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Risk cube data biases

Risk cubes, actually a 5x5 matrix, used in industry characterize particular risks in terms of the likelihood of occurrence, and the consequence of the actualized risk. Human cognitive bias research led by Daniel Kahneman and Amos Tversky exposed systematic translations of objective probability and value as judged by human subjects. An examination of industry-generated risk cube data reveals evidence of biases in the judgment of likelihood and consequence. In addition, other biases -including a ‘diagonal bias’ -are revealed in the risk cube data. The evidence presented could improve risk cube based risk analysis. Copyright © 2008 by Eric D. Smith & William T. Siefert. Published and used by INCOSE with permission. RISK CUBES In the industry data analyzed for this paper, the two parameters of risk cube data are likelihood (L) and consequence (C). While the Term “Risk Cube” is used, the graphic is actually a 5x5 matrix. It is unclear why the term a cube took hold as it has only two dimensions. The use of the term “cube” is used as a matter of convenience for the reader. The cognitive bias literature uses the terms subjective probability and utility, respectively. In this paper, the possible distinctions between these terms will not be developed. (Note: For historical reasons, “value” and “value function” are used in the psychology literature in reference to what we here more clearly refer to as “utility” and “utility function.”) Risk is usually computed as the product of Likelihood and Consequence: R = L x C. Usually, likelihood is scaled from 0-1, as is consequence. Table 1: Equivalent terms: Likelihood and probability, consequence and value Subjective Parameters Likelihood (L) Consequence (C) Subjective Probability, π(p) Utility (negative), U(v) Shown on: Ordinate, Y axis Abscissa, X axis Objective Parameters Objective Probability, p Objective Value, v Risk Cubes in Industry. The most common tool used to track and manage risks is the risk cube, an example of which is shown in Figure 1. The shape and number of zones are determined by individual companies, and vary in different risk cubes. Lines dividing the zones are iso-risk lines. Allowing the zones to be fuzzy eventually leads to continuous numbers. Risk points appear inside the cube. The cube provides a visual communication aid of the risks posed by particular events. The closer to the lower left corner of the cube the risk is placed, the lower the risk.