A Visualization Framework for Real Time Decision Making in a Multi-Input Multi-Output System

Human beings have the capacity to make quick and accurate decisions when multiple objectives are involved provided they have access to all the relevant information. Accurate visual measures/decision surfaces (maps) are critical to the effectiveness of this process. This paper introduces a methodolog...

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Bibliographic Details
Published in:IEEE systems journal 2008-03, Vol.2 (1), p.129-145
Main Authors: Ashok, P., Tesar, D.
Format: Article
Language:English
Subjects:
3-D visualization
Bayesian methods
Complex systems
Concrete
Decision making
decision scenarios
Decision support systems
decision surface norms
decision surfaces
Displays
Graphics
Human
Humans
large multi-input multi-output (MIMO) systems
Mathematical models
Methodology
MIMO
Networks
Norms
performance maps
Real time systems
Studies
Visual
Visualization
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Summary:Human beings have the capacity to make quick and accurate decisions when multiple objectives are involved provided they have access to all the relevant information. Accurate visual measures/decision surfaces (maps) are critical to the effectiveness of this process. This paper introduces a methodology that allows one to create a visual decision making interface for any multi-input multi-output (MIMO) system. In this case, the MIMO is thought of in the broadest sense to include battlefield operations, complex system design, and human support systems (rehabilitation). Our methodology starts with a Bayesian causal network approach to modeling the MIMO system. Various decision making scenarios in a typical MIMO system are presented. This is then followed by a description of the framework that allows for the presentation of the relevant scenario dependent data to the human decision maker (HDM). This presentation is in the form of 3-D surface plots called decision surfaces. Additional decision making tools (norms) are then presented. These norms allow for single value numbers to be presented along with the decision surfaces to better aid the HDM. We then present some applications of the framework to representative MIMO systems. This methodology easily adapts to systems that grow bigger and also when two or more systems are combined to form a larger system.
ISSN:1932-8184
1937-9234
DOI:10.1109/JSYST.2008.916060