AI Article Synopsis
- The paper examines the challenges of selecting suitable decision-making models for demographic agent-based models, highlighting factors like context, time preference, uncertainty, and the variability in agents' decision strategies.
- It emphasizes the need for empirical, modular, and multi-model approaches in simulations of human decision-making due to the absence of a unified strategy for these complexities.
- The authors propose an iterative method of data collection and simulation, where findings from simulations guide further data gathering, particularly in the context of migration-focused demographic models.
Article Abstract
This paper investigates the issues associated with choosing appropriate models of choice for demographic agent-based models. In particular, we discuss the importance of context, time preference, and dealing with uncertainty in decision modelling, as well as the heterogeneity between agents in their decision-making strategies. The paper concludes by advocating empirically driven, modular, and multi-model approaches to designing simulations of human decision-making, given the lack of an agreed strategy for dealing with any of these issues. Furthermore, we suggest that an iterative process of data collection and simulation experiments, with the latter informing future empirical data collection, should form the basis of such an endeavour. The discussion is illustrated with reference to selected demographic agent-based models, with a focus on migration.
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| http://dx.doi.org/10.1080/00324728.2017.1350280 | DOI Listing |
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