About us
Possibility LGMs have been used successfully to analyze processes, networks, systems and architectures over a wide range of engineering domains. The capability to link LGMs together facilitates the development of large models. The logic structures associated with the LGM scale easily so as new information becomes available the model accommodates it. There is no need “start from scratch” as the scope of the analysis grows.
The metrics calculated for the alternatives can be a combination of qualitative (linguistic) and quantitative values. LED uses LGMs to construct inferential models for rank ordering using these metrics as inputs. Many LED inferential models use approximate reasoning (AR) to emulate expert judgment. AR-based models use forward chaining rule bases that operate on linguistic variables – natural language expressions that are well suited to expert elicitation. Direct linkage between the possibility and inferential LGMs makes it possible to look at many options in a single analysis run.
Applications
LED models have been used for a wide range of systems, risk and decision analysis problems --
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Analysis of espionage, sabotage and terrorism
- Design of scientific infrastructures
- Evaluation of the safety and security of complex systems
- Allocation of resources for advanced technology development
- Design of systems architectures and concepts of operations
- Forensic studies of safety and security incidents
Case study summaries are available directly from LETech.
