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General collections demography model with multiple risks

Grau-Bové, Josep; Andrews, Miriam; (2025) General collections demography model with multiple risks. Humanities and Social Sciences Communications , 12 (1) , Article 889. 10.1057/s41599-025-05325-6. Green open access

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Abstract

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.

Type: Article
Title: General collections demography model with multiple risks
Open access status: An open access version is available from UCL Discovery
DOI: 10.1057/s41599-025-05325-6
Publisher version: https://doi.org/10.1057/s41599-025-05325-6
Language: English
Additional information: © 2025 Springer Nature Limited. This article is licensed under a Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of the Built Environment
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of the Built Environment > Bartlett School Env, Energy and Resources
URI: https://discovery.ucl.ac.uk/id/eprint/10210392
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