Minimising biases in expert elicitations to inform environmental management: Case studies from environmental flows in Australia. (February 2018)

• Record Type: Journal Article
• Authors: de Little, Siobhan C.; Casas-Mulet, Roser; Patulny, Lisa; Wand, Joanna; Miller, Kimberly A.; Fidler, Fiona; Stewardson, Michael J.; Webb, J. Angus
• Is Part Of: Environmental modelling & software. Volume 100(2018)
• Page Start: 146
• Page End: 158
• Publication Date: 2018-02
• Subjects: Expert elicitation -- Decision making -- Bayesian networks -- Riparian vegetation -- Environmental flow management -- Bias; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2017.11.020
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: Environmental managers often do not have sufficient empirical data to inform decisions, and instead must rely on expert predictions. However, the informal methods often used to gather expert opinions are prone to cognitive and motivational biases. We developed a structured elicitation protocol, where opinions are directly incorporated into Bayesian Network (BBN) models. The 4-stage protocol includes approaches to minimise biases during pre-elicitation, workshop facilitation and output analysis; and results in a fully functional BBN model. We illustrate our protocol using examples from environmental flow management in Australia, presenting models of vegetation responses to changes in riverine flow regimes. The reliance on expert opinion and the contested nature of many environmental management decisions mean that our structured elicitation protocol is potentially of great value for developing robust environmental recommendations. This method also lends itself to effective adaptive management, because the expert-populated ecological response models can be readily updated with field data. Highlights: We present a framework for expert elicitation that minimizes expert biases. It brings together three hitherto separate methods to maximize effectiveness. It results in a fully functional Bayesian network model to inform decision making. We illustrate the framework using examples from environmental water management. The model can be updated with empirical data within an adaptive management cycle.