<?xml version="1.0" encoding="UTF-8"?>
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  <title>IRIS Macrotipologia:</title>
  <link rel="alternate" href="https://hdl.handle.net/10807/101" />
  <subtitle />
  <id>https://hdl.handle.net/10807/101</id>
  <updated>2026-07-14T01:02:35Z</updated>
  <dc:date>2026-07-14T01:02:35Z</dc:date>
  <entry>
    <title>Is hostile behavior intuitive or deliberative? A Hawk-Dove experiment with a varying harshness of conflict</title>
    <link rel="alternate" href="https://hdl.handle.net/10807/342537" />
    <author>
      <name />
    </author>
    <id>https://hdl.handle.net/10807/342537</id>
    <updated>2026-07-11T00:12:19Z</updated>
    <published>2026-01-01T00:00:00Z</published>
    <summary type="text">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.</summary>
    <dc:date>2026-01-01T00:00:00Z</dc:date>
  </entry>
  <entry>
    <title>Demografías en movimiento: la decolonialidad como proyecto de justicia social</title>
    <link rel="alternate" href="https://hdl.handle.net/10807/342299" />
    <author>
      <name />
    </author>
    <id>https://hdl.handle.net/10807/342299</id>
    <updated>2026-07-08T00:39:11Z</updated>
    <published>2025-01-01T00:00:00Z</published>
    <summary type="text">Titolo: Demografías en movimiento: la decolonialidad como proyecto de justicia social
Autori: Francesca Luana Calia</summary>
    <dc:date>2025-01-01T00:00:00Z</dc:date>
  </entry>
  <entry>
    <title>Tigray, la pace sospesa che può riaccendere il Corno d’Africa</title>
    <link rel="alternate" href="https://hdl.handle.net/10807/342036" />
    <author>
      <name />
    </author>
    <id>https://hdl.handle.net/10807/342036</id>
    <updated>2026-07-07T00:17:39Z</updated>
    <published>2026-01-01T00:00:00Z</published>
    <summary type="text">Titolo: Tigray, la pace sospesa che può riaccendere il Corno d’Africa
Autori: Beatrice Nicolini
Abstract: Il Tigray è la regione più settentrionale dell’Etiopia, un altopiano di circa 53.000 chilometri quadrati che confina a nord con l’Eritrea, a ovest con il Sudan e a est con la regione Afar. “Afar” è il nome del popolo e della regione che abita una delle nove regioni amministrative dell’Etiopia, a est del Tigray. Il termine designa sia l’etnia (il popolo Afar, noto anche come Danakil) sia il territorio, che si estende nel triangolo dell’Afar, una delle zone più calde e basse del pianeta, geologicamente attiva perché si trova alla giunzione di tre placche tettoniche. Il popolo Afar è tradizionalmente nomade-pastorale, di lingua cuscitica, e vive distribuito tra Etiopia, Eritrea e Gibuti. Storicamente è rimasto ai margini dei grandi centri di potere etiopi, e la regione è tra le più povere e scarsamente infrastrutturate del paese.</summary>
    <dc:date>2026-01-01T00:00:00Z</dc:date>
  </entry>
  <entry>
    <title>Optimizing the calibration of agent-based models throughreinforcement learning</title>
    <link rel="alternate" href="https://hdl.handle.net/10807/341944" />
    <author>
      <name />
    </author>
    <id>https://hdl.handle.net/10807/341944</id>
    <updated>2026-07-04T00:14:18Z</updated>
    <published>2026-01-01T00:00:00Z</published>
    <summary type="text">Titolo: Optimizing the calibration of agent-based models throughreinforcement learning
Autori: Delli Gatti, Domenico; Glielmo, Aldo; Gusella, Filippo; Turco, Enrico Maria</summary>
    <dc:date>2026-01-01T00:00:00Z</dc:date>
  </entry>
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