Decision Support and Problem Formulation Activity

2011 ◽  
pp. 192-199 ◽  
Author(s):  
David Paradice
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Decision Maker ◽  
Support Systems ◽  
Problem Formulation ◽  
Decision Makers ◽  
Problem Situation ◽  
Continue Development ◽  
Decisionmaking Process ◽  
Decision Choices

While decision choices are certainly important and warrant appropriate attention, early stages of the decisionmaking process may be even more critical in terms of needing adequate support. The alternatives from which a decision maker may be able to choose are integrally tied to the assumptions made about the problem situation. Consequently, decision support systems (DSSs) may be more effective in helping decision makers to make good choices when support for problem formulation is provided. Research validates the notion that support for problem formulation and structuring leads to better decisions. This article explores this concept and looks at opportunities in emerging software trends to continue development of problem formulation support in DSS-type settings.

Modeling Cooperative Decision Support Systems with Hybrid Agents

2013 ◽  
Vol 5 (3) ◽  
pp. 31-48 ◽  
Author(s):  
Abdelkader Adla
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Decision Maker ◽  
Support Systems ◽  
Decision Makers ◽  
Agent Technology ◽  
Artificial Agent ◽  
Action Execution ◽  
Human Agent ◽  
Multi Agents

In this paper, the authors propose to use Multi-Agents Systems (MAS) to model Cooperative Decision Support Systems (DSS). These systems support the collaboration of two kinds of agents: the human agent (the decision-maker or the user) and the artificial agent (machine) to solve jointly a problem and make a decision. In this way, the authors take advantage of the capacities of both the decision-maker and the machine. The novelty of the proposed approach is the modeling of Cooperative DSS using agent technology by coupling two MAS, the first is reactive and the latter is cognitive or deliberative. The resulting system is designed to support operators, as decision-makers during contingencies. Using the system, the operators should be able to: gather information about the incident location, access databases related to the incident, activate predictive modeling programs, support analyses, and monitor the progress of the situation and action execution. A simple scenario is given, to illustrate the feasibility of the proposal.

Business Intelligence and Data Mining

2013 ◽  
pp. 57-70
Author(s):  
Zsolt T. Kardkovács
Keyword(s):  
Data Mining ◽  
Decision Support ◽  
Strategic Planning ◽  
Decision Support Systems ◽  
Business Intelligence ◽  
Business Processes ◽  
Support Systems ◽  
Decision Makers ◽  
Key Factors ◽  
Short Introduction

Whenever decision makers find out that they want to know more about how the business works and progresses, or why customers do what they do, then data miners are summoned, and business intelligence is to be built or altered. Data mining aims at retrieving valid, interesting, explicable connection between key factors for either operative reporting or supporting strategic planning. While data mining discovers static connections between factors, business intelligence visualizes relevant data for decision makers in order to make them identify fast changes and analyze precisely business states. In this chapter, the authors give a short introduction for data oriented decision support systems with data mining and business intelligence in it. While these techniques are widely used in business processes, there are much more bad practices than good ones. We try to make an attempt to demystify and clear the myths about these technologies, and determine who should and how (not) to use them.

Visualization-Based Decision Support Systems

2013 ◽  
pp. 162-180
Author(s):  
Vicki L. Sauter ◽  
Srikanth Mudigonda ◽  
Ashok Subramanian ◽  
Ray Creely
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Support Systems ◽  
Lessons Learned ◽  
Decision Makers ◽  
Specific Information ◽  
Visualization Software ◽  
General Rules ◽  
Visualization Technology ◽  
Visualization Algorithms

Increasingly, decision makers are incorporating large quantities of interrelated data in their decision making. Decision support systems need to provide visualization tools to help decision makers glean trends and patterns that will help them design and evaluate alternative actions. While visualization software that might be incorporated into decision support systems is available, the literature does not provide sufficient guidelines for selecting among possible visualizations or their attributes. This paper describes a case study of the development of a visualization component to represent regional relationship data. It addresses the specific information goals of the target organization, various constraints that needed to be satisfied, and how the goals were achieved via a suitable choice of visualization technology and visualization algorithms. The development process highlighted the need for specific visualizations to be driven by the specific problem characteristics as much as general rules of visualization. Lessons learned during the process and how these lessons may be generalized to address similar requirements is presented.

