Project Management in the Oil & Gas Industry - A Bayesian Approach

A reliable ‘Estimate at Completion’ from the early stage of project execution is essential in order to enable efficient and proactive project management. The non-repetitive and uncertain nature of projects and the involvement of multiple stakeholders require the use and integration of multiple informative sources in order to provide accurate forecasts. Moreover, in the Oil&Gas industry projects are characterized by a high level of complexity and financial impact. The paper aims at multiple objectives: introducing the need for the identification and utilization of all the available knowledge in order to improve the forecasting process; developing a Bayesian approach in order to integrate the diverse knowledge sources; exploring the integration of data records and experts’ judgment related to the ongoing project; exploring the integration of data records related to projects completed in the past and to the ongoing project and finally developing a Bayesian model capable of using three different knowledge sources: data records and experts’ judgments related to the ongoing project and data records related to similar projects completed in the past. The model has been tested in a set of large and complex projects in the Oil&Gas industry, in order to forecast the final duration and the final cost. The results show a higher forecasting accuracy of the Bayesian model compared to the traditional Earned Value Management (EVM) methodology

Stress and reflection effects on the IGT

Stressful situations hinder judgment. Effects of stress induced by anticipated public speaking on the Iowa Gambling Task (IGT) were examined. The Cognitive Reflection Task (CRT) was used to examine the relationship between reflective thinking and IGT performance. The stress manipulation increased blood pressure and was associated with poorer IGT and CRT performance. Stressed participants were slower to avoid the disadvantageous decks. Moreover, CRT scores correlated with optimal deck selections indicating the importance of reflective thinking for good performance on the IGT. These correlations were observed in relatively early trials, which challenges the view that analytic thinking is not important when card contingencies are being learned. Data revealed that IGT performance in healthy individuals is not always optimal; stress levels impair performance. A mediation analysis was consistent with the proposal that the stress manipulation reduced IGT performance by impeding reflective thinking. Thus reflective processing is an important explanation of IGT performance in healthy populations. It was concluded that more reflective participants appear to learn from the outcomes of their decisions even when stressed

Evaluating Quality of Decision-Making Processes in Medicines' Development, Regulatory Review, and Health Technology Assessment : A Systematic Review of the Literature.

Introduction: Although pharmaceutical companies, regulatory authorities, and health technology assessment (HTA) agencies have been increasingly using decision-making frameworks, it is not certain whether these enable better quality decision making. This could be addressed by formally evaluating the quality of decision-making process within those organizations. The aim of this literature review was to identify current techniques (tools, questionnaires, surveys, and studies) for measuring the quality of the decision-making process across the three stakeholders. Methods: Using MEDLINE, Web of Knowledge, and other Internet-based search engines, a literature review was performed to systematically identify techniques for assessing quality of decision making in medicines development, regulatory review, and HTA. A structured search was applied using key words and a secondary review was carried out. In addition, the measurement properties of each technique were assessed and compared. Ten Quality Decision-Making Practices (QDMPs) developed previously were then used as a framework for the evaluation of techniques identified in the review. Due to the variation in studies identified, meta-analysis was inappropriate. Results: This review identified 13 techniques, where 7 were developed specifically to assess decision making in medicines' development, regulatory review, or HTA; 2 examined corporate decision making, and 4 general decision making. Regarding how closely each technique conformed to the 10 QDMPs, the 13 techniques assessed a median of 6 QDMPs, with a mode of 3 QDMPs. Only 2 techniques evaluated all 10 QDMPs, namely the Organizational IQ and the Quality of Decision Making Orientation Scheme (QoDoS), of which only one technique, QoDoS could be applied to assess decision making of both individuals and organizations, and it possessed generalizability to capture issues relevant to companies as well as regulatory authorities. Conclusion: This review confirmed a general paucity of research in this area, particularly regarding the development and systematic application of techniques for evaluating quality decision making, with no consensus around a gold standard. This review has identified QoDoS as the most promising available technique for assessing decision making in the lifecycle of medicines and the next steps would be to further test its validity, sensitivity, and reliability.Peer reviewedFinal Published versio

The Affective Impact of Financial Skewness on Neural Activity and Choice

Few finance theories consider the influence of “skewness” (or large and asymmetric but unlikely outcomes) on financial choice. We investigated the impact of skewed gambles on subjects' neural activity, self-reported affective responses, and subsequent preferences using functional magnetic resonance imaging (FMRI). Neurally, skewed gambles elicited more anterior insula activation than symmetric gambles equated for expected value and variance, and positively skewed gambles also specifically elicited more nucleus accumbens (NAcc) activation than negatively skewed gambles. Affectively, positively skewed gambles elicited more positive arousal and negatively skewed gambles elicited more negative arousal than symmetric gambles equated for expected value and variance. Subjects also preferred positively skewed gambles more, but negatively skewed gambles less than symmetric gambles of equal expected value. Individual differences in both NAcc activity and positive arousal predicted preferences for positively skewed gambles. These findings support an anticipatory affect account in which statistical properties of gambles—including skewness—can influence neural activity, affective responses, and ultimately, choice

