── ── Mental model
Loss Aversion and Prospect Theory
People evaluate outcomes relative to a reference point (not absolute wealth), weight losses ~2.25x as heavily as equivalent gains, are risk-averse in gain frames and risk-seeking in loss frames, and distort probabilities (overweighting small, underweighting large). The same physical outcome feels different depending on framing — this skill diagnoses and corrects that asymmetry.
How it works
Step 1 — Specify decision: options, probability × payoff distributions, reference point (explicit or implicit). Step 2 — Compute EV: Σ(probability × payoff) for each option; identify EV-dominant choice. Step 3 — Identify distortions: loss aversion (losses weighted >1x gains?), reference dependence (alternative reference points?), probability weighting (small overweighted? large underweighted?), diminishing sensitivity (large outcomes compressed?). Step 4 — Reframe and re-test: shift the reference point; restate as gain vs. loss; express probabilities numerically. If the decision flips, prospect-theory distortions are doing meaningful work. Step 5 — Choose decision rule: catastrophic+irreversible → respect loss aversion · moderate+repeatable → maximize EV · large+reversible → Kelly criterion · one-shot → add regret minimization. Step 6 — Document: chosen option, its EV, why it dominates, and which distortions were acknowledged/overridden.
When to use it
- someone says 'I don't want to lose what I have', a deal is stuck because a concession feels like a loss, a pricing or incentive change gets unexpected pushback, someone is refusing a bet that looks positive in expected value, a free trial cancels at high rate
When not to use it
the loss being avoided is genuinely catastrophic and irreversible (use Kelly/antifragile instead); the decision is small and one-shot where EV approximation is acceptable.
Worked example
Kahneman and Tversky's 1979 Prospect Theory
The 1979 paper that founded prospect theory was the culmination of a decade of work by Daniel Kahneman and Amos Tversky at Hebrew University, then at the Center for Advanced Study in the Behavioral Sciences at Stanford. The pair had already published the seminal "Judgment under Uncertainty: Heuristics and Biases" (1974, Science), which established availability, representativeness, and anchoring as systematic violations of probability theory. The 1979 paper extended the program to decisions under risk.
Install this skill (free, MIT)
npx skills add deciqAI/knowledge-skills