Risk, Uncertainty and Investment Decision-Making in the Upstream Oil and Gas Industry Fiona Macmillan ma hons (University of Aberdeen) October 2000 a thesis presented for the degree of Ph. D. at the University of Aberdeen declaration
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KEYAct fork
Event fork Best strategy Green numbers EMV of decision alternative $340,000 $-100,000 Test favourable 0.6 Oil
0.1 $400, 000 $-100,000 $40,000
Don’t drill
$244,000 (0.6*340000+100000*0.4) Acquire seismic data Don’t acquire seismic data
1
0
Net Liquid Assets Figure 5.9: The decision-maker’s preference curve 
  
0.74 Acquire seismic data $-30,000
0.74 Test favourable 0.6
Test unfavourable 0.4
0.83 0.60 Drill Don’t drill 0.1 Oil 0.1
No oil 0.9 Don’t acquire seismic data Don’t drill 0.83 Oil
0.85 $400,000
No oil 0.15
0.68 Drill Don’t drill 0.67 Oil 0.55
$400,000 No oil 0.45
$-100,000 $400,000,0.98 $0,0.0 $100,000,0.6 $400,000,0.98 $0,0.0
$100,000,0.6 $430,000,1.00 $30,000,0.27 $130,000,0.68
Figure 5.10: Analysis using preferences $-100,000 $-100,000 KEYAct fork
Red numbers Preference from curve Best strategy Green numbers Preference for event fork Pink numbers Preference for act chosen Act not chosen Preference theory can be extended to decisions involving multiple attributes. Multi-attribute preference theory (more commonly referred to as multi-attribute utility theory) shows how, provided certain conditions apply, the main decision problem can be broken into sub-problems and a single attribute preference function (or curve) can be derived for each attribute and then these can be combined to obtain a multi-attribute function (Goodwin and Wright, 1991 p86). A number of methods have been proposed for performing this analysis, but the approach described by Keeney and Raiffa (1976) is the most popular. A number of researchers have questioned the application of preference theory to real problems. Most of their concerns relate to the generation of a decision-maker’s preference curve. Since, crucially, whilst von Neumannn and Morgenstern proved that a preference curve exists for each decision-maker who makes decisions consistent with the eight axioms they did not specify how to obtain this curve. Since 1944, many researchers have studied this problem, but so far, their attempts have been only marginally successful: “The unfortunate truth is that we (as a business community) do not as yet have a satisfactory way to construct an individual’s preference curve.” (Newendorp, 1996 p162) Generally researchers have attempted to describe a decision-maker’s preference curve by obtaining the decision-maker’s responses to a carefully designed set of hypothetical investment questions (Newendorp, 1996 p160; Hammond, 1967; Swalm, 1966). In these tests, the decision-maker is offered a choice between a gamble having a very desirable outcome and an undesirable outcome, and a no-risk alternative of intermediate desirability. Such tests, though, have only been marginally successful for two reasons. Firstly, the test procedures have to use hypothetical gambles and decisions rather than actual gambles. The rationale for this is that if the procedure used real decision-making situations, decision-makers generally would either accept or reject a decision without stopping to explicitly state what the probabilities would have to be for them to have been indifferent between the gamble and the no-risk alternative (Newendorp, 1996). Tocher (1977) argues that since these gambles are only imaginary, the decision-maker’s judgements about the relative attractiveness of the gambles may not reflect what the decision-maker would really do. This is known as the preference reversal phenomenon and has been researched extensively (Slovic, 1995; Mowen and Gentry, 1980, Grether and Plott, 1979; Lichenstein and Slovic, 1971; Lindman, 1971) and many theories have been proposed to explain it (for example, Ordonez and Benson, 1997; Goldstein and Einhorn, 1987). Secondly, most decision-makers are not used to making decisions on the basis of a precise discernment of probabilities used to justify a gamble. Rather, the probabilities are usually specified for a given investment, and the parameter that the decision-maker focuses on is whether the successful gain is sufficient to justify the gamble (Newendorp, 1996). A further limitation of the application of preference theory to real problems is the inability to construct corporate preference curves. Hammond (1967) argues that an organisation’s propensity for risk is higher than that of an individual, and that therefore companies’ preference curves are usually more risk seeking than those of individuals’. However, Hammond believes, that the individual manager could unwittingly apply his own much more conservative preference curve when making decisions on behalf of the company. Therefore, Hammond (1967) concludes that organisations should have a corporate preference curve for individual mangers to use when making company decisions. However, whilst research into constructing corporate preference curves has been attempted, its theoretical results have, to date, been too complex for practical application (Hammond, 1967). Recently, though one company based in Aberdeen, Det Norske Veritas (DNV), has started offering preference theory-based analysis to upstream companies. Despite these limitations, proponents of preference theory claim that it provides the decision-maker with the most representative decision parameter ever developed. They argue that its use will produce a more consistent decision policy than that which results from using EMV and that preference theory also accounts for the non-arbitrary factors in an arbitrary way (Swalm, 1966). Others are more cautionary. Bailey et al. (in press) argue that proponents of preference theory should not claim that it is a descriptive tool but rather offer it as a prescriptive technique that can be used to help individuals or companies take decisions. They write: “…preference theory does have a more limited but still important role. It can graphically demonstrate to decision-makers what their style of decision-making implies. It might show a highly conservative, very risk averse decision-maker that there was room for much more flexibility without incurring enormous penalties, or it might show an intuitive decision-maker that his decision-making was altogether too risky.” However, opponents argue that it is impossible to quantify emotions regarding money, and therefore the whole idea of preference theory is an exercise in frustration (Tocher, 1977). Underpinning preference theory, the EMV concept, decision tree analysis and indeed the whole of decision analysis, as highlighted in the sections above and in Chapter 2, is the ability of the analyst to generate subjective probabilistic estimates of the variables under investigation, as a mechanism for quantifying the risk and uncertainty. Traditional probability theory relies on the relative frequency concept in which probability is perceived to be the long-run relative frequency with which a system is observed in a particular 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 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. 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