General collections demography model with multiple risks.

Area of Science:

• Heritage Science
• Computational Modeling
• Conservation Science

Background:

• Collections in cultural institutions face complex deterioration processes.
• Understanding object lifespan is crucial for effective preservation strategies.
• Existing models may not fully capture the interplay of continuous and probabilistic degradation.

Purpose of the Study:

• To present a novel Agent-Based Model (ABM) for simulating object deterioration in collections.
• To combine continuous and probabilistic degradation mechanisms within a unified framework.
• To explore emergent behaviors and predict the lifespan of collections.

Main Methods:

• Agent-Based Modeling (ABM) incorporating Monte Carlo sampling.
• Integration of damage functions with ABC framework risk parameters for adverse events.
• Hybrid approach combining continuous and probabilistic degradation simulations.
• Toy application tested using paper as a representative collection material.

Main Results:

• The model successfully simulates the decay in condition of a collection due to combined degradation processes.
• It provides insights into the emergent behavior of collections under various deterioration scenarios.
• The model allows for the exploration of a range of possible lifetimes for collections.

Conclusions:

• The developed ABM offers a universal implementation of Collections Demography principles.
• This hybrid approach enhances the study of collection deterioration and lifespan prediction.
• The model serves as a foundation for future research and identification of conservation gaps.