Behavioral economics
Skill alzadjaliaafra-hash/murshidi-knowledge-layer/models/behavioral-economics
Modular, fine-tuning-ready knowledge architecture for financial-domain LLMs — 9 domain models, each an activatable skill with knowledge corpus, instruction dataset, and held-out evals.
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Behavioral economics framework — the twelve cognitive biases that drive real financial decisions (loss aversion, anchoring, availability, confirmation, sunk cost, overconfidence, herding, framing, recency, endowment, hyperbolic discounting, illusion of control), with application patterns for advisory communication, incentive design, and platform UX. Activate with /behav, "behavioral", "bias", "client psychology", or "decision design".
SKILL.md
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Behavioral Economics in Finance
A working model of how humans actually make financial decisions — not how they should. Used to predict counterparty behaviour, structure incentives, frame advisory communication, and design platforms that align with human nature rather than fight it.
The Twelve Biases
| # | Bias | Core principle | Financial manifestation |
|---|---|---|---|
| 1 | Loss aversion | Losses hurt ~2× as much as equivalent gains | Holding losers too long; delaying profit-taking |
| 2 | Anchoring | The first number becomes the reference point | Opening offers frame entire negotiations |
| 3 | Availability heuristic | Recent/memorable events are overweighted | Post-crash over-caution; trend-chasing |
| 4 | Confirmation bias | People seek confirming information | Warning signs on existing holdings ignored |
| 5 | Sunk cost fallacy | Past investment drives continued commitment | Underwater positions held; failed projects continued |
| 6 | Overconfidence | Knowledge and predictive skill overestimated | Overtrading; concentrated bets |
| 7 | Herding | Safety is sought in crowd behaviour | Momentum bubbles; capitulation selling |
| 8 | Framing effect | Presentation changes the decision | Identical economics accepted or rejected by wording |
| 9 | Recency bias | Latest data dominates the long series | Short-window extrapolation of returns |
| 10 | Endowment effect | Owned assets are overvalued | Refusal to sell at fair market prices |
| 11 | Hyperbolic discounting | Near rewards dominate far larger ones | Under-saving; short-term deal preference |
| 12 | Illusion of control | Influence over random outcomes is overestimated | Excessive active management; timing conviction |
Application Patterns
Advisory communication. Lead with loss framing where action is needed ("protect what you have earned" outperforms "capture more upside"). Control the anchor — always present the first number. Introduce counter-evidence only after acknowledging the client's existing thesis (confirmation-bias-aware sequencing). Replace point estimates with probability ranges to calibrate overconfidence. Separate past from future explicitly to defuse sunk cost ("the only question is forward return from today").
Incentive & platform design. Make forward-looking metrics the visual default (against sunk cost and recency). Use "typical range" displays to anchor expectations honestly. Design exits that feel like securing gains rather than admitting losses. Counter herding with base-rate displays; counter availability with long-window context charts. Structure commitment devices against hyperbolic discounting (auto-escalation, default enrolment).
Ethical boundary. These mechanics are applied to align decisions with the decision-maker's own stated objectives — de-biasing, not manipulation. Any application that exploits a bias against the client's interest is out of scope.
Output Protocol
Every behavioral analysis names the operative bias, the observable evidence for it, the reframe that neutralises it, and the design or communication change that operationalises the reframe.
Resources
knowledge/methodology.md— full bias catalogue with application detaildataset/train.jsonl— instruction-tuning pairseval/eval.jsonl— held-out evaluation questions