Causality: models, reasoning, and inference
Causality: models, reasoning, and inference
| Hi-index | 0.00 |
To effectively evaluate and analyze R&D performance, it is necessary to measure the relative importance of performance analysis factors and quantitative analysis methods that consider the objectivity and relevance of detail factors that constitute performance evaluation.This study suggests a framework for R&D performance evaluations by computing weights through an AHP (Analytical Hierarchy Process) expert survey and by applying a Bayesian Network approach whereby, through which, giving objectivity and allowing inference analyses. This framework can be used as a performance analysis indicator, which uses input and output performance factors in order to perform quantitative analysis for projects. We can quantitatively define the satisfactory level of each project and each performance analysis factor by assigning probability values. It is possible to analyze the relationship between project evaluation results (qualitative evaluation) and performance analysis indicator (quantitative performance). This performance analysis framework can infer posteriori probability using the prior probability and the likelihood function of each performance factor. In addition, by inferring the relationships among performance factors, it allows performing probability analyses on the successful and unsuccessful factors, which can provide further feedback.In conclusion, the framework would improve the national R&D program in terms of financial investment efficiency by aligning budget allocation and performance evaluation.