EURO Journal on Decision Processes最新文献

This article highlights a major disconnect between the theory and practice of policy design. It provides a contrast between two ways to envisage design in political science. The first focuses on functional requirements and techniques, highlighting what policymakers need to do and the steps they use. The second focuses on theories and empirical studies that situate policy design within the wider study of policy processes, highlighting a major gap between requirements and outcomes. These approaches should complement and inform each other, but rarely do. Most policy theories treat classic descriptions of policy design (such as making policy via series of steps or stages) as divorced from reality, and only useful as ideal-types to contrast with what actually happens. Policy theories may be more accurate, but very few provide equivalent practical lessons (and most do not try). If so, what are the prospects of bringing together these literatures? The article examines two kinds of theory-informed policy design: theories at the service of analysis or sources of critical analysis and cautionary tales.

A new method of predicting time-series-based stock prices and a new model of an investment portfolio based on predictions obtained is proposed here. For this purpose, a new regression scheme is implemented on a long-short-term-memory-based deep neural network. The predictions once obtained are used to construct an investment portfolio or more specifically a predicted portfolio. A large set of experiments have been carried on stock data of NIFTY-50 obtained from the National stock exchange of India. The results confirm that the proposed model outperforms various standard predictive models as well as various standard portfolio optimization models.

Based on the graph model for conflict resolution (GMCR), an analytical framework is proposed to assist the focal decision maker (DM) with an informational advantage in resolving real-world hyper-inverse conflict situations considering the sequential (SEQ) stability. The hyper-inverse conflict resolution aims to obtain the opponent’s misunderstanding preferences of focal DM, which can assist the focal DM in taking the initiative in a conflict. Among all stabilities in GMCR, the SEQ stability is selected in the present study, because this stability concept provides a logical basis for a DM to sanction the opponent, which reflects the case for many conflicts. Moreover, a nonlinear binary optimization model rooted in the matrix representation of SEQ stability is constructed to capture the cause and process of the abnormal individual stability (or stabilities) in the hypergame. Then, a solution procedure is designed for solving the optimization model to obtain the preferences that are misunderstood by the opponent. Finally, an illustrative example of two DMs in an environmental management conflict is studied to demonstrate how the proposed framework can be conveniently employed in practice.

Fuzzy analytic hierarchy process (FAHP) has been extensively applied to multi-criteria decision making (MCDM). However, the computational burden resulting from the calculation of fuzzy eigenvalue and eigenvector is heavy. As a result, a FAHP problem is usually solved using approximation techniques such as fuzzy geometric mean (FGM) and fuzzy extent analysis (FEA) instead of exact methods. Therefore, the FAHP results are subject to considerable inaccuracy. To solve this problem, in this study, a FAHP method based on the combination of α-cut operations (ACO), center-of-gravity (COG) defuzzification and defuzzification convergence mechanism (DCM) is proposed. First, ACO is applied to derive the near-exact fuzzy maximal eigenvalue and fuzzy weights. Subsequently, the α cuts of the fuzzy maximal eigenvalue and fuzzy weights are interpolated to generate samples that are uniformly distributed along the x-axis so that COG can be correctly applied to defuzzify the fuzzy maximal eigenvalue and fuzzy weights. To accelerate the computation process, DCM is applied to terminate the enumeration process if the defuzzified values of fuzzy weights have converged. The ACO–COG–DCM method has been applied to a real case to illustrate its applicability. In addition, a simulation study was also conducted to perform a parametric analysis. According to the experimental results, the proposed ACO–COG–DCM method improved the accuracy of estimating fuzzy weights by up to 56%. Furthermore, the experimental results also showed that the inaccuracy of estimating fuzzy weights was mostly owing to the deficiency of the FAHP method rather than the inconsistency of fuzzy pairwise comparison results.

Experts and laypeople often have to make judgements based on a large body of evidence that is uncertain and inconclusive. Theory suggests people are likely to modify their decisions according to how information is framed and structured. This study assesses the effect of information form (text versus numerical) and sequence (bundled versus non-bundled) on decision outcomes regarding the regulation of plants with novel traits. Both experts and laypersons were surveyed and offered one of eight different treatments that tested for the effect of information presentation, the effect of the assigned decision role (as an individual or a member of a decision committee), and the structure of the decision itself. Results show that non-experts are more prone than experts to change their decisions when faced with different structures or decision architectures, especially when the form of information changes.

