EURO Journal on Decision Processes最新文献

In a search for a positive model of decision-making with observable primitives, we rely on the burgeoning literature in cognitive neuroscience to construct a three-element machine (agent). Its control unit initiates either impulsive or cognitive elements to solve a problem in a stationary Markov environment, the element chosen depends on whether the problem is mundane or novel, memory of past successes, and the strength of inhibition. Our predictions are based on a stationary asymptotic distribution of the memory, which, depending on the parameters, can generate different “characters”, e.g., an uptight dimwit, who could succeed more often with less inhibition, as well as a laid-back wise-guy, who could gain more with a stronger inhibition of impulsive (intuitive) responses. As one would expect, stronger inhibition and lower cognitive costs increase the frequency of decisions made by the cognitive element. More surprisingly, increasing the “carrot” and reducing the “stick” (being in a more supportive environment) enhance contemplative decisions (made by the cognitive unit) for an alert agent, i.e., the one who identifies novel problems frequently enough.

Regulatory health, safety, and environment (HSE) interventions have an impact on both costs and benefits for the industry. It is common for the regulators to evaluate such interventions by providing a comparison of costs and benefits as a basis for decision-making. Fulfilling an assignment for the Norwegian government, two consulting companies proposed a methodology for regulatory evaluation in the petroleum industry. This methodology acknowledges that uncertainty must have a higher weight than given through traditional cost–benefit analyses, but it is still to a great extent based on the use of expected values. We question this use of modified cost–benefit analyses for providing decision support in contexts where uncertainty is the dominating attribute. Furthermore, we argue that the decision-makers should be able to take a dynamic approach, where the chosen method should fit its context. As an example, we present a framework in line with such a dynamic approach. The article is an extended version of an ESREL conference article.

Quantitative risk assessments for offshore oil and gas installations have been developed and used to support decision making about major hazards risk for more than 30 years. Initially, these studies were used to support the design process, aiming to develop installations that could be operated safely throughout their lifetime. As installations were put into operation, the studies were updated with as-built and operational information to provide a basis for making decisions also in the operational phase. This was however only partially successful, and the general impression has been that the studies have not been very actively used in operations. Many explanations have been given, the most common being that the reports were too complicated and written for risk analysis experts, not operations personnel on offshore installations and that the results could not be updated sufficiently often to reflect changes in risk on a day-by-day basis. This may be a part of the explanation, but in this paper, we have looked into the decision context and the types of decisions made in operation, compared to those in the design phase. Based on this, it is concluded that the focus of existing models need to be extended to cover activity risk in a more detailed way, as well as the risk associated with the technical systems. Instead, a revised methodology for developing quantitative risk assessments is proposed, focusing on the parameters and activities that change during operation. The methodology has also been tested on an offshore installation, to investigate the feasibility in practice.

The risk caused by DP vessels in offshore marine operations is not negligible, due to wide applications of DP vessels in complex marine operations, and the sharp increase of DP vessel population. The DP accidents/incidents on the Norwegian Continental Shelf (NCS) that have occurred after 2000 indicate a need for improving safety of DP operations, which calls for new risk reduction measures. The focus of this paper is particularly on the offshore loading operations with DP shuttle tanker in offloading from floating production storage and offloading (FPSO) vessels on the NCS, but the results may be relevant also for other types of DP vessels in offshore oil and gas operations. In the paper, Man, Technology and Organization (MTO) analysis is applied to investigate the cause and barrier failures of nine reported accidents/incidents occurring over a 16-year period (2000–2015). MTO is based on three methods, including structured analysis by use of an event- and cause-diagram, change analysis by describing how events have deviated from earlier events or common practice, and barrier analysis by identifying technological and administrative barriers which have failed or are missing. The results are categorized into technical failures, human failures, organizational failures, as well as a combination of failures. The main finding is that the majority of the accidents are caused by the combination of technical, human and organizational failures. Critical root causes, results of change analysis and barrier analysis, and combination of failures are focused in the discussion. Recommendations of potential safety improvements are made on the aspects of the assessment of the actual system function, barrier management for marine systems, risk information to support different decision-makings, and the development of an on-line risk monitoring and decision supporting system.

