Combining Heterogeneous Indicators by Adopting Adaptive MCDA: Dealing with Uncertainty

Pileggi, Salvatore F.

doi:10.1007/978-3-030-77980-1_39

Cited by 2 publications

(1 citation statement)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…IndShaker implements a computational method previously proposed [9]. Such a method has been applied, among others, to model and analyse a case study on Sustainable Global Development [10], as well as it has been further discussed in terms of bias and uncertainty management [11]. Additionally, the knowledge-base underpinning the tool proposed in this paper is linked to existing vocabularies (see Section 4).…”

Section: Previous Workmentioning

confidence: 99%

IndShaker: A Knowledge-based Approach to Enhance Multi-perspective System Dynamics Analysis

Pileggi¹

2022

Preprint

View full text Add to dashboard Cite

Decision making as a result of system dynamics analysis requires, in practice, a straightforward and systematic modelling capability as well as a high-level of customisation and flexibility to adapt to situations and environments that may vary very much from each other. While in general terms a completely generic approach could be not as effective as ad-hoc solutions, the proper application of modern technology may facilitate agile strategies as a result of a smart combination of qualitative and quantitative aspects. In order to address such a complexity, we propose a knowledge-based approach that integrates the systematic computation of heterogeneous criteria with open semantics. The holistic understanding of the framework is described by a reference architecture and the proof-of-concept prototype developed can support high-level system analysis, as well as it suitable within a number of applications contexts - i.e. as a research/educational tool, communication framework, gamification and participatory modelling. Additionally, the knowledge-based philosophy, developed upon Semantic Web technology, increases the capability in terms of holistic knowledge building and re-use via interoperability. Last but not least, the framework is designed to constantly evolve in the next future, for instance by incorporating more advanced AI-powered features.

show abstract

Section: Previous Workmentioning

confidence: 99%

IndShaker: A Knowledge-based Approach to Enhance Multi-perspective System Dynamics Analysis

Pileggi¹

2022

Preprint

View full text Add to dashboard Cite

show abstract

IndShaker: A Knowledge-Based Approach to Enhance Multi-Perspective System Dynamics Analysis

Pileggi

2022

Modelling

View full text Add to dashboard Cite

Decision making as a result of system dynamics analysis requires, in practice, a straightforward and systematic modeling capability as well as a high-level of customization and flexibility to adapt to situations and environments that may vary very much from each other. While in general terms a completely generic approach could be not as effective as ad hoc solutions, the proper application of modern technology may facilitate agile strategies as a result of a smart combination of qualitative and quantitative aspects. In order to address such complexity, we propose a knowledge-based approach that integrates the systematic computation of heterogeneous criteria with open semantics. The holistic understanding of the framework is described by a reference architecture and the proof-of-concept prototype developed can support high-level system analysis, as well as being suitable within a number of applications contexts—i.e., as a research/educational tool, communication framework, gamification and participatory modeling. Additionally, the knowledge-based philosophy, developed upon Semantic Web technology, increases the capability in terms of holistic knowledge building and re-use via interoperability. Last but not least, the framework is designed to constantly evolve in the next future, for instance by incorporating more advanced AI-powered features.

show abstract

Combining Heterogeneous Indicators by Adopting Adaptive MCDA: Dealing with Uncertainty

Cited by 2 publications

References 27 publications

IndShaker: A Knowledge-based Approach to Enhance Multi-perspective System Dynamics Analysis

IndShaker: A Knowledge-based Approach to Enhance Multi-perspective System Dynamics Analysis

IndShaker: A Knowledge-Based Approach to Enhance Multi-Perspective System Dynamics Analysis

Contact Info

Product

Resources

About