We show that the two methods produce different results, particularly at high population densities and for increasing interaction complexity (e.g. This work appears to be the first evidence of the importance of scheduling methods on emergent properties for individual-based models and consequently individually-explicit interactions and behaviour in ecology.Cellular learning automata is a combination of cellular automata and learning automata.The schemes shown in the images below are as follows (Cornforth et al.2005): The time-state diagrams below show the differences that are caused by changing the update scheme of the cellular automata model without changing any other parameters.
The clocked scheme could be appropriate for modelling insect colonies, while the self-synchronous scheme could be applied to neural tissue.The consequence of these updating methods has been investigated for cellular automata, but not for IBMs.Here, we assess the two methods for their potential to give different results in a deliberately simple IBM.Note: Many of our articles have direct quotes from sources you can cite, within the Wikipedia article! The state of every cell in the model is updated together, before any of the new states influence other cells. Cellular automata, as with other multi-agent system models, usually treat time as discrete and state updates as occurring synchronously.