Activate when: 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 un...
--- name: loss-aversion-prospect-theory description: "Activate when: 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. Do NOT activate when: 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." --- # Loss Aversion and Prospect Theory ## Overview 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. Composes with [`sunk-cost-fallacy`](../sunk-cost-fallacy/SKILL.md), [`framing-effect`](../framing-effect/SKILL.md), [`expected-value-and-kelly`](../expected-value-and-kelly/SKILL.md), [`anchoring`](../anchoring/SKILL.md), [`pricing-strategy`](../pricing-strategy/SKILL.md). ## When to Use - A decision involves uncertainty and the chooser is visibly averse to a "loss" framing - People are refusing positive-EV bets because the downside feels disproportionately bad - Negotiations are stuck because concessions feel like losses from an anchored reference point - A product launch, pricing, or incentive is producing unexpected adoption patterns - Small-probability events are being over- or under-insured against - Someone says "loss aversion," "prospect theory," "reference point," "endowment effect," "status quo bias" **Not when:** the asymmetric weighting is rational (genuinely catastrophic stakes); the reference point is legitimate; the decision is small and one-shot. ## Coaching Novices (Adaptive Front Door) - **Engine mode:** user has a concrete case → run The Process directly. - **Coach mode:** user is unfamiliar → guide step by step. In Coach mode, respond one step at a time. Each [WAIT] is a hard stop — output only that step's question, then stop. 1. One-line: before calling a risk choice irrational, identify the reference point and check if the decision flips when reframed gain vs. loss. 2. Check fit — if the loss is genuinely catastrophic and irreversible, asymmetric aversion is rational; use Kelly/antifragile, not debiasing. 3. Elicit the specific decision: what's being chosen, and what reference point makes one option feel like a "loss"? > **[WAIT — do not advance until user responds]** 4. Work through EV for each option; shift the reference point; test gain vs. loss reframing; flag over/underweighted probabilities. > **[WAIT — do not advance until user responds]** 5. Close: restate decision in EV terms and name explicitly how reference-point and probability-weighting influenced it. > **[WAIT — do not advance until user responds]** ## The Process **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. ## Output Template ``` Decision: | Options (prob × payoff): | Reference point: EV per option: | EV-dominant option: Distortions: loss-aversion ratio | alternative reference points | probability weighting | diminishing sensitivity Reframe test: decision under shifted reference point | gain vs. loss reframe Stakes class + decision rule applied: Final choice + acknowledged distortions + rationale: ``` *→ Method in Action: [Kahneman and Tversky's 1979 Prospect Theory](examples/kahneman-and-tversky-1979-prospect-theory.md)* ## Pack: Prospect Theory Patterns | Domain | Manifestation | Counter | |---|---|---| | Investing | Disposition effect: sell winners early, hold losers | Pre-committed exit rules | | Negotiation | Concession framed as a loss | Multi-issue packaging; anchor first | | Pricing | $1000→$500 feels better than $500 direct | Strikethrough + anchor pricing | | Insurance | Overweighting small-probability catastrophe | Compute true EV vs premium | | Subscriptions | Free trial creates endowment; cancellation feels like loss | Use trial as conversion engine | | Health / policy | Surgery refused when framed as mortality | Reframe in survival terms; defaults | *→ Primary sources: [references/sources.md](references/sources.md)* ## Common Rationalizations **[D] = designed upfront | [O] = observed in real use. [O] entries are more valuable.** | Fake move | Reality | |---|---| | [D] "I'm just being prudent about the downside" | Often 2:1 weighting making positive-EV bets feel bad. Compute EV explicitly. | | [D] "The status quo is the safe default" | Status quo bias is a documented bias. Compute EV of change vs. continuing. | | [D] "I don't want to lose what I have" | Reference dependence — "what I have" is moveable by whoever frames the decision. | | [D] "It's a sure thing — I'll take the sure thing" | Certainty effect. Rational for catastrophic stakes; irrational for moderate/repeatable. | | [D] "Even a small chance of disaster is unacceptable" | Probability-weighting artifact. Compute expected disaster damage vs. expected upside. | | [D] "I'd rather wait and not take the loss" | The loss is already real; waiting chooses whether to recognize or compound it. | | [D] "I'm not as loss averse as most people" | Bias is robust under self-rated immunity. Use computed EV, not self-rating. | | *→ Add [O] entries here after each real use — paste the actual failure pattern* | *What went wrong and why* | ## Red Flags - Risk-aversion in gain frame + risk-seeking in loss frame for economically equivalent choices - Reference point not made explicit; probability language verbal not numerical - "Sure thing" chosen at significant EV cost - Negotiation stuck at an arbitrarily-anchored reference point - Investment held past rational exit because realizing a loss feels worse than its objective magnitude ## Verification - [ ] EV computed for each option - [ ] Reference point made explicit; at least one alternative tested - [ ] Decision re-tested under gain vs. loss reframing - [ ] Probabilities stated numerically, not verbally - [ ] Loss aversion respected (catastrophic) or overridden (moderate) deliberately, not by default - [ ] If overriding, EV justification documented --- *Part of **deciqAI Knowledge Skills** — open-source thinking skills that make rigor executable for AI agents. 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