The Value of Performance Weights and Discussion in Aggregated Expert Judgments

Hanea, Anca M.; McBride, Marissa; Burgman, Mark A.; Wintle, Bonnie

doi:10.1111/risa.12992

Cited by 47 publications

(57 citation statements)

References 55 publications

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“…They then provided a second score which was mathematically aggregated (Hanea et al, 2018a). The element of discussion is powerful, as the sharing of information between participants has been shown to improve the accuracy of Delphi-style forecasts (Hanea et al, 2018b). The IDEA protocol has also performed well relative to prediction markets in early studies (Hanea et al, 2017).…”

Section: Methodsmentioning

confidence: 99%

Bioengineering horizon scan 2020

Kemp

Adam

Boehm

et al. 2020

eLife

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Horizon scanning is intended to identify the opportunities and threats associated with technological, regulatory and social change. In 2017 some of the present authors conducted a horizon scan for bioengineering (Wintle et al., 2017). Here we report the results of a new horizon scan that is based on inputs from a larger and more international group of 38 participants. The final list of 20 issues includes topics spanning from the political (the regulation of genomic data, increased philanthropic funding and malicious uses of neurochemicals) to the environmental (crops for changing climates and agricultural gene drives). The early identification of such issues is relevant to researchers, policy-makers and the wider public.

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Section: Methodsmentioning

confidence: 99%

Bioengineering horizon scan 2020

Kemp

Adam

Boehm

et al. 2020

eLife

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“…Most validation studies found that when experts revise their estimates, they do so in the direction of the "truth" (e.g. Burgman et al 2011b;Hanea et al 2018).…”

Section: Applicability Of Idea Methodology To Ecological Problemsmentioning

confidence: 99%

Predicting species and community responses to global change in Australian mountain ecosystems using structured expert judgement

Camac

Umbers

Morgan

et al. 2020

Preprint

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Conservation managers are under increasing pressure to make decisions about the allocation of finite resources to protect biodiversity under a changing climate. However, the impacts of climate and global change drivers on species are outpacing our capacity to collect the empirical data necessary to inform these decisions. This is particularly the case in the Australian Alps which has already undergone recent changes in climate and experienced more frequent large-scale bushfires. In lieu of empirical data, we used a structured expert elicitation method (the IDEA protocol) to estimate the abundance and distribution of nine vegetation groups and 89 Australian alpine and subalpine species by the year 2050. Experts predicted that most alpine vegetation communities would decline in extent by 2050; only woodlands and heathlands were predicted to increase in extent. Predicted species-level responses for alpine plants and animals were highly variable and uncertain. In general, alpine plants spanned the range of possible responses, with some expected to increase, decrease or not change in cover. By contrast, almost all animal species were predicted to decline or not change in abundance or elevation range; more species with water-centric life-cycles were expected to decline in abundance than other species. In the face of rapid change and a paucity of data, the method and outcomes outlined here provide a pragmatic and coherent basis upon which to start informing conservation policy and management, although this approach does not diminish the importance of collecting long-term ecological data.

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“…The method generates relatively accurate judgments that usually improve with performance‐based weighting (Hanea et al. ).…”

Section: Improving Quantitative Judgmentsmentioning

confidence: 99%

Untapped potential of collective intelligence in conservation and environmental decision making

Vercammen

Burgman

2019

Conservation Biology

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Environmental decisions are often deferred to groups of experts, committees, or panels to develop climate policy, plan protected areas, or negotiate trade‐offs for biodiversity conservation. There is, however, surprisingly little empirical research on the performance of group decision making related to the environment. We examined examples from a range of different disciplines, demonstrating the emergence of collective intelligence (CI) in the elicitation of quantitative estimates, crowdsourcing applications, and small‐group problem solving. We explored the extent to which similar tools are used in environmental decision making. This revealed important gaps (e.g., a lack of integration of fundamental research in decision‐making practice, absence of systematic evaluation frameworks) that obstruct mainstreaming of CI. By making judicious use of interdisciplinary learning opportunities, CI can be harnessed effectively to improve decision making in conservation and environmental management. To elicit reliable quantitative estimates an understanding of cognitive psychology and to optimize crowdsourcing artificial intelligence tools may need to be incorporated. The business literature offers insights into the importance of soft skills and diversity in team effectiveness. Environmental problems set a challenging and rich testing ground for collective‐intelligence tools and frameworks. We argue this creates an opportunity for significant advancement in decision‐making research and practice.

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The Value of Performance Weights and Discussion in Aggregated Expert Judgments

Cited by 47 publications

References 55 publications

Bioengineering horizon scan 2020

Bioengineering horizon scan 2020

Predicting species and community responses to global change in Australian mountain ecosystems using structured expert judgement

Untapped potential of collective intelligence in conservation and environmental decision making

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