General collections demography model with multiple risks.

This paper presents an Agent-Based Model (ABM) with Monte Carlo sampling, designed to simulate the deterioration processes within a population of objects over time. The model incorporates damage functions with the risk parameters of the ABC framework to simulate adverse events. As a result, it combi...

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Publicado en:Humanities & Social Sciences Communications Vol. 12; no. 1; pp. 1 - 12
Autores principales: Grau-Bové, Josep, Andrews, Miriam
Formato: Artículo
Publicado: Springer Nature 6/21/2025
Materias:
Acceso en línea:Ver este registro en EBSCOhost
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      pub: Springer Nature
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        10.1057/s41599-025-05325-6
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          Grau-Bové, Josep
          Andrews, Miriam
        affil: https://ror.org/02jx3x895 University College London, London, UK
      su:
        Evidence gaps
        Cultural values
        Demography
        Collections
        Archives
      sug:
        subj:
          Evidence gaps
          Cultural values
          Demography
          Collections
          Archives
      ab: This paper presents an Agent-Based Model (ABM) with Monte Carlo sampling, designed to simulate the deterioration processes within a population of objects over time. The model incorporates damage functions with the risk parameters of the ABC framework to simulate adverse events. As a result, it combines continuous and probabilistic degradation. This hybrid approach makes it possible to study the emergent behavior of the system and explore the range of possible lifetimes of collections with cultural value or scientific interest within galleries, museums, archives or libraries. A toy application of the model is tested with paper, with the main outcome of the model being the decay in condition of a collection as a consequence of all the combined degradation processes. The model is based on six hypotheses that are described for further testing. This paper presents a first attempt at a universal implementation of Collections Demography principles, with the hope that it will generate discussion and the identification of research gaps.
      pubtype: Academic Journal
      doctype: Article
      src: R
    language: English
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          year: 2025
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