Behavioral decision research has demonstrated that judgments and decisions of ordinary people and experts are subject to numerous biases. Decision and risk analysis were designed to improve judgments and decisions and to overcome many of these biases. However, when eliciting model components and parameters from decisionmakers or experts, analysts often face the very biases they are trying to help overcome. When these inputs are biased they can seriously reduce the quality of the model and resulting analysis. Some of these biases are due to faulty cognitive processes; some are due to motivations for preferred analysis outcomes. This article identifies the cognitive and motivational biases that are relevant for decision and risk analysis because they can distort analysis inputs and are difficult to correct. We also review and provide guidance about the existing debiasing techniques to overcome these biases. In addition, we describe some biases that are less relevant because they can be corrected by using logic or decomposing the elicitation task. We conclude the article with an agenda for future research.

Cognitive and Motivational Biases in Decision and Risk Analysis.

Summary
Expert judgment informs a variety of important applications in conservation and natural resource management, including threatened species management, environmental impact assessment, and structured decision-making. However, expert judgments can be prone to contextual biases. Structured elicitation protocols mitigate these biases and improve the accuracy and transparency of the resulting judgments. Despite this, the elicitation of expert judgment within conservation and natural resource management remains largely informal. We suggest this may be attributed to financial and practical constraints which are not addressed by many existing structured elicitation protocols.
In this paper, we advocate that structured elicitation protocols must be adopted when expert judgments are used to inform science. In order to motivate a wider adoption of structured elicitation protocols, we outline the IDEA protocol. The protocol improves the accuracy of expert judgments and includes several key steps which may be familiar to many conservation researchers, such as the four-step elicitation, and a modified Delphi procedure (“Investigate”, “Discuss”, “Estimate” and “Aggregate”). It can also incorporate remote elicitation, making structured expert judgment accessible on a modest budget.

The IDEA protocol has recently been outlined in the scientific literature, however, a detailed description has been missing. This paper fills that important gap by clearly outlining each of the steps required to prepare for and undertake an elicitation.

Whilst this paper focuses on the need for the IDEA protocol within conservation and natural resource management, the protocol (and the advice contained in this paper), is applicable to a broad range of scientific domains, as evidenced by its application to biosecurity, engineering, and political forecasting. By clearly outlining the IDEA protocol, we hope that structured protocols will be more widely understood and adopted, resulting in improved judgments and increased transparency when expert judgment is required.


This article is protected by copyright. All rights reserved.

https://besjournals.onlinelibrary.wiley.com/doi/epdf/10.1111/2041-210X.12857

A practical guide to structured expert elicitation using the IDEA protocol

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Expasy, the Swiss Bioinformatics Resource Portal, as designed by its users.

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Papers

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