Reaching a decision when multiple, possibly conflicting, criteria are taken into account is often a difficult task. This normally requires the intervention of an analyst to aid the decision maker in following a clear methodology with respect to the steps that need to be taken, as well as the use of different algorithms and software tools. Most of these tools focus on one or a small number of algorithms, some are difficult to adapt and interface with other tools, while only a few belong to dynamic communities of contributors allowing them to expand in use and functionality. In this paper, we address these issues by proposing to use the R statistical environment and the MCDA package of decision aiding algorithms and tools. This package is meant to provide a wide range of MCDA algorithms that may be used by an analyst to tailor a decision aiding process to their needs, while the choice of R takes advantage of the yet poorly explored opportunity to interface data analysis and decision aiding. We additionally demonstrate the use of this tool on a practical application following a well-defined decision aiding process.

Many important decision and risk analysis problems are complicated by dependencies between input variables. In such cases, standard one-variable-at-a-time sensitivity analysis methods are typically eschewed in favor of fully probabilistic, or n-way, analysis techniques which simultaneously vary all n input variables and capture their interdependencies. Unfortunately, much of the intuition provided by one-way sensitivity analysis may not be available in fully probabilistic methods because it is difficult or impossible to isolate the marginal effects of the individual variables. In this paper, we present a dependence-adjusted approach for identifying and analyzing the impact of the input variables in a model through the use of probabilistic sensitivity analysis based on copulas. This approach provides insights about the influence of the input variables and the dependence relationships between the input variables. One contribution of this approach is that it facilitates assessment of the relative marginal influence of variables for the purpose of determining which variables should be modeled in applications where computational efficiency is a concern, such as in decision tree analysis of large-scale problems. In addition, we also investigate the sensitivity of a model to the magnitude of correlations in the inputs.

The present work is devoted to the problem of city recovery management after a large scale disaster. A modern city is represented as a complex urban socio-technical system consisting of three interdependent parts: a physical lifeline system, citizens’ daily life demand, and service systems. The functioning of the last two ones directly depends on the connectivity of the physical lifelines. In cases when all the consequences of a large scale disaster are known and the system damage can be evaluated withing high accuracy researches in the field of recovery of socio-technical systems mainly use different optimization methods of recovery plan generation using various objective functions. However, the recovery of urban socio-technical system in the case of non-reliable information requires another approach. At the initial time moment collecting the information about the system state can be hindered by many causes and possible cascading failures in the infrastructure system lead to the reassessment of the city damage. The applicability of an optimization technique to the recovery management in such cases is rather troubled. The process of recovery planning should be very adaptive to such changing conditions. We developed a method of recovery management based on the reactive decision-making. It uses the step-by-step realization logic which can be applied to the recovery management under significant information uncertainties. In the present paper, we summarize the basic logic of the novel method, results of numerical simulation that demonstrate its applicability to the recovery management in cases of initial lack of information. Besides, the comparison of this approach with the result obtained by the optimization technique demonstrates the quasi-optimality of the reactive decision-making based approach of the city recovery planning.

Organizations exercise to strengthen their crisis management capability, to identify possible improvements to plans, and to develop necessary skills. Taking part in exercises is one way leaders can test their decision-making abilities under emergencies and crises. Crisis management exercises provide arenas for learning and knowledge sharing. This paper discusses how exercises can be performed better and more efficiently. We draw upon practical experiences from 12 table top and functional exercises, involving a variety of military and civilian actors, and we argue that it is possible to improve the planning and conduct of many exercises, leading to more relevant results and greater benefits for the participants. We want to demonstrate that exercises are important tools for decision-making and strategic planning processes within units or organizations. The target audience for the paper is anyone involved in crisis management exercises, from strategic planners and decision-makers to practitioners. The paper identifies practical recommendations for successful crisis management exercises, both discussion-based and operations-based. Learning points are extracted and analyzed with the use of problem structuring methods, resulting in a list of success criteria for crisis management exercises with examples of what works well and what does not.

The efficient use of resources in health systems is important due to the increasing demand and limited funding. Large health systems often have fixed input resources (such as budget and staffing) to be allocated among individual hospitals/clinics with particular target output levels. We propose an optimization model with fuzzy constraints that can be used for automatic resource re-allocation with respect to different levels of risk preferences. We illustrate its applicability using data from a U.S. Army hospital network. The implications of the proposed fuzzy decision-making model for healthcare decision makers and its relevance to healthcare policy and management are also discussed.