scholarly journals A Taxonomy of Sequential Decision Support Systems

10.28945/2382 ◽  
2001 ◽  
Author(s):  
Anil Aggarwal
Keyword(s):  
Decision Support ◽  
Decision Makers ◽  
Internet Technology ◽  
Sequential Decision Making ◽  
Sequential Decision ◽  
Sequential Decisions ◽  
Research Support ◽  
Hierarchical Levels ◽  
Decision Environment ◽  
Recurring Tasks

Advances in information technology and explosion of internet technology is creating new professional users, across and within countries. These users are looking at technology to provide decision support for non-recurring tasks, to provide prototyping capabilities and to provide research support. However, organizational decision environment is also changing, creating havoc for system builders who must match changing technology to changing decision environment. This paper focuses on one such technology, namely, Decision Support System (DSS) and one such decision environment, namely, sequential decisions. It is argued that the next millennium DSS must focus on the 'D' of DSS because of the complexities of the decisions they are trying to support. These DSSs, called SDSSs, are further examined in the context of data-dialog-model- communication. Communication component is needed because of the complexity of sequential decision-making which spans across several hierarchical levels or involves several decision makers at the same level.

The Effects of Amount of Information Provided and Feedback of Results on Decision Making Efficiency

1965 ◽  
Vol 7 (6) ◽  
pp. 513-520 ◽  
Author(s):  
Charles H. Hammer ◽  
Seymour Ringel
Keyword(s):  
Decision Making ◽  
Decision Makers ◽  
Sequential Decision Making ◽  
Sequential Decision ◽  
Decision Accuracy ◽  
Independent Variables ◽  
Amount Of Information ◽  
Taking Action

Sixty subjects worked a series of sequential decision making tasks in which the amount of information provided and feedback of results were the independent variables. Data were collected on decision accuracy, confidence in decision accuracy, and judged sufficiency of the information provided. Accuracy, confidence in accuracy, and ratings of sufficiency increased as amount of information provided was increased. Feedback produced increases in decision accuracy only. For forty percent of all correct responses, subjects judged the information provided to be insufficient as a basis for taking action. These data strongly suggest that lack of confidence in their ability to make accurate decisions may cause some decision makers to delay taking action even when they are able to make an accurate decision on the basis of the information available.

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.

scholarly journals Social Influence Undermines the Wisdom of the Crowd in Sequential Decision Making

2020 ◽  
Author(s):  
Vincenz Frey ◽  
Arnout van de Rijt
Keyword(s):  
Decision Making ◽  
Social Information ◽  
Experimental Studies ◽  
Decision Makers ◽  
Sequential Decision Making ◽  
The Internet ◽  
Central Tendency ◽  
Sequential Decision ◽  
Group Members ◽  
Positive Effect

Teams, juries, electorates, and committees must often select from various alternative courses of action what they judge to be the best option. The phenomenon that the central tendency of many independent estimates is often quite accurate—“the wisdom of the crowd”—suggests that group decisions based on plurality voting can be surprisingly wise. Recent experimental studies demonstrate that the wisdom of the crowd is further enhanced if individuals have the opportunity to revise their votes in response to the independent votes of others. We argue that this positive effect of social information turns negative if group members do not first contribute an independent vote but instead cast their votes sequentially such that early mistakes can cascade across strings of decision makers. Results from a laboratory experiment confirm that when subjects sequentially state which of two answers they deem correct, majorities are more often wrong when subjects can see how often the two answers have been chosen by previous subjects than when they cannot. As predicted by our theoretical model, this happens even though subjects’ use of social information improves the accuracy of their individual votes. A second experiment conducted over the internet involving larger groups indicates that although early mistakes on easy tasks are eventually corrected in long enough choice sequences, for difficult tasks wrong majorities perpetuate themselves, showing no tendency to self-correct. This paper was accepted by Yuval Rottenstreich, decision analysis.

Students As Sequential Decision-Makers: Quantifying the Impact of Problem Knowledge and Process Deviation on the Achievement of Their Design Problem Objective

2018 ◽  
Author(s):  
Murtuza Shergadwala ◽  
Ilias Bilionis ◽  
Jitesh H. Panchal
Keyword(s):  
Decision Making ◽  
Design Process ◽  
Design Problem ◽  
Design Space ◽  
Research Question ◽  
Decision Makers ◽  
Sequential Decision ◽  
Sequential Decisions ◽  
Decision Making Model ◽  
The Impact

Factors such as a student’s knowledge of the design problem and their deviation from a design process impact the achievement of their design problem objective. Typically, an instructor provides students with qualitative assessments of such factors. To provide accurate assessments, there is a need to quantify the impact of such factors in a design process. Moreover, design processes are iterative in nature. Therefore, the research question addressed in this study is, How can we quantify the impact of a student’s problem knowledge and their deviation from a design process, on the achievement of their design problem objective, in successive design iterations? We illustrate an approach in the context of a decision-making scenario. In the scenario, a student makes sequential decisions to optimize a mathematically unknown design objective with given constraints. Consequently, we utilize a decision-making model to abstract their design process. Their problem knowledge is quantified as their belief about the feasibility of the design space via a probability distribution. Their deviation from the decision-making model is quantified by introducing uncertainty in the model. We simulate cases where they have a combination of high (or low) knowledge of the design problem and high (or low) deviation in their design process. The results of our simulation study indicate that if students have a high (low) deviation from the modeled design process then we cannot (can) infer their knowledge of the design problem based on their problem objective achievement.

scholarly journals A Multi-Criteria Decision Support Concept for Selecting the Optimal Contractor

2021 ◽  
Vol 11 (4) ◽  
pp. 1660 ◽  
Author(s):  
Ivan Marović ◽  
Monika Perić ◽  
Tomaš Hanak
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Project Performance ◽  
Decision Makers ◽  
Construction Project Management ◽  
Analytic Hierarchy ◽  
Conflicting Demands ◽  
Decision Making Problem ◽  
Scientific Approach ◽  
Hierarchy Process

A way to minimize uncertainty and achieve the best possible project performance in construction project management can be achieved during the procurement process, which involves selecting an optimal contractor according to “the most economically advantageous tender.” As resources are limited, decision-makers are often pulled apart by conflicting demands coming from various stakeholders. The challenge of addressing them at the same time can be modelled as a multi-criteria decision-making problem. The aim of this paper is to show that the analytic hierarchy process (AHP) together with PROMETHEE could cope with such a problem. As a result of their synergy, a decision support concept for selecting the optimal contractor (DSC-CONT) is proposed that: (a) allows the incorporation of opposing stakeholders’ demands; (b) increases the transparency of decision-making and the consistency of the decision-making process; (c) enhances the legitimacy of the final outcome; and (d) is a scientific approach with great potential for application to similar decision-making problems where sustainable decisions are needed.

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