The nature of risk in complex projects

© 2017 Project Management Institute, Inc. Risk analysis is important for complex projects; however, systemicity makes evaluating risk in real projects difficult. Looking at the causal structure of risks is a start, but causal chains need to include management actions, the motivations of project actors, and sociopolitical project complexities as well as intra-connectedness and feedback. Common practice based upon decomposition-type methods is often shown to point to the wrong risks. A complexity structure is used to identify systemicity and draws lessons about key risks. We describe how to analyze the systemic nature of risk and how the contractor and client can understand the ramifications of their actions

The nature of risk in complex projects

© 2017 Project Management Institute, Inc. Risk analysis is important for complex projects; however, systemicity makes evaluating risk in real projects difficult. Looking at the causal structure of risks is a start, but causal chains need to include management actions, the motivations of project actors, and sociopolitical project complexities as well as intra-connectedness and feedback. Common practice based upon decomposition-type methods is often shown to point to the wrong risks. A complexity structure is used to identify systemicity and draws lessons about key risks. We describe how to analyze the systemic nature of risk and how the contractor and client can understand the ramifications of their actions

Expert Elicitation for Reliable System Design

This paper reviews the role of expert judgement to support reliability assessments within the systems engineering design process. Generic design processes are described to give the context and a discussion is given about the nature of the reliability assessments required in the different systems engineering phases. It is argued that, as far as meeting reliability requirements is concerned, the whole design process is more akin to a statistical control process than to a straightforward statistical problem of assessing an unknown distribution. This leads to features of the expert judgement problem in the design context which are substantially different from those seen, for example, in risk assessment. In particular, the role of experts in problem structuring and in developing failure mitigation options is much more prominent, and there is a need to take into account the reliability potential for future mitigation measures downstream in the system life cycle. An overview is given of the stakeholders typically involved in large scale systems engineering design projects, and this is used to argue the need for methods that expose potential judgemental biases in order to generate analyses that can be said to provide rational consensus about uncertainties. Finally, a number of key points are developed with the aim of moving toward a framework that provides a holistic method for tracking reliability assessment through the design process.Comment: This paper commented in: [arXiv:0708.0285], [arXiv:0708.0287], [arXiv:0708.0288]. Rejoinder in [arXiv:0708.0293]. Published at http://dx.doi.org/10.1214/088342306000000510 in the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org

Consumer finance: challenges for operational research

Consumer finance has become one of the most important areas of banking, both because of the amount of money being lent and the impact of such credit on global economy and the realisation that the credit crunch of 2008 was partly due to incorrect modelling of the risks in such lending. This paper reviews the development of credit scoring—the way of assessing risk in consumer finance—and what is meant by a credit score. It then outlines 10 challenges for Operational Research to support modelling in consumer finance. Some of these involve developing more robust risk assessment systems, whereas others are to expand the use of such modelling to deal with the current objectives of lenders and the new decisions they have to make in consumer finance. <br/

Misunderstanding Models in Environmental and Public Health Regulation

Computational models are fundamental to environmental regulation, yet their capabilities tend to be misunderstood by policymakers. Rather than rely on models to illuminate dynamic and uncertain relationships in natural settings, policymakers too often use models as “answer machines.” This fundamental misperception that models can generate decisive facts leads to a perverse negative feedback loop that begins with policymaking itself and radiates into the science of modeling and into regulatory deliberations where participants can exploit the misunderstanding in strategic ways. This paper documents the pervasive misperception of models as truth machines in U.S. regulation and the multi-layered problems that result from this misunderstanding. The paper concludes with a series of proposals for making better use of models in environmental policy analysis.The Kay Bailey Hutchison Center for Energy, Law, and Busines

Operations research in consumer finance: challenges for operational research

Consumer finance has become one of the most important areas of banking both because of the amount of money being lent and the impact of such credit on the global economy and the realisation that the credit crunch of 2008 was partly due to incorrect modelling of the risks in such lending. This paper reviews the development of credit scoring,-the way of assessing risk in consumer finance- and what is meant by a credit score. It then outlines ten challenges for Operational Research to support modelling in consumer finance. Some of these are to developing more robust risk assessment systems while others are to expand the use of such modelling to deal with the current objectives of lenders and the new decisions they have to make in consumer financ