Outlining some requirements for synthetic populations to initialise agent-based models
Nicholas Roxburgh (The James Hutton Institute)
Rocco Paolillo (Consiglio Nazionale delle Ricerche (CNR))
T. Filatova (TU Delft - Policy Analysis)
Clémentine Cottineau-Mugadza (TU Delft - Urban Studies)
Mario Paolucci (Consiglio Nazionale delle Ricerche (CNR))
Gary Polhill (The James Hutton Institute)
More Info
expand_more
Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.
Abstract
We propose a wish list of features that would greatly enhance population synthesis methods from the perspective of agent-based modelling. The challenge of synthesising appropriate populations is heightened in agent-based modelling by the emphasis on complexity, which requires accounting for a wide array of features. These often include, but are not limited to: attributes of agents, their location in space, the ways they make decisions and their behavioural dynamics. In the real-world, these aspects of everyday human life can be deeply interconnected, with these associations being highly consequential in shaping outcomes. Initialising synthetic populations in ways that fail to respect these covariances can therefore compromise model efficacy, potentially leading to biased and inaccurate simulation outcomes.
help_outline