Improving accuracy and intelligibility of decisions


Autoria(s): Mengersen, Kerrie L.; Whittle, Peter
Data(s)

08/04/2011

Resumo

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.

Identificador

http://eprints.qut.edu.au/41322/

Publicador

Springer

Relação

DOI:10.1007/s00003-011-0694-3

Mengersen, Kerrie L. & Whittle, Peter (2011) Improving accuracy and intelligibility of decisions. Zeitschrift für Verbraucherschutz und Lebensmittelsicherheit (Journal of Consumer Protection and Food Safety).

Direitos

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

Fonte

Faculty of Science and Technology; Institute for Sustainable Resources

Palavras-Chave #050100 ECOLOGICAL APPLICATIONS #060200 ECOLOGY #070100 AGRICULTURE LAND AND FARM MANAGEMENT #070200 ANIMAL PRODUCTION #070300 CROP AND PASTURE PRODUCTION #070600 HORTICULTURAL PRODUCTION #decision-making #risk #Bayesian #Uncertainty #invasive alien species #environmental modelling
Tipo

Journal Article