Prospect Theory

Prospect theory, developed by Daniel Kahneman and Amos Tversky, describes how people really choose under risk, as opposed to how expected-value theory says they should. Its core findings: people judge outcomes as gains or losses from a reference point, not as final wealth; losses hurt roughly twice as much as equal gains please (loss aversion); sensitivity diminishes as amounts grow; small probabilities are overweighted and moderate-to-large ones underweighted; and the same choice framed differently produces different decisions. Using it means spotting these patterns in a decision, whether yours or someone else's, and correcting or designing for them.

Input required

• The choice and the options, with outcomes and probabilities.
• The reference point the decision-maker is using (status quo, expectation, sunk cost, a peer's result).
• How the options are framed — as gains or losses, in what wording.
• Who is deciding and for whom — oneself, a team, customers.

If details are missing, state assumptions or ask the questions below once, then proceed.

Questions to ask first

Ask in one batch of at most five questions, skipping any already answered, then proceed:

1. What are the options, with outcomes and probabilities?
2. What does the decider treat as "normal" or "break-even" right now?
3. Is any option described as avoiding a loss versus gaining a benefit?
4. Is there a sunk cost, a deadline, or a recent loss influencing the choice?
5. Is the aim to improve your own decision or to design an offer or message for others?

The core ideas

• Reference dependence — outcomes are coded as gains or losses relative to a reference point; moving the reference point changes the choice.
• Loss aversion — a loss looms larger than an equal gain, by about 2:1 in many studies.
• Diminishing sensitivity — the difference between $0 and $100 feels larger than between $1,000 and $1,100.
• Risk attitudes flip — typically risk-averse for gains (take the sure $500 over a 50% shot at $1,000) and risk-seeking for losses (gamble to avoid a sure loss).
• Probability weighting — tiny chances are overweighted (lottery tickets, insurance for rare events); near-certainty is underweighted; the certainty effect makes a sure thing special.
• Framing — "90% survival" versus "10% mortality" gives the same facts but different choices.
• Related effects — endowment effect (valuing what one owns more), sunk-cost behavior (throwing good money after bad), status quo bias, and mental accounting.

Method

1. Restate the decision in neutral terms: final outcomes and probabilities, no gain/loss language.
2. Identify the reference point being used and which outcomes feel like gains and which like losses.
3. Find the distortions. Check for loss aversion keeping the status quo, risk-seeking to dodge a sure loss, overweighted rare events, and sunk costs steering the choice.
4. Re-frame several ways. Evaluate the options with different reference points (current state, goal, outside view) and as final totals. If the preference flips, the framing is driving it.
5. Compare with the rational benchmark. Compute the Expected Value Analysis and ask whether the preferred option differs, and whether the difference is justified by real risk limits, not just feeling.
6. Decide on purpose. Keep the intuition if it reflects real values (a ruinous loss deserves extra weight); correct it if it reflects framing.
7. For designing offers or communications, apply the findings honestly: present information clearly, make defaults sensible, and avoid exploiting biases against the audience's interests.

Output format

• Neutral restatement of the choice.
• Reference point and frame currently in play.
• Distortions detected, each with how it shows up.
• Reframed comparison — does the preference change?
• Benchmark — expected value and risk, versus the intuitive choice.
• Recommendation — what to do, and the reasoning that survives reframing.

Ground rules

• Describe, don't scold. These patterns are normal human psychology, not stupidity.
• Not every deviation is an error. Loss aversion can be rational when a loss threatens survival.
• Effects vary. The 2:1 ratio is an average from experiments; individuals, stakes, and cultures differ.
• Don't manipulate. Use the theory to inform and to remove distortions, not to push people into choices against their interests.

Self-check before answering

• Was the choice restated without gain/loss framing?
• Were reference point, loss aversion, probability weighting, and sunk costs each considered?
• Was the preference tested under at least one alternative frame?
• Is the advice separate from the description of the bias?

Common pitfalls

• Treating the status quo as risk-free. Doing nothing has costs.
• Sunk-cost chasing — continuing because of past investment.
• Overweighting dramatic, rare events — and neglecting common ones.
• Gambling to avoid a sure loss — turning a small loss into a large one.
• Framing blindness — believing the wording did not matter.
• Overgeneralizing the research — applying lab averages to a specific person as if they were laws.

Example prompt

We've spent $400k on a project that's behind schedule. Management says "we can't stop now." Use prospect theory to explain what biases may be at work, re-frame the choice, and tell me what the decision looks like with sunk costs ignored.

Learn more

• Expected Value Analysis for the rational benchmark.
• Decision Analysis for the end-to-end decision process.
• Game Theory for strategic choices among interacting parties.
• Regret Minimization for decisions shaped by anticipated regret.
• Decision Journal for catching your own patterns over time.
• Premortem for countering optimism and sunk-cost commitment.