How agent heterogeneity, model structure and input data determine the performance of an empirical ABM – A real-world case study on residential mobility. (January 2016)

• Record Type: Journal Article
• Authors: Buchmann, Carsten M.; Grossmann, Katrin; Schwarz, Nina
• Is Part Of: Environmental modelling & software. Volume 75(2016:Jan.)
• Page Start: 77
• Page End: 93
• Publication Date: 2016-01
• Subjects: Agent-based model -- Empirical data-driven model -- Residential mobility -- Level of detail -- Complexity -- Model performance and validation; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2015.10.005
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: Agent-based models (ABMs) have tended to become more complicated in recent years, going along with challenges of fully understanding model behaviour. While partly answered using simple and abstract models, the question of the role of different aspects of model detail for controlling model outcomes still has not been explored with empirical ABMs. We therefore use a detailed model built upon an empirical survey of residential mobility to explore the effects of three domains of model detail - agent heterogeneity, structural model detail and detail in the data used - on model performance. Evaluation is done by measuring the ability to predict empirical patterns for different topics, population, relocations and vacancies at different scales. Our diverse results indicate that a good data basis is crucial, heterogeneity strongly controls small-scale patterns and different design aspects must be tested thoroughly. This outcome calls for caution when interpreting the results of such models. Highlights: Evaluation of effect of model detail on performance of empirical agent-based model. Agent heterogeneity controls small-scale patterns, role of model design aspects varies. Correct and detailed data is crucial for predicting patterns. Model performance is measured by relative distances between prediction and data. Approach allows for future use in an optimization framework.