Influence diagrams: Predictive Approach in Decision Support Systems

Amin Hosseinian-Far, Hamid Jahankhani, Elias Pimenidis

Research output: Contribution to journalArticleResearchpeer-review

Abstract

Nowadays computerized Decision Support Systems (DSS) are essential for many private and governmental organizations. The main clients of such information systems are mainly managers at various levels, although other employees might need to make rapid decisions using such a system. There are various definitions and categorizations for DSS. A generalized delineation for Decision Support Systems would be: interactive information systems which assist the decision makers and planners in their business and decision making activities. Influence Diagrams (ID) can be utilized for the design of DSS. Influence Diagrams are visual representations of probability and uncertainty. Decisions, related objectives, uncertainties and elements can be visualised and inferred using ID. The main advantage of an ID is its provision of visual conceptual representation of the problem domain. ID can be employed in a DSS in various contexts, e.g. in environmental sustainability framework. In such a scenario, DSS designed with probabilistic networks and ID would assist the decision makers to precisely predict the effects of climate change policy plans. Hence integration and use of ID would provide a more accurate platform for planning. In addition to that, fewer planning failures and less investment fiascos together with thorough sustainable growth would assist the current economy recovery. This paper reflects on the potentiality of using Influence Diagrams and probabilistic inference for knowledge representation of DSS. Furthermore, the predictive nature of probabilistic extrapolation and probability inference in the context of environmental sustainability would be critically assessed. There are number of Integrated Development Environments (IDE) that provide built-in Influence Diagrams for the programmer. Moreover, available development environments providing built-in Influence Diagrams are introduced and compared.
Original languageEnglish
Pages (from-to)16-22
JournalStrategic Management
Publication statusPublished - 2012

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Decision support systems
Sustainable development
Information systems
Planning
Knowledge representation
Extrapolation
Climate change
Managers
Decision making
Personnel
Recovery
Industry

Cite this

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title = "Influence diagrams: Predictive Approach in Decision Support Systems",
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author = "Amin Hosseinian-Far and Hamid Jahankhani and Elias Pimenidis",
year = "2012",
language = "English",
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journal = "Strategic Management",
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Influence diagrams: Predictive Approach in Decision Support Systems. / Hosseinian-Far, Amin; Jahankhani, Hamid; Pimenidis, Elias.

In: Strategic Management, 2012, p. 16-22.

Research output: Contribution to journalArticleResearchpeer-review

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AB - Nowadays computerized Decision Support Systems (DSS) are essential for many private and governmental organizations. The main clients of such information systems are mainly managers at various levels, although other employees might need to make rapid decisions using such a system. There are various definitions and categorizations for DSS. A generalized delineation for Decision Support Systems would be: interactive information systems which assist the decision makers and planners in their business and decision making activities. Influence Diagrams (ID) can be utilized for the design of DSS. Influence Diagrams are visual representations of probability and uncertainty. Decisions, related objectives, uncertainties and elements can be visualised and inferred using ID. The main advantage of an ID is its provision of visual conceptual representation of the problem domain. ID can be employed in a DSS in various contexts, e.g. in environmental sustainability framework. In such a scenario, DSS designed with probabilistic networks and ID would assist the decision makers to precisely predict the effects of climate change policy plans. Hence integration and use of ID would provide a more accurate platform for planning. In addition to that, fewer planning failures and less investment fiascos together with thorough sustainable growth would assist the current economy recovery. This paper reflects on the potentiality of using Influence Diagrams and probabilistic inference for knowledge representation of DSS. Furthermore, the predictive nature of probabilistic extrapolation and probability inference in the context of environmental sustainability would be critically assessed. There are number of Integrated Development Environments (IDE) that provide built-in Influence Diagrams for the programmer. Moreover, available development environments providing built-in Influence Diagrams are introduced and compared.

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