Decision support consists in helping a decision-maker to improve his/her decisions. However, clients requesting decision support are often themselves experts and are often taken by third parties and/or the general public to be responsible for the decisions they make. This predicament raises complex challenges for decision analysts, who have to avoid infringing upon the expertise and responsibility of the decision-maker. The case of diagnosis decision support in healthcare contexts is particularly illustrative. To support clinicians in their work and minimize the risk of medical error, various decision support systems have been developed, as part of information systems that are now ubiquitous in healthcare contexts. To develop, in collaboration with the hospitals of Lyon, a diagnostic decision support system for day-to-day customary consultations, we propose in this paper a critical analysis of current approaches to diagnostic decision support, which mainly consist in providing them with guidelines or even full-fledged diagnosis recommendations. We highlight that the use of such decision support systems by physicians raises responsibility issues, but also that it is at odds with the needs and constraints of customary consultations. We argue that the historical choice to favor guidelines or recommendations to physicians implies a very specific vision of what it means to support physicians, and we argue that the flaws of this vision partially explain why current diagnostic decision support systems are not accepted by physicians in their application to customary situations. Based on this analysis, we propose that decision support to physicians for customary cases should be deployed in an “adjustive” approach, which consists in providing physicians with the data on patients they need, when they need them, during consultations. The rationale articulated in this article has a more general bearing than clinical decision support and bears lessons for decision support activities in other contexts where decision-makers are competent and responsible experts.

The behaviour of the actors in a decision aid process is determined by different factors, all of which have an impact on the effectiveness of the process and its results. A framework, originally created to document decision aid processes and to study the reasons for backward and forward cycles in the process, has been used to comparatively analyse several multi-criteria decision aid interventions. The study has led to the identification of three basic process typologies, in relation with some organisational and processual complexities and factors which, as behavioural aspects, have the main influence on the interaction between the analysts and actors of a decision aid process. The work is a proposal for the behavioral OR research agenda.

Considering the psychological aspects of altruism and reciprocity in strategic interactions, we propose using the Cobb–Douglas utility function to refine standard utility functions as a weighted product of the player’s utility (“me” factor) and the jointly utility of players (“us” factor). We model an altruism willingness function to define the strategic allocation within “me” vs. “us” based on perceived kindness of the counterparts during the past agreements and unconditional altruism. Numerical examples are presented to show how the model represents the sequential group decision process of players with heterogeneous altruism profiles.

An experiment was set up to determine whether some short, focused training could influence decision makers to take a more structured and process-based approach to project decision-making. The experiment also investigated the impact on project decision-making of the way a decision is framed by an authority figure, i.e. how a decision is influenced by an authority figure advocating a process-driven, neutral or an opinion/schedule-driven approach. The experiment was set up so that half of the participants watched three 15-min training videos before answering questions on decision-making scenarios for projects, and the other half just answered questions on the decision-making scenarios. 40% of participants (split across those who watched the training videos and those that only answered the decision-making scenario questions) had undergone some prior training on decision making. The results demonstrate that watching the training videos has an impact. The impact is greater when there has been no prior training; however, there is still impact in each case, albeit small for some. This implies that the benefits of 1 h of training prior to project decision-making is more valuable for those with no prior training, but still worthwhile for those with prior training. The results showed that framing by an authority figure has a strong influence on the participants’ responses, in terms of whether a process-based, neutral or opinion/intuition-based response was given.

With the proliferation of information and communication technologies, especially with recent developments in Artificial Intelligence, social robots at home and the workplace are no longer being treated as lifeless and emotionless, leading to proposals which aim at incorporating affective elements within agents. Advances in areas such as affective decision-making and affective computing drive this interest. Our motivation in this paper is to use affection as a basic element within a decision-making process to facilitate robotic agents providing more seemingly human responses. We use earlier research in cognitive science and psychology to provide a model for an autonomous agent that makes decisions partly influenced by affective factors when interacting with humans and other agents. The factors included are emotions, mood, personality traits, and activation sets in relation with impulsive behavior. We describe several simulations with our model to study and compare its performance when facing various types of users. Through them, we essentially showcase that our model allows for a powerful agent design mechanism regulating its behavior and provides greater decision-making adaptivity when compared to emotionless agents and simpler emotional models. We conclude describing potential uses of our model in several application areas.