Embracing Uncertainty: The Interface of Bayesian Statistics and 
Cognitive Psychology

Anderson, Judith H.

doi:10.5751/es-00043-020102

Cited by 112 publications

(47 citation statements)

References 57 publications

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“…For example, numerous studies have shown that all individuals find it difficult to assign numerical probabilities to judgements (e.g. Kahneman et al, 1982, Anderson, 1998. This is because heuristics (rules of thumb) are used to assess probability, and these introduce bias.…”

mentioning

confidence: 99%

Dynamics of uncertainty in geological interpretation

Polson

Curtis

2010

JGS

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Abstract:Interpretation of geological data is based on both personal judgement and previous experience of related scenarios. In combining such information geologists employ heuristics (rules of thumb), and are therefore subject to biases that are well known in cognitive psychology and are common to all expert judgements. Here we analyse dynamic uncertainty in an evolving geological interpretation. Through a well-designed elicitation process we show how the inclusion of multiple experts influences interpretational bias. In particular, group convergence of opinion is observed, and we show how this can be differentiated from 'herding' behaviour similar to that observed in economic bubbles by forcing a consensus to be reached. Thus we can identify when and why the judgement of a single geological expert should be treated with caution. This process can be applied to any geological interpretational scenario

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confidence: 99%

Dynamics of uncertainty in geological interpretation

Polson

Curtis

2010

JGS

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“…Therefore, translation of estimates into formats of direct use to the decision-making process should be used when possible (e.g. Hunt et al 2001 Guillaume et al 2010) probabilities tends to be biased, such that it is better to communicate them as frequencies, even if the probability refers to the likelihood of a one-off event (Anderson 1998). Rather than providing tables of probabilities, they may be better visualised (Barnett et al 2012) by using maps or graphs.…”

Section: Communicating and Using Predictions Of Uncertain Outcomesmentioning

confidence: 99%

Methods for Exploring Uncertainty in Groundwater Management Predictions

Guillaume

Hunt

Comunian

et al. 2016

Integrated Groundwater Management

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Models of groundwater systems help to integrate knowledge about the natural and human system covering different spatial and temporal scales, often from multiple disciplines, in order to address a range of issues of concern to various stakeholders. A model is simply a tool to express what we think we know. Uncertainty, due to lack of knowledge or natural variability, means that there are always alternative models that may need to be considered. This chapter provides an overview of uncertainty in models and in the definition of a problem to model, highlights approaches to communicating and using predictions of uncertain outcomes and summarises commonly used methods to explore uncertainty in groundwater management predictions. It is intended to raise awareness of how alternative models and hence uncertainty can be explored in order to facilitate the integration of these techniques with groundwater management.

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“…Thus, even this simple change in how scientists present results about probabilities could enhance their communication with decision makers and the public. Anderson (1998Anderson ( , 2001 Holt and Peterman (2006).…”

Section: Improving Communication About Uncertaintiesmentioning

confidence: 99%

Consideration of uncertainties in environmental science and management with examples from Pacific salmon

Peterman

2015

Preprint

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An informal review of the history of new quantitative methods in environmental science, including environmental risk assessment, shows about a 10- to 20-year lag in wide acceptance of such methods by management agencies. To reduce that lag time as innovative methods continue to emerge, environmental scientists will need to work much more intensively with communications specialists on better ways to explain risk analyses and decision-making strategies to non-technical decision makers and the public. Four key uncertainties make such communication difficult: (1) natural variability in both physical and biological processes, (2) imperfect data arising from observation error (i.e., measurement error), (3) incomplete understanding of an environmental system's structure and dynamics, and (4) outcome uncertainty (deviations between realized outcomes and management targets). These uncertainties create risks -- risks to natural populations as well as to people who use them. Examples of these four sources of uncertainty are presented here for Pacific salmon (Oncorhynchus spp.). One promising framework for explicitly taking such uncertainties into account was initially developed in the early 1990s by scientific advisors to the International Whaling Commission. They built stochastic models, which essentially were comprehensive formal decision analyses, to derive management procedures (i.e., sampling designs for collecting data, methods to analyze those data, and state-dependent harvest-control rules for use by managers) that were robust to all the uncertainties considered. This method of "Management Strategy Evaluation" or "Management Procedure Evaluation" is now considered the "gold standard" for conducting risk assessments and making risk-management decisions in marine fisheries.

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Embracing Uncertainty: The Interface of Bayesian Statistics and Cognitive Psychology

Abstract: Abstract•

Cited by 112 publications

References 57 publications

Dynamics of uncertainty in geological interpretation

Dynamics of uncertainty in geological interpretation

Methods for Exploring Uncertainty in Groundwater Management Predictions

Consideration of uncertainties in environmental science and management with examples from Pacific salmon

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