Decision Support System: Overview, Different Types and Elements

Fernando, Joey G.; Baldelovar, Myelinda

doi:10.36647/ttidmkd/02.02.a003

Cited by 2 publications

(2 citation statements)

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“…Similar to S6, S5, and S6, entropies and energies will not be calculated for S4. Summing the states reveals that their total number is 256, which coincides with the expected theoretical value of 4 4 , precisely the number of combinations of four elements taken four at a time (since three pointers have been fixed to the same value). The previous table possesses greater expressive power, as it subdivides scenario S4 into sub-scenarios, thereby deconstructing the information.…”

Section: S4 Scenariosupporting

confidence: 67%

“…Scoot Morton as a management decision system (MDS) [2], but in the following years this type of solution has increasingly become an interactive computer-based solution to help any decision-maker who uses a large amount of data and information to solve unstructured problems, i.e., problems with a large number of variables, or with uncertainty or info-incompleteness [3]. As reported in [4] a way to classify DSSs is if they are (i) document driven, (ii) model driven, (iii) communication driven, (iv) data driven, and (v) knowledge driven. As highlighted in [5], most current DSSs are limited to merely finding the situation, that is, a kind of diagnostics, and do not have a unified integrated mechanism to offer adequate solutions.…”

Section: Introduction: Dss Short Overviewmentioning

confidence: 99%

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Decision Support System Driven by Thermo-Complexity: Scenario Analysis and Data Visualization

Iovane,

Chinnici

2024

Applied Sciences

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The present modelling aims to construct a computational information representation system useful for decision support system (DSS) solutions in the realization of intelligent systems or complex systems analysis solutions. Starting from an n-dimensional space (with n ≥ 7) represented by problem variables (referred to as CSF—Critical Success Factors), a dimensional embedding procedure is used to transition to a two-dimensional space. In the two-dimensional space, thanks to new lattice motion algorithms, the decision support system can determine the optimal solution with a lower computational cost based on the decision-maker’s preferences. Finally, thanks to an algorithm that takes into account the hierarchical order of importance of the seven CSFs as per the expert’s liking or according to his optimization logics, a return is made to the n-dimensional space and the final solution in the original space. As we will see, the starting and ending states in the n-dimensional space (referred to as micro-states) when projected into the two-dimensional space generate states (referred to as macro-states) which are degenerate. In other words, the correspondence between micro-states and macro-states is not one-to-one, as multiple micro-states correspond to one macro-state. Therefore, in relation to the decision-maker’s preferences, it will be the responsibility of the decision support system to provide the decision-maker with the micro-state of interest in the n-dimensional space (dimensional emergence procedure), starting from the obtained optimal macro-state. This result can be achieved starting from a flat chain of sensors capable of measuring/emulating certain specific parameters of interest. As we will see, it emerges that by considering random–exhaustive rolling value paths in order to track and potentially intervene to rebalance a dynamic system representing the state of stress/sensing of a system of interest, we are using the most general and, therefore, complex hypotheses of ergodic theory. In this work, we will focus on the representation of information in n-dimensional and two-dimensional spaces, as well as construct evaluation scenarios. We will also show the results of the decision support system in some cases of specific interest, thanks to a specific lattice motion algorithm of the realized decision-making environment.

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Section: S4 Scenariosupporting

confidence: 67%