Five things you should know about cost overrun

This paper gives an overview of good and bad practice for understanding and curbing cost overrun in large capital investment projects, with a critique of Love and Ahiaga-Dagbui (2018) as point of departure. Good practice entails: (a) Consistent definition and measurement of overrun; in contrast to mixing inconsistent baselines, price levels, etc. (b) Data collection that includes all valid and reliable data; as opposed to including idiosyncratically sampled data, data with removed outliers, non-valid data from consultancies, etc. (c) Recognition that cost overrun is systemically fat-tailed; in contrast to understanding overrun in terms of error and randomness. (d) Acknowledgment that the root cause of cost overrun is behavioral bias; in contrast to explanations in terms of scope changes, complexity, etc. (e) De-biasing cost estimates with reference class forecasting or similar methods based in behavioral science; as opposed to conventional methods of estimation, with their century-long track record of inaccuracy and systemic bias. Bad practice is characterized by violating at least one of these five points. Love and Ahiaga-Dagbui violate all five. In so doing, they produce an exceptionally useful and comprehensive catalog of the many pitfalls that exist, and must be avoided, for properly understanding and curbing cost overrun

Understanding risky behavior : the influence of cognitive, emotional and hormonal factors on decision-making under risk

Financial risky decisions and evaluations pervade many human everyday activities. Scientific research in such decision-making typically explores the influence of socio-economic and cognitive factors on financial behavior. However, very little research has explored the holistic influence of contextual, emotional, and hormonal factors on preferences for risk in insurance and investment behaviors. Accordingly, the goal of this review article is to address the complexity of individual risky behavior and its underlying psychological factors, as well as to critically examine current regulations on financial behavior

The role of conviction and narrative in decision-making under radical uncertainty

We propose conviction narrative theory (CNT) to broaden decision-making theory for it better to understand and analyse how subjectively means-end rational actors cope in contexts in which the traditional assumptions in decision-making models fail to hold. Conviction narratives enable actors to draw on their beliefs, causal models and rules of thumb to identify opportunities worth acting on, to simulate the future outcome of their actions and to feel sufficiently convinced to act. The framework focuses on how narrative and emotion combine to allow actors to deliberate and to select actions that they think will produce the outcomes they desire. It specifies connections between particular emotions and deliberative thought, hypothesizing that approach and avoidance emotions evoked during narrative simulation play a crucial role. Two mental states, Divided and Integrated, in which narratives can be formed or updated, are introduced and used to explain some familiar problems that traditional models cannot

How robust is the optimistic update bias for estimating self-risk and population base rates?

Humans hold unrealistically optimistic predictions of what their future holds. These predictions are generated and maintained as people update their beliefs more readily when receiving information that calls for adjustment in an optimistic direction relative to information that calls for adjustment in a pessimistic direction. Thus far this update bias has been shown when people make estimations regarding the self. Here, we examine whether asymmetric belief updating also exists when making estimations regarding population base rates. We reveal that while participants update beliefs regarding risk in the population in an asymmetric manner, such valence-dependent updating of base rates can be accounted for by priors. In contrast, we show that optimistic updating regarding the self is a robust phenomenon, which holds even under different empirical definitions of desirable information

A Structured Approach to Strategic Decisions

Some management decisions are made without weighing quite so much information. But strategic decisions tend to involve the distillation of complexity into a single path forward. Given how unreliable human judgment is, all evaluations are susceptible to errors. These errors can stem from known cognitive biases - or they can be random errors, sometimes called "noise". Unreliability in judgment has long been recognized and studied, particularly in the context of decision-making about hiring. Here, Kahneman et al. describe the Mediating Assessments Protocol, a practical, broadly applicable approach to reducing errors in strategic decision-making

An Experimental Evaluation of a De-biasing Intervention for Professional Software Developers

The role of expert judgement is essential in our quest to improve software project planning and execution. However, its accuracy is dependent on many factors, not least the avoidance of judgement biases, such as the anchoring bias, arising from being influenced by initial information, even when it's misleading or irrelevant. This strong e ect is widely documented. Objective: We aimed to replicate this anchoring bias using professionals and, novel in a software engineering context, explore de-biasing interventions through increasing knowledge and awareness of judgement biases. Method: We ran two series of experiments in company settings with a total of 410 software developers. Some developers took part in a workshop to heighten their awareness of a range of cognitive biases, including anchoring. Later, the anchoring bias was induced by presenting low or high productivity values, followed by the participants' estimates of their own project productivity. Our hypothesis was that the workshop would lead to reduced bias, i.e., work as a de-biasing intervention. Results: The anchors had a large e ect (robust Cohen's d = 1:19) in in uencing estimates. This was substantially reduced in those participants who attended the workshop (robust Cohen's d = 0:72). The reduced bias related mainly to the high anchor. The de-biasing intervention also led to a threefold reduction in estimate variance. Conclusion: The impact of anchors upon judgement was substantial. Learning about judgement biases does appear capable of mitigating, although not removing, the anchoring bias. The positive e ect of de-biasing through learning about biases suggests that it has value