A note on the field of decision analysis

2020

The objective of this investigation is to discuss qualitatively the different methodological approaches developed to deal with uncertainty in decision making processes. For its preparation were used mainly the analysis of documents, the historicallogical method and the analytical-synthetic method which allowed an assessment of the state of the art in the topic. It was possible to identify that the phenomenon of uncertainty has two natures: one aleatory and other epistemic. Aleatory uncertainty arises from stochastic processes, while epistemic uncertainty is caused by imprecision, ignorance, credibility or incompleteness in the information necessary to make the decision. Aleatory uncertainty is effectively modeled by probability theory, which constitutes the starting point for maximizing expected utility in decision processes. Epistemic uncertainty is modeled, depending on the characteristic of the information, mainly through fuzzy sets theory, rough sets or gray systems. Each of the...

Expert Systems with Applications

Managerial decision-making often involves the consideration of multiple criteria with high levels of uncertainty. Multi-attribute utility theory, a primary method proposed for decision-making under uncertainty, has been repeatedly shown to be difficult to use in practice. This paper presents a novel approach termed Simulated Uncertainty Range Evaluations (SURE) to aid decision makers in the presence of high levels of uncertainty. SURE has evolved from an existing method that has been applied extensively in the pharmaceutical and speciality chemical sectors involving uncertain decisions in whole process design. The new method utilises simulations based upon triangular distributions to create a plot which visualises the preferences and overlapping uncertainties of decision alternatives. It facilitates decisionmakers to visualise the not-so-obvious uncertainties of decision alternatives. In a real-world case study for a large pharmaceutical company, SURE was compared to other widely-used methods for decision-making and was the only method that correctly identified the alternative eventually chosen by the company. The case study demonstrates that SURE can perform better than other existing methods for decision-making involving multiple criteria and uncertainty.

2011

Risk Analysis, 1987

This paper provides an overview of decision analysis and its use in risk management decision making. The paper discusses the distinctive characteristics of decision analysis and compares these characteristics with those of its principal alternative-cost-benefit analysis. The paper also discusses each of the steps in a decision analysis and the strengths and limitations of the method. KEY WORDS Decision analysis; risk; cost-benefit analysis; health and safety.

Journal of Multi-Criteria Decision Analysis, 1994

Decision Technologies and Applications, 2009

Many operations researchers know about the use of decision analysis to help decide among alternative investments (such as pharmacological research and development investments) using decision trees with chance nodes to compute expected monetary value or return on investment of different alternatives. Such an analysis often aims to maximize a single evaluation measure for a single decision maker. We demonstrate less widely known decision analysis techniques using spreadsheet models that will help analysts understand and model the multiple objective perspectives of the stakeholders to a decision. Using this approach can aid in identifying mutually agreeable alternative actions, designing new and better alternatives, and foreseeing opposition to decisions. It can also help analysts understand the evolution of past decisions from multiple perspectives.

Effective organizational use of decision analysis goes far beyond having individuals in the organization who know how to do decision analysis. In this note, we consider three activities that are key to creating value for the organization: making excellent choices, actually bringing about change, and operating effectively with new choices. We recommend a process that involves lacing together a decision board and a project team as they work together through six work steps. The process is focused on alternative generation, evaluation, and implementation, and is grounded in decision analysis. Tools for creativity enhancement and structuring of analysis are crucial to success; much of this note is about creativity. Collaborative efforts to frame the assessment of risk and make explicit value trade-offs are integral to the process. Riskmanagement techniques expand the set of alternatives to add value and reduce risk. Companies and consultants have been very successful with this process. 1

International Journal of the Analytic Hierarchy Process, 2023

In their excellent recounting of the development of multiple criteria decision making (MCDM) from its early history to the 21 st century, proposed that MCDM is both old and new. It is old because decision makers have always had to make tradeoffs with objectives when making decisions; the authors refer to Benjamin Franklin's approach to making decisions by trading off benefits and costs during the 1700's. However, MCDM as an important sub-field of Management Science or Operations research is rather new and began in the late 1950s. The elements of decision making fundamentally consist of the "decision", a "decision-maker" and a "decision analysis methodology." This is the reason that the MCDM field is inherently interdisciplinary. While applied mathematics is used for the decision quantitative analysis; the decision at hand, its content, needs to be addressed with tools of the corresponding discipline. This is further complicated because the decision topic may correspond to fields as diverse as medicine, sociology, or a variety of other fields. The multiple fields tackled by MCDM can easily be appreciated by taking a quick look at the list of articles that comprise the current IJAHP issue and noticing the variety of disciplines involved in the target decisions. Because of this, Wallenius and Wallenius (2023) argue in this issue for the MCDM profession to focus on important problems ranging from the risk of future pandemics to climate change. This need to focus on the discipline where the decision takes place is an important contingent aspect of MCDM that must be carefully considered and many times is not, in particular, by emerging scholars. This is one reason why the MCDM section of INFORMS evaluates research candidates for the MCDM Junior Best Paper Research Award, by allocating 30% value to the relevance of the decision and 20% to its potential impact on society while the remaining 50% considers the theoretical and methodological value of the MCDM analysis. That is, half the value of the research study (50%) focuses on aspects related to the decision itself .

2010

Decision Analysis, 2006

D ecision support models help structure and inform complex choices under uncertainty. Two classic models are risk analysis and decision analysis. Risk analysis is understood here as risk characterization, and in some cases, the identification and benefit assessment of some risk management options. It is based on systems analysis and probability, and it excludes the actual decision phase, which requires the preferences, e.g., the utility function, of the decision maker(s). Risk analysis and decision analysis have some similarities and are often complementary. To model uncertainties, both rely on probability, generally a subjective Bayesian degree of belief. A decision analysis can include a risk analysis component, and the design of a risk management plan may require decision analysis support. The challenge for risk analysts is to characterize potential failure problems before decision options have been identified, and when there is no single decision maker, or group of decision makers, who can provide preference functions and degrees of belief. Yet, a correct and complete model of uncertainties in the probabilistic risk analysis phase is important if the results are to be used later for decision support, especially when the number of systems involved and the duration of their operations is unknown. In this paper, we explore some of the challenges inherent to probabilistic risk analysis that should be of interest to the decision analysts who intend to use risk analysis results.