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UID:event-13075@amse-aixmarseille.fr
DTSTAMP:20260414T002403Z
CREATED:20260414T002403Z
LAST-MODIFIED:20260414T002403Z
STATUS:CONFIRMED
SEQUENCE:0
SUMMARY:big data and econometrics seminar - Avner Seror
DTSTART:20260324T130000Z
DTEND:20260324T143000Z
DESCRIPTION:We propose a Random Rule Model (RRM) in which behavior is gener
 ated by switching among a small library of transparent\, parameter-free dec
 ision rules. A differentiable gate learns environment-dependent rule propen
 sities\, producing an interpretable mixture over named procedures. We devel
 op a global identification theory based on two verifiable conditions on the
  observed support. Applied to $10{\,}000$ binary lottery problems\, rule-ga
 ting substantially outperforms structured neural benchmarks based on expect
 ed utility and prospect theory\, approaching the most flexible benchmark wh
 ile remaining highly restrictive under permutation-fit tests\, and retains 
 predictive content on an independent dataset. Mechanism diagnostics reveal 
 that extreme-outcome screening\, salience\, and attention rules carry the l
 argest responsibility weights\, with systematic shifts along tradeoff compl
 exity and dispersion asymmetry. Robustness checks confirm that the findings
  are not driven by the ex~ante library choice\, marginal dominance relation
 ships\, or the availability of additional regressors.\\n\\nContact: Sulliva
 n Hué : sullivan.hue[at]univ-amu.frMichel Lubrano : michel.lubrano[at]univ
 -amu.fr\n\nPlus d'informations: https://amse-aixmarseille.fr/fr/evenements/
 avner-seror
LOCATION:Îlot Bernard du Bois - Salle 11\, AMU - AMSE\, 5-9 boulevard Maur
 ice Bourdet\, 13001 Marseille
URL;VALUE=URI:https://amse-aixmarseille.fr/fr/evenements/avner-seror
CONTACT:Sullivan Hué : sullivan.hue[at]univ-amu.frMichel Lubrano : michel.
 lubrano[at]univ-amu.fr
TRANSP:OPAQUE
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