EURO Journal on Decision Processes最新文献

In this paper we examine a multi-period portfolio optimization problem in a fuzzy environment. The proposed optimization model is subject to CVaR constraint, transaction constraint and cardinality constraint. The returns of the assets are assumed to be trapezoidal fuzzy variables and therefore the portfolio return and risk are quantified by the possibilistic mean and semivariance of the fuzzy returns respectively. A dynamic programming method is used to solve the proposed mixed interger optimization model for different cardinality constraints. A numerical study based on real stocks market data is provided to test the efficiency of the proposed algorithm. The sensitivity of the optimal portfolio investment strategies is tested for different confidence levels for the CVaR constraint.

In this paper we will provide a quantitative analysis of a simple model of the Federal Disaster Assistance policy from the viewpoint of three different stakeholders. This quantitative methodology is new and has applications to other areas such as business and healthcare processes. The stakeholders are interested in process transparency but each has a different opinion on precisely what constitutes transparency. We will also consider three modifications to the Federal Disaster Assistance policy and analyse, from a stakeholder viewpoint, how stakeholder satisfaction changes from process to process. This analysis is used to rank the favourability of four policies with respect to all collective stakeholder preferences.

Increased population growth and urbanization have brought critical challenges to urban water systems, including water scarcity and environmental degradation. To address the problems, real-time controlled rainwater storages are now being used to reduce flooding by intercepting rainfall, while also providing an alternate water supply and actively restoring baseflow to improve biodiversity outcomes. These benefits can be enhanced when the storages are managed as an optimized network. This paper proposes a multi-objective-optimization-based strategy utilizing mixed integer linear programming and compromise programming to control a network of rainwater storages. The proposed strategy is observed to substantially reduce storage overflow, improve stream baseflow, and fulfill most of the domestic non-potable water demand. It shows a clear advantage over the NSGA II-based strategy, indicating the effectiveness of mathematical programming with scalarization techniques in solving multi-objective problems.

The relationship between negotiation processes and outcomes is a challenging problem for theoretical and empirical analyses. In this paper, we study whether a dynamic bargaining model that incorporates a notion of negotiator confidence in the process and that predicts the asymmetric Nash bargaining solution as the outcome is compatible with observations in negotiation experiments. This requires establishing how the compatibility between the model and the actual bargaining process can be assessed, without knowing a key parameter in the model. We find that the model is largely compatible with the observed bargaining process, but that actual agreements tend to be more balanced than the solution predicted by the model. We also find a close relationship between the parameter representing negotiator confidence in the model and the negotiator’s (independently ascertained) aspiration levels, thus providing additional evidence for the model’s external validity.

The ethical dimension of Operational Research and Decision Aiding, although not a new subject, has become more of a concern, both for the large public and the OR community, because of the wide spread of autonomous artefacts endowed with decision capacity thanks to the use of models, methods and tools developed within our field. The paper addresses the question of whether there exists an “Ethical Operational Research practice”, identifies the ethical questions which are specific to our professional community and suggests research topics which, although independently developed, are relevant for handling such questions.

The adoption of two-sided digital platforms is influenced by cross-side network effects, that is, the number of adopters on the demand side of a platform affects the number of adopters on the supply side and vice versa. Accordingly, publicizing adopter numbers might stimulate platform success. However, finding the right point in time to do so is difficult because there is little or no research on corresponding strategies and best practices based on already successful platforms are inconclusive. This paper addresses this research gap by (i) demonstrating the impact of publicizing adopter numbers on platform growth, (ii) analyzing the effect of market characteristics on the strategic fit of such information-sharing strategies, (iii) introducing an agent-based simulation optimization tool that aims to identify the optimal number of adopters on the other side of the platform that should be reached before advertising information is begun, and (iv) illustrating the applicability of such a tool by means of a real-world use case. Agent-based modeling and simulation was chosen because the effectiveness of advertising adopter numbers depends on various interactions between (potential) platform users, which gives rise to a complex system that cannot be properly captured via traditional analytical approaches. The simulation accounts for the stakeholders’ decision processes and can support the platform operator's decision on when to start publicizing adopter numbers. The results of our numerical experiments indicate that there is no single conclusive answer to the question when advertising adopter numbers should begin, as the right time depends on the specific market characteristics.

Improving policy making is key to address numerous contemporary challenges such as the environmental crisis, climate change, global inequality, financial crises, or pandemics. Policy making is a sequence of stages structuring policy problems and choices made to address them. Among these stages, policy design is a crucial phase since it impacts the quality of the policy alternatives being considered. Policy design is, however, largely neglected in the scientific literature, and in practice it is mainly conducted informally. Design theory, and more specifically Concept-Knowledge (C-K) theory, originally aimed at assisting the process of creating marketable objects, offers promises to formalize and rationalize policy design. We critically analyze this theory, showing that, despite its strengths, as it stands it is ill-adapted to support the innovative design of policy alternatives. For that purpose, we propose a framework, C-K^E/I. This framework, which is inspired by and compatible with C-K, appraises innovation based on the explicit or implicit modal statements held by a certain individual or group (“E/I” stands for Explicit vs. Implicit). Through an ex-post analysis of a case study—the search for innovative policy solutions to water management problems in the Apulia Region, Italy—we illustrate the practical applicability and usefulness of our framework.

Value-Focused Thinking (VFT) is used in many situations to identify and structure value-based objectives. VFT shares motivations with the consolidated field of Operations Research (OR), called Problem Structuring Methods (PSMs). This study presents a historical outline of the origins of PSMs and how VFT fits into the literature. The procedures adopted involve a literature review on VFT applications. The results confirmed that VFT is applied in problematic situations in which PSMs are used without a definite framework. The structure adopted for VFT varies between 2 and 10 steps. The contribution of this study is to promote a dialogue with the related literature and demonstrate how the VFT is used in the PSM field. In addition, based on an analysis of the most frequently adopted practices in the literature, we present a systematic structure of VFT in four stages that makes it more similar to the traditional PSM.

This paper aims at suggesting that welfare measurement could be based upon Sen’s capability approach (CA). This should allow establishing a “rational” framework improving how we aide the design and assessment of public policies. We propose the use of a multi-objective mathematical program as basis for measuring individual’s welfare and suggest that citizens with similar capabilities could be clustered together establishing targets for specific public policies aiming at improving or protecting the welfare on such well identified social groups.