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dc.contributor.author Price, Catherine S
dc.contributor.author Moodley, D
dc.contributor.author Pillay, AW
dc.date.accessioned 2020-07-30T08:52:37Z
dc.date.available 2020-07-30T08:52:37Z
dc.date.issued 2019-12
dc.identifier.citation Price, C.S., Moodley, D. & Pillay, A.W. 2019. Modelling uncertain adaptive decisions: Application to KwaZulu-Natal sugarcane growers. In: South African Forum for Artificial Intelligence Research (FAIR 2019), Cape Town, South Africa, 3-6 December 2019 en_US
dc.identifier.uri https://7e3fccca-0be5-4fbf-a873-20f506a93842.filesusr.com/ugd/7df15f_70e8f258ab1e4833a69cf78f4ec6498f.pdf
dc.identifier.uri https://www.fair2019.org.za/
dc.identifier.uri https://www.fair2019.org.za/programme
dc.identifier.uri http://hdl.handle.net/10204/11525
dc.description Presented in: South African Forum for Artificial Intelligence Research (FAIR 2019), Cape Town, South Africa, 3-6 December 2019 en_US
dc.description.abstract A dynamic Bayesian decision network was developed to model the preharvest burning decision-making processes of sugarcane growers in a KwaZulu-Natal sugarcane supply chain and extends previous work by Price et al. (2018). This model was created using an iterative development approach. This paper recounts the development and validation process of the third version of the model. The model was validated using Pitchforth and Mengersen (2013)’s framework for validating expert elicited Bayesian networks. During this process, growers and cane supply members assessed the model in a focus group by executing the model, and reviewing the results of a prerun scenario. The participants were generally positive about how the model represented their decision-making processes. However, they identified some issues that could be addressed in the next iteration. Dynamic Bayesian decision networks offer a promising approach to modelling adaptive decisions in uncertain conditions. This model can be used to simulate the cognitive mechanism for a grower agent in a simulation of a sugarcane supply chain. en_US
dc.language.iso en en_US
dc.relation.ispartofseries Workflow;23262
dc.subject Dynamic Bayesian decision network en_US
dc.subject Expert model validation en_US
dc.subject Model development en_US
dc.title Modelling uncertain adaptive decisions: Application to KwaZulu-Natal sugarcane growers en_US
dc.type Presentation en_US


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