The Value of Assessing Uncertainty

Abstract

Despite the perception of lucrative earnings in the oil industry, various authors have noted that industry performance is routinely below expectations. For example, the average reported return for the industry was around 7% in the 1990s, even though a typical project hurdle rate was at least 15%. The underperformance is generally attributed to poor project evaluation and selection due to chronic bias. While a number of authors have investigated cognitive biases in oil and gas project evaluation, there have been few quantitative studies of the impact of biases on economic performance. Incomplete investigation and possible underestimation of the impact of biases in project evaluation and selection are at least partially responsible for persistence of these biases.

The objectives of this work were to determine quantitatively the value of assessing uncertainty or, alternatively, the cost of underestimating uncertainty. This work presents a new framework for assessing the monetary impact of overconfidence bias and directional bias (i.e., optimism or pessimism) on portfolio performance. For moderate amounts of overconfidence and optimism, expected disappointment (having realized NPV less than estimated NPV) was 30-35% of estimated NPV for typical industry portfolios and optimization cases. Greater degrees of overconfidence and optimism resulted in expected disappointments approaching 100% of estimated NPV. Comparison of simulation results with expected industry performance in the 1990s, indicates that these greater degrees of overconfidence and optimism have been experienced in the industry.

The value of reliably quantifying uncertainty is in reducing or eliminating expected disappointment and expected decision error (selecting the wrong projects), which is achieved by focusing primarily on elimination of overconfidence; other biases are taken care of in the process. Elimination of expected disappointment will improve industry performance overall to the extent that superior projects are available and better quantification of uncertainty allows identification of these superior projects.