Each summer in Australia, bushfires burn many hectares of forest, causing deaths, injuries, and destroying property. Agent-based simulation is a powerful tool to test various management strategies on a simulated population, and to raise awareness of the actual population behaviour. But valid results depend on realistic underlying models. This article describes two simulations of the Australian population's behaviour during bushfires designed in previous work, one based on a finite-state machine architecture, the other based on a belief-desire-intention agent architecture. It then proposes several contributions towards more realistic agent-based models of human behaviour: a methodology and tool for easily designing BD Imodels; a number of objective and subjective criteria for comparing agent-based models; a comparison of our two models along these criteria, showing that BDI provides better explanability and understandability of behaviour, makes models easier to extend, and is therefore best adapted; and a discussion of possible extensions of BDI models to further improve their realism.