pith:RSPXSJF5
How Many Mechanisms? Measuring Parsimony in Risky Choice
Most subjects' risky lottery choices concentrate on a few simple rules like salience and regret rather than full utility maximization.
arxiv:2601.02964 v4 · 2026-01-06 · econ.GN · q-fin.EC
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Claims
for a majority of subjects, observed concentration exceeds what standard utility models generate on the same menus. The concentration organizes around salience thinking, modal-payoff focusing, and regret.
That the chosen library of six rules is representative of the main mechanisms driving risky choices and that the index is not sensitive to the specific menus or rule definitions used.
Risky choice data from lottery experiments exhibit more parsimony than standard utility models, with concentration around salience thinking, modal-payoff focusing, and regret.
Formal links
Receipt and verification
| First computed | 2026-05-29T02:05:38.954058Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
8c9f7924bd962912f306da6ac10549bf9a27b4cc2720d63f9fc2196c567939cb
Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/RSPXSJF5SYURF4YG3JVMCBKJX6 \
| jq -c '.canonical_record' \
| python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 8c9f7924bd962912f306da6ac10549bf9a27b4cc2720d63f9fc2196c567939cb
Canonical record JSON
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