Table 5.
Methods and techniques useful to implement the identified criteria in decision-support tools and systems.
| # | Typology/Sector | Criteria | Method-Technique-Action and Description |
|---|---|---|---|
| 1 | Decision Analysis/MCDA methodologies | C1, C2, C3, C4, C5, C6, C7, C8, C9 | Multi-Attribute Value Theory (MAVT): MCDA methodology that uses Value (Utility) functions to identify the most preferred alternative or to rank order the alternatives |
| 2 | Decision Analysis/MCDA methodologies | C1, C2, C3, C4, C5, C6, C7, C8, C9 | Outranking methods: They are based on the concept that an alternative may be dominant, with a certain degree, over another one |
| 3 | Decision Analysis/MCDA methodologies | C1, C2, C3, C4, C5, C6, C7, C8, C9 | Multi-objective optimization: An area of MCDA concerned with mathematical optimization problems involving more than one objective function to be optimized simultaneously |
| 4 | Decision Analysis/MCDA methodologies | C1, C2, C3, C4, C5, C6, C7, C8, C9 | Analytic hierarchy process (AHP): MCDA methodology that uses decomposition of the decision problem into a hierarchy of subproblems and evaluation of the relative importance of its various elements by pairwise comparisons |
| 5 | Decision Analysis/MCDA methodologies | C1, C2, C3, C4, C5, C6, C7, C8, C9 | Fuzzy logic: Introduces a formalization of vagueness and the notion of a degree of satisfaction of an object instead of an absolute evaluation |
| 6 | Decision Analysis/MCDA methodologies | C1, C2, C3, C4, C5, C6, C7, C8, C9 | Decision trees (decision analysis): A tool to model decisions, outcomes chances, and their possible consequences |
| 7 | Decision Analysis/MCDA methodologies | C1, C2, C3, C4, C5, C6, C7, C8, C9 | Value of Information (VoI): A methodology that can be used in tiers to explore uncertainty in risk assessment and decision-making |
| 8 | Decision Analysis/Mental modeling | C9 | Stakeholder profiling/need identification: The process of collecting and reviewing the opinions of relevant stakeholders with respect to the features, capabilities, usability of a decision-support tool |
| 9 | Decision Analysis/Mental modeling | C9 | Interviews/Focus Groups/Influence diagrams: Different techniques to perform mental modeling methodologies and present results |
| 10 | Decision Analysis/Software development | C2, C6, C7 | Decision-Support Systems: Building dedicating software for supporting decision-making |
| 11 | Risk Assessment-Management/Models | C3, C5 | Link-integration of models: Link or integration of various types of models (e.g., ERA-HH-exposure read-across grouping) in a decision-support tool |
| 12 | Risk Assessment-Management/Models | C3, C5 | Full life cycle/Cooper Stage Gate: Models and tools to cover the full life cycle (ERA, HH, LCIA, Social, EA, Risk Control) and connected to Cooper Stage Gate model. Provide multiple options for the user |
| 13 | Risk Assessment-Management/Risk management Measures | C2, C3, C6 | Types of Risk Management measures: Link-Integration of RMMs (e.g., Inventory of Technological Alternatives and Risk Management Measures (TARMMs), personalized risk management measures defined by the user or connection to the Exposure Control Efficacy Library (ECEL) database) |
| 14 | Risk Assessment-Management/Usability | C1, C2, C3, C4, C5, C7, C8 | Automatic conversion system: Introduction of an automatic conversion system, to improve usability of the system |
| 15 | Risk Assessment-Management/Usability | C1, C2, C3, C4, C5, C7, C8 | Quantal data: Support for quantal data in Human Health Hazard Assessment |
| 16 | Risk Assessment-Management/Usability | C1, C2, C3, C4, C5, C7, C8 | Nano-specific ontologies: A formal way to describe taxonomies and classification networks, essentially defining the structure of knowledge for various domains, they can be represented and shared through the recognized standard Web Ontology Language |
| 17 | Risk Assessment-Management/Usability | C1, C2, C3, C4, C5, C7, C8 | Assessment tree interface: Visual flow of sections (tiered approach/connected lifecycle models) |
| 18 | Software development/Features | C1, C2, C3, C4, C5, C6, C7, C8, C9 | Multiple interfaces: Web application accessible from any web browser, which can also be downloaded and installed in an intranet server. Also supports solutions to the confidentiality issue |
| 19 | Software development/Features | C1, C2, C3, C4, C5, C6, C7, C8, C9 | Graphical User Interfaces (GUIs): Minimum requirement for modern software-tools |
| 20 | Software development/Features | C1, C2, C3, C4, C5, C6, C7, C8, C9 | Bugs tracking system: Dedicated system, for efficiently improving Decision-Support Tools |
| 21 | Software development/Features | C1, C2, C3, C4, C5, C6, C7, C8, C9 | Feature request system: Dedicated system, for efficiently improving Decision-Support Tools |
| 22 | Software development/Features | C1, C2, C3, C4, C5, C6, C7, C8, C9 | Hosting environment: A crucial component for embedding models in a decision-support tool and allowing smooth operations for the user |
| 23 | Software development/Features | C1, C2, C3, C4, C5, C6, C7, C8, C9 | Appearance and usability of the web application: Smartly designed applications allow increased user-friendliness and improve risk/uncertainty communication |
| 24 | Software development/Features | C1, C2, C3, C4, C5, C6, C7, C8, C9 | Public pages: System users can select information for public viewing, allowing communication and partnerships with other stakeholders |
| 25 | Software development/Features | C1, C2, C3, C4, C5, C6, C7, C8, C9 | Data extraction/migration/interoperability features: Various import, migration, and export features increase user-friendliness of the systems and interoperability |
| 26 | Software development/Features | C1, C2, C3, C4, C5, C6, C7, C8, C9 | Easy registration/Multiple login methods: Improved usability of a system through multiple ways of identifying users and allowing them to register to the system |
| 27 | Software development/Features | C1, C2, C3, C4, C5, C6, C7, C8, C9 | Manual/Wiki: User guides in the form of a manual document or documented wiki pages can be used as technical communication documents |
| 28 | Software development/Features | C1, C2, C3, C4, C5, C6, C7, C8, C9 | Guidance: Interactive guidance of the user to the functionalities of a system |
| 29 | Software development/Features | C1, C2, C3, C4, C5, C6, C7, C8, C9 | User communication: Systems can use different types of communication protocols for informing users |
| 30 | Software development/Features | C1, C2, C3, C4, C5, C6, C7, C8, C9 | Case study examples: Documented applications available to the user for experimentation and information sharing |
| 31 | Software development/Features | C1, C2, C3, C4, C5, C6, C7, C8, C9 | Pairing of functionalities with stakeholder profiling: Driving software developments by implementing identified features through the mental modeling processes |
| 32 | Software development/Features | C1, C2, C3, C4, C5, C6, C7, C8, C9 | Expandable system (modular): System designed to handle multiple material and needs in the future |
| 33 | Software development/Features | C1, C2, C3, C4, C5, C6, C7, C8, C9 | Data gaps: Cover lack of data with modeling techniques |
| 34 | Software development/Features | C1, C2, C3, C4, C5, C6, C7, C8, C9 | API communication: Software to software communication |
| 35 | Software development/Features | C1, C2, C3, C4, C5, C6, C7, C8, C9 | Type of portal: HUB vs Integrated software |
| 36 | Software development/Features | C1, C2, C3, C4, C5, C6, C7, C8, C9 | Models: Basic characteristics of models for decision support: Multiple, Fast, