https://hdl.handle.net/10807/196 2026-08-03T02:47:14Z https://hdl.handle.net/10807/343496 Titolo: Model-dependent vs Robust predictors of risk tolerance: a dual-model variable importance approach Autori: Lippi, Andrea; Elena, Calegari; Simone, Rossi Abstract: Empirical analyses of investor risk tolerance typically rely on a single modelling framework, leaving open the question of whether the identified determinants are genuine empirical regularities or artefacts of specification choices. This paper addresses this question by proposing a dual-model framework that combines an interpretable ordered probit model with an ordinal forest, a machine learning method designed for ordinal outcomes, and applies it to survey data from 1,549 investors in Finland, Germany, Italy, and Spain. Rather than selecting a single best-performing model, the framework uses the two approaches jointly to distinguish determinants that are robust to modelling assumptions from those that are model-dependent. To enable a direct comparison of variable relevance across models, we introduce likelihood-based and permutation-based measures of variable importance evaluated within a unified five-fold cross-validation procedure and scored using the Ranked Probability Score, a proper scoring rule for ordinal outcomes. The results reveal a clear robustness gradient. Age and country of residence emerge as the only high-robustness determinants, with stable importance rankings across all measures and negligible fold-to-fold variability. Education, occupation, and income form a second tier of consistently relevant predictors. By contrast, gender and children display notable divergence across models, indicating that their estimated effects are partially sensitive to functional-form assumptions. Financial literacy, marital status, and home ownership show consistently low and unstable importance across all measures. These findings have direct implications for portfolio suitability assessment: determinants with high robustness provide a reliable basis for investor classification, while model-sensitive variables introduce classification risk, the probability that the same investor receives different risk category assignments under alternative specifications. 2026-01-01T00:00:00Z https://hdl.handle.net/10807/342537 Titolo: Is hostile behavior intuitive or deliberative? A Hawk-Dove experiment with a varying harshness of conflict Autori: Ennio Bilancini; Leonardo Boncinelli; Pablo Marcos-Prieto; Chiara Nardi Abstract: Using a one-shot Hawk–Dove game, we experimentally investigate the effect of different cognitive modes—intuitive (induced by Time Pressure), deliberative (by Time Delay), and motivated deliberative (by Time Delay combined with a written motivation)—on the propensity to behave hostilely (i.e., to play Hawk). We also examine whether cognitive modes affect responsiveness to payoff incentives by varying the harshness of conflict. Our results show that intuition significantly increases the likelihood of hostile behavior, while motivated deliberation reduces it. The harshness of conflict does not significantly affect behavior, and we find no evidence that its effect differs across cognitive manipulations. However, when restricting attention to subjects in the pooled delay conditions, the effect of harshness becomes statistically significant, indicating that responsiveness to payoff incentives may require deliberation. Consistently, we find that deliberation increases the likelihood that subjects best respond to their own beliefs. 2026-01-01T00:00:00Z https://hdl.handle.net/10807/341940 Titolo: L'industria europea delle imprese di investimento Autori: Beccalli, Elena 2001-01-01T00:00:00Z https://hdl.handle.net/10807/341776 Titolo: Governing Through Division Autori: Giovanna Invernizzi; Federico Trombetta 2026-01-01T00:00:00Z