The Prelec Probability Weighting Function, a cornerstone of Behavioral Economics, describes how individuals assign subjective weights to objective probabilities, typically overweighting low-probability events and underweighting high-probability events. The aim of this dissertation is to estimate the Prelec parameters (particularly α) using data on insurance policies and the related premiums purchased by residents of the Bassano del Grappa area, with particular attention to heterogeneity across income groups. Based on the empirical results, it will also be possible to derive and construct behavioral profiles and compare them with the findings of previous studies.
La Prelec Probability Weighting Function, pilastro dell’Economia Comportamentale, descrive la tendenza degli individui ad attribuire pesi soggettivi alle probabilità oggettive, sovrastimando in genere gli eventi caratterizzati da basse probabilità di accadimento e sottostimando quelli molto probabili. L’elaborato si propone di stimare i parametri di Prelec (in particolare α) a partire dai dati relativi alle polizze assicurative, e i conseguenti premi pagati, sottoscritte dagli abitanti del bassanese, prestando attenzione all’eterogeneità presente tra fasce di reddito. Sulla base dei risultati ottenuti, sarà possibile costruire degli archetipi comportamentali e confrontarli con le evidenze fornite dalla letteratura esistente.
Dai premi assicurativi alla Cumulative Prospect Theory: stima della Prelec Probability Weighting Function nel bassanese
LUNARDON, GIONA
2025/2026
Abstract
The Prelec Probability Weighting Function, a cornerstone of Behavioral Economics, describes how individuals assign subjective weights to objective probabilities, typically overweighting low-probability events and underweighting high-probability events. The aim of this dissertation is to estimate the Prelec parameters (particularly α) using data on insurance policies and the related premiums purchased by residents of the Bassano del Grappa area, with particular attention to heterogeneity across income groups. Based on the empirical results, it will also be possible to derive and construct behavioral profiles and compare them with the findings of previous studies.| File | Dimensione | Formato | |
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https://hdl.handle.net/20.500.12608/112553