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Collective reinforcement of first impression Bias

Abstract : We propose a simple model of attitude dynamics in which an agent tends to ignore the features which contradict its views. For instance, having received a first very negative feature, the agent may stop to consider any moderately positive feature. We call this phenomenon first impression bias (FIB). We consider a population of agents which are all in contact with a media, communicating randomly chosen features of an object. In some cases, we observe on simulations that FIB is significantly more frequent when the agents interact with each other than when they are only in contact with the media. We design an analytical aggregated model of the global agent-based model behaviour which helps to explain the higher number of FIB due to the interactions.
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Contributor : Migration Irstea Publications <>
Submitted on : Friday, May 15, 2020 - 12:39:47 PM
Last modification on : Tuesday, March 23, 2021 - 5:22:03 PM


  • HAL Id : hal-02588329, version 1
  • IRSTEA : PUB00020378



Guillaume Deffuant, S. Huet. Collective reinforcement of first impression Bias. WCSS'06 The First World Congress on Social Simulation, Kyoto (Japon) 21-25 aout 2006, 2006, pp.8. ⟨hal-02588329⟩



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