# As such the subjective probabilities used in decision

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Unformatted text preview: cular state in a series of identical experiments. However, given its emphasis on repeated experiments, the frequentist concept is unsuitable for modelling under the conditions of risk and uncertainty present in the majority of investment decision problems. As such, the subjective probabilities used in decision analysis are founded upon a quite a different conceptual base. As indicated in Chapter 2, to a subjectivist, probability represents an observer’s degree of belief that a system will adopt a particular state. There is no presumption of an underlying series of experiments. The observer need only be going to observe the system on one occasion. Moreover, subjective probabilities encode something about the observer of the system, not the system itself. The justification for using subjective probabilities in decision analysis does not just rest on the case that frequentist probabilities are inappropriate but also in the principles of consistency that the Bayesians suggest should be embodied in rational decision-making (for a full discussion see French, 1989 p31). Accepting the rationale that subjective probability estimates should be used for investment decision-making under conditions of uncertainty however does cause problems since, partly because of their subjective nature, there is no formula in the decision analysis literature for generating these probabilities. Therefore, analysts typically use their judgement, extrapolate from historical data or, for example when estimating recoverable reserves for a particular field, use the results achieved in other 97 similar plays to guide their predictions. Traditionally analysts used single-value probability estimates to express the degree of risk and uncertainty relating to the uncertain parameters. More popular now is to generate subjective probability estimates using risk analysis which adds the dimension of simulation to decision analysis. The following section draws on the prescriptive decision analysis literature first to provide a brief overview of the main concepts of risk analysis and then to indicate the impact of risk analysis on investment decision-making in the upstream. It is important to recognise that risk analysis is a special case of decision analysis that uses techniques of simulation. Often in the literature, the terms are used interchangeably leading to confusion when comparing accounts. 5.4 RISK ANALYSIS Simulation as a means of risk analysis in decision-making was first applied to petroleum exploration investments in 1960 (Grayson, 1960). The technique can be applied to any type of calculation involving random variables. It can be used to answer technical questions such as (“What is the volume of recoverable reserves of hydrocarbons in this acreage?”) and economic ones such as (“What is the probability that the NPV of this prospect will exceed the target of \$x million?”) (Bailey et al., in press). The main concepts of risk analysis using simulation will now be presented before its applicability to the upstream is examined. Risk analysis based on Monte Carlo simulation is a technique whereby the risk and uncertainty encompassing the main projected variables in a decision problem are described...
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## This document was uploaded on 03/30/2014.

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