Abstract
Notionally objective probabilistic risk models, built around ideas of cause and effect, are used to predict impacts and evaluate trade-offs. In this paper, we focus on the use of expert judgement to fill gaps left by insufficient data and understanding. Psychological and contextual phenomena such as anchoring, availability bias, confirmation bias and overconfidence are pervasive and have powerful effects on individual judgements. Research across a range of fields has found that groups have access to more diverse information and ways of thinking about problems, and routinely outperform credentialled individuals on judgement and prediction tasks. In structured group elicitation, individuals make initial independent judgements, opinions are respected, participants consider the judgements made by others, and they may have the opportunity to reconsider and revise their initial estimates. Estimates may be aggregated using behavioural, mathematical or combined approaches. In contrast, mathematical modelers have been slower to accept that the host of psychological frailties and contextual biases that afflict judgements about parameters and events may also influence model assumptions and structures. Few, if any, quantitative risk analyses embrace sources of uncertainty comprehensively. However, several recent innovations aim to anticipate behavioural and social biases in model construction and to mitigate their effects. In this paper, we outline approaches to eliciting and combining alternative ideas of cause and effect. We discuss the translation of ideas into equations and assumptions, assessing the potential for psychological and social factors to affect the construction of models. We outline the strengths and weaknesses of recent advances in structured, group-based model construction that may accommodate a variety of understandings about cause and effect.
Highlights
Quantitative models often drive the risk analyses that estimate the probability of adverse events and assess their impacts on stakeholders
We describe novel approaches to solving these challenges that use collaborative elicitation and aggregation of conceptual and mathematical model structures that emulate the systems developed for structured elicitation of model parameters
For the last 50 years, thinking about the psychology and social context of expert judgement has led to a revolution in the ways in which expert judgements are obtained and aggregated [5, 10, 12, 13, 60]
Summary
Quantitative models often drive the risk analyses that estimate the probability of adverse events and assess their impacts on stakeholders. A single analyst or analytical team can, explore alternative model structural assumptions through sensitivity analysis, most straight-forwardly by varying the structure of the model, and its input parameter values and assessing changes in the response variables This approach is limited implicitly by the scope of the analyst’s perspectives on the problem, as noted above. These groups could first explore ideas of cause and effect iteratively using mental models, before building mathematical models, as outlined above In this approach after building a model, analysts participate in formal, facilitated, Delphi-like structured discussions [9] to compare results, assess common features, discuss differences, and share information, thereby generating insights into the problem and its solutions. The research needs to define best practice, ideally comparing and contrasting different processes and recommending approaches to model development for decision making that perform best and that are fit for purpose
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