DSS and Multiple Perspectives of Complex Problems

2011 ◽  
pp. 286-295 ◽  
Author(s):  
David Paradice ◽  
Robert A. Davis
Keyword(s):  
Decision Support ◽  
Information Systems ◽  
Decision Support Systems ◽  
Support Systems ◽  
Collaborative Work ◽  
Vantage Point ◽  
Decision Makers ◽  
Complex Problems ◽  
Knowledge Based ◽  
Spreadsheet Models

Decision support systems have always had a goal of supporting decision-makers. Over time, DSS have taken many forms, or many forms of computer-based support have been considered in the context of DSS, depending on one’s particular perspective. Regardless, there have been decision support systems (DSS), expert systems, executive information systems, group DSS (GDSS), group support systems (GSS), collaborative systems (or computer-supported collaborative work (CSCW) environments), knowledge-based systems, and inquiring systems, all of which are described elsewhere in this encyclopedia. The progression of decision support system types that have emerged follows to some degree the increasing complexity of the problems being addressed. Some of the early DSS involved single decision-makers utilizing spreadsheet models to solve problems. Such an approach would be inadequate in addressing complex problems because one aspect of problem complexity is that multiple stakeholders typically exist. Baldwin (1993) examined the need for supporting multiple views and provides the only attempt found in the information systems literature to operationalize the concept of a perspective. In his work, a view is defined as a set of beliefs that partially describe a general subject of discourse. He identified three major components of a view: the belief or notion to convey, a language to represent the notion, and a subject of discourse. He further described notions as comprising aspects and a vantage point. Aspects are the characteristics or attributes of a subject or situation that a particular notion emphasizes. A vantage point is described by the level of detail (i.e., overview or detailed analysis). Assuming the subject of discourse can be identified with the notion, Baldwin described how differences in views may occur via differences in the notion, the language, or both.

scholarly journals Contribution of decision support systems to water management improvement in basins with high evaporation in Mediterranean climates

2019 ◽  
Vol 50 (4) ◽  
pp. 1020-1036 ◽  
Author(s):  
Verónica Ruiz-Ortiz ◽  
Santiago García-López ◽  
Abel Solera ◽  
Javier Paredes
Keyword(s):  
Water Management ◽  
Decision Support ◽  
Surface Water ◽  
Decision Support Systems ◽  
Support Systems ◽  
Management Strategies ◽  
Decision Makers ◽  
Basin Management ◽  
Surface Water And Groundwater

Abstract The entry into force of Directive 2000/60/EC of the European Parliament and the Council of 23 October 2000 established a new model for the management and protection of surface water and groundwater in Europe. In this sense, a thorough knowledge of the basins is an essential step in achieving this European objective. The utility of integrative decision support systems (DSS) for decision-making in complex systems and multiple objectives allows decision-makers to identify characteristics and improve water management in a basin. In this research, hydrological and water management resource models have been combined, with the assistance of the DSS AQUATOOL, with the aim of deepening the consideration of losses by evaporation of reservoirs for a better design of the basin management rules. The case study treated is an Andalusian basin of the Atlantic zone (Spain). At the same time, different management strategies are analysed based on the optimization of the available resources by means of the conjunctive use of surface water and groundwater.

scholarly journals Best Student Selection Using Extended Promethee II Method

2017 ◽  
Author(s):  
Andysah Putera Utama Siahaan ◽  
Mesran Mesran
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Educational Level ◽  
Support Systems ◽  
Decision Makers ◽  
Student Selection ◽  
Level Of Education ◽  
Promethee Ii ◽  
Education Leaders ◽  
Higher Education Leaders

