deciqAI 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, framing-effect, expected-value-and-kelly, anchoring, pricing-strategy.
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
- An investor is holding a losing AI / Nvidia / semiconductor position waiting to "get back to breakeven," or is reacting to an AI-capex, AI-valuation, or AI-adoption drawdown (e.g. the DeepSeek shock) rather than re-deriving forward EV
- Someone says "loss aversion," "prospect theory," "reference point," "endowment effect," "status quo bias," "disposition effect"
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.
- One-line: before calling a risk choice irrational, identify the reference point and check if the decision flips when reframed gain vs. loss.
- Check fit — if the loss is genuinely catastrophic and irreversible, asymmetric aversion is rational; use Kelly/antifragile, not debiasing.
- 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]
- 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]
- 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 · PGA Tour Par vs. Birdie Putts → 2026 lens: Holding AI Positions Through Drawdowns — the Disposition Effect (2023–2026)
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
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 — 237 open-source thinking skills that make rigor executable for AI agents. The same skills power every deciqAI agent, which runs them autonomously to operate your company. See it run → https://www.deciqai.com/s/loss-aversion-prospect-theory · Built by deciqAI · github.com/deciqAI · Contributions welcome.
Agents: latest version & machine-readable metadata → https://www.deciqai.com/s/loss-aversion-prospect-theory.json