title: Cognitive and Motivational Biases in Decision and Risk Analysis.
abstract: 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.
authors: School of Business and Economics, Loughborough University, LE11 3TU, Loughborough, Leicestershire, UK.
authors: Gilberto
authors: G
authors: Montibeller
authors: Daniel Epstein Department of Industrial and Systems Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA, 90089, USA.
authors: Detlof
authors: D
authors: von Winterfeldt
mesh_terms: D003071:Cognition; D003657:Decision Making; D006801:Humans; D008962:Models, Theoretical; D012306:Risk
keywords: Cognitive biases; decision analysis; decision modeling; motivational biases; risk analysis