Despite the perception of lucrative earnings in the oil industry, various authors have noted that industry performance is chronically below expectations. For example, Brashear et al. (2001) noted that average return was around 7% in the 1990s, despite using typical project hurdle rates of at least 15%. The underperformance is generally attributed to poor project evaluation and selection due to chronic bias. Some authors have investigated the impact of common biases such as anchoring and overconfidence. These biases are typically investigated in isolatiion, however, which does not reveal the potentially significant compounding effect of biases in combination. Some authors hhave investigated bias in the project selection process itself (Optimizer's Curse), but suggest that the impact is relatively small (Begg and Bratvold, 2008). We believe that incomplete investigation and thus likely underestimation of the impact of biases in project evaluation and selection are at least partially responsible for persistence of these biases. In this paper we present a new framework for assessing the monetary impacct of biases acting in combination. In particular, we investigated the relationship between overconfidence and directional bias, i.e., optimism. For moderate amounts of overconfidence and optimism, expected disappointment was 20-40% of true EV for the industry portfolios and optimization cases we analyzed. Greater degrees of overconfidence and optimism resulted in expected disappointments greater than 100% of true EV. Comparison of modeling results with industry performance in the 1990s indicates that these greater degrees of overconfidence and optimism were indeed experienced in the industry. Expected disappointment can be reduced by focusing primarily on elimination of overconfidence; other biases are taken care of in the process. Considerable value should result from efforts to eliminate overconfidence and better quantify uncertainty, which could lead to better overall performance of the industry.
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