Improving accuracy and intelligibility of decisions
Mengersen, Kerrie L. & Whittle, Peter (2011) Improving accuracy and intelligibility of decisions. Zeitschrift für Verbraucherschutz und Lebensmittelsicherheit (Journal of Consumer Protection and Food Safety).
Intelligible and accurate risk-based decision-making requires a complex balance of information from different sources, appropriate statistical analysis of this information and consequent intelligent inference and decisions made on the basis of these analyses. Importantly, this requires an explicit acknowledgement of uncertainty in the inputs and outputs of the statistical model. The aim of this paper is to progress a discussion of these issues in the context of several motivating problems related to the wider scope of agricultural production. These problems include biosecurity surveillance design, pest incursion, environmental monitoring and import risk assessment. The information to be integrated includes observational and experimental data, remotely sensed data and expert information. We describe our efforts in addressing these problems using Bayesian models and Bayesian networks. These approaches provide a coherent and transparent framework for modelling complex systems, combining the different information sources, and allowing for uncertainty in inputs and outputs. While the theory underlying Bayesian modelling has a long and well established history, its application is only now becoming more possible for complex problems, due to increased availability of methodological and computational tools. Of course, there are still hurdles and constraints, which we also address through sharing our endeavours and experiences.
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|Item Type:||Journal Article|
|Additional Information:||Invited paper, OECD Conference on 'Decision Making and Science: the balancing of risk based decisions that influence the sustainability of agricultural production'. Berlin, Germany, 7-8 October 2010.|
|Keywords:||decision-making, risk, Bayesian, Uncertainty, invasive alien species, environmental modelling|
|Subjects:||Australian and New Zealand Standard Research Classification > ENVIRONMENTAL SCIENCES (050000) > ECOLOGICAL APPLICATIONS (050100)
Australian and New Zealand Standard Research Classification > BIOLOGICAL SCIENCES (060000) > ECOLOGY (060200)
Australian and New Zealand Standard Research Classification > AGRICULTURAL AND VETERINARY SCIENCES (070000) > AGRICULTURE LAND AND FARM MANAGEMENT (070100)
Australian and New Zealand Standard Research Classification > AGRICULTURAL AND VETERINARY SCIENCES (070000) > ANIMAL PRODUCTION (070200)
Australian and New Zealand Standard Research Classification > AGRICULTURAL AND VETERINARY SCIENCES (070000) > CROP AND PASTURE PRODUCTION (070300)
Australian and New Zealand Standard Research Classification > AGRICULTURAL AND VETERINARY SCIENCES (070000) > HORTICULTURAL PRODUCTION (070600)
|Divisions:||Past > QUT Faculties & Divisions > Faculty of Science and Technology
Past > Institutes > Institute for Sustainable Resources
|Copyright Owner:||Copyright 2011 Bundesamt fu¨r Verbraucherschutz und Lebensmittelsicherheit (BVL)|
This is the author-version of the work.
Conference proceedings published, by Springer Verlag, will be available via SpringerLink. http://www.springerlink.com
|Deposited On:||15 Apr 2011 06:23|
|Last Modified:||15 Apr 2011 06:25|
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