given data, and (c) explain a limitation. Practise writing all three answers in under five minutes; the third part is what separates a full-mark answer from an average one.

Applications of Pseudocertainty effect

In research: Pseudocertainty effect appears in science research whenever the underlying quantities have to be modelled precisely. Papers usually cite it as a starting assumption and then explore where it breaks down.
In technology and industry: Engineering practice reuses Pseudocertainty effect in design rules, simulations and safety margins. Knowing the idea lets you read a specification sheet and understand why the numbers look the way they do.
In the classroom: Pseudocertainty effect is common in secondary-school and first-year university syllabi. It links to neighbouring topics Cognitive biases, Prospect theory, Risk, so understanding it makes those chapters shorter.
In everyday life: Look for Pseudocertainty effect outside the textbook — in sport, cooking, traffic, electronics or the sky above you. An example you found yourself is remembered far longer than one you were given.

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How to study Pseudocertainty effect in 20 minutes

Read the reference excerpt below once, without taking notes.
Close the page and write down what Pseudocertainty effect means in your own words.
Compare your version with the excerpt and mark what you missed.
Work through the three examples above with pen and paper.
Explain Pseudocertainty effect out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is Pseudocertainty effect in simple terms?

Why does Pseudocertainty effect matter?

Because it connects several science ideas at once: it gives you a definition you can apply, a quantity you can calculate, and a way to check whether a result is plausible.

How should I study Pseudocertainty effect?

Read the excerpt, restate it from memory, then work through the examples and applications listed on this page. The five-step study plan above takes about twenty minutes.

What does this page cover?

It gives you a compact reference excerpt plus original lgStudy explanations, examples, applications and study material on Pseudocertainty effect.