At the end of learning at an educational level, leaders often perceive difficulties in determining the best students at a certain level of education. Cumulative Achievement Index may not be used for decision-makers in determining the best students. There are criteria other criteria that influence them are actively organize, have never done a repair value, never follow short semester, never leave. Using these criteria and using Multi-Criteria Decision Making (MCDM) based methods applied to decision support systems can deliver the expected outcomes of higher education leaders. Many methods can be used on decision support systems such as Promethee, Promethee II, Electre, AHP, SAW, or TOPSIS. In this discussion, the author uses Extended Promethee II method in determining the best student at a college.

Distributed Model Management

2011 ◽  
pp. 272-277 ◽  
Author(s):  
Omar F. El-Gayar ◽  
Amit V. Deokar
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Support Systems ◽  
Dynamic Environment ◽  
Decision Makers ◽  
Model Management ◽  
Internet Technology ◽  
Decision Models ◽  
Distributed Model ◽  
Distributed Decision

Modern organizations are faced with numerous information management challenges in an increasingly complex and dynamic environment. Vast amounts of data and myriads of models of reality are routinely used to predict key outcomes. Decision support systems (DSS) play a key role in facilitating decision making through management of quantitative models, data, and interactive interfaces (Power, 2000). The basic thrust of such applications is to enable decision-makers to focus on making decisions rather than being heavily involved in gathering data and conceiving and selecting analytical decision models. Accordingly, the number and complexity of decision models and of modeling platforms has dramatically increased, rendering such models a corporate (and national) resource (Muhanna & Pick, 1994). Further, Internet technology has brought many new opportunities to conduct business electronically, leading to increased globalization. Managers and decision makers are increasingly collaborating in distributed environments in order to make efficient and effective use of organizational resources. Thus, the need for distributed decision support in general, and model sharing and reuse in particular, is greater today than ever before. This has attracted significant attention from researchers in information systems-related areas to develop a computing infrastructure to assist such distributed model management (Krishnan & Chari, 2000). In this article, we focus on distributed model management advances, and the discussion is organized as follows. The next section provides a background on model management systems from a life-cycle perspective. This is followed by a critical review of current research status on distributed decision support systems from a model management viewpoint with a particular emphasis on Web services. Future trends in this area are then discussed, followed by concluding remarks.

The forest management decision support system project

1991 ◽  
Vol 67 (6) ◽  
pp. 622-628 ◽  
Author(s):  
Dan Bulger ◽  
Harold Hunt
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Forest Management ◽  
Decision Support System ◽  
Decision Support Systems ◽  
Support System ◽  
Support Systems ◽  
Decision Makers ◽  
Management Decision ◽  
Management Decision Support

The focus of a decision support system is much different from Management Information Systems (MIS) and data-based "decision support systems". Decision support systems, as defined by the authors, focus on decisions and decision makers, and on information. Technology is treated as a tool and data as the raw material. In many traditional systems the focus is on the technology, and the data is the "information", while decision makers are, to some extent, externalized.The purpose of the Forest Management Decision Support System (FMDSS) project is to develop a set of software tools for creating forest management decision support systems. This set of tools will be used to implement a prototype forest management decision support system for the Plonski forest, near Kirkland Lake, Ontario.There are three critical ingredients in building the FMDSS, these are: (1) knowledge of the decision making process, (2) knowledge of the forest, and (3) the functionality of underlying support technology. The growing maturity of the underlying technology provides a tremendous opportunity to develop decision support tools. However, a significant obstacle to building FMDSS has been the diffuse nature of knowledge about forest management decision making processes, and about the forest ecosystem itself. Often this knowledge is spread widely among foresters, technicians, policy makers, and scientists, or is in a form that is not easily amenable to the decision support process. This has created a heavy burden on the project team to gather and collate the knowledge so that it could be incorporated into the function and design of the system. It will be difficult to gauge the success of this exercise until users obtain the software and begin to experiment with its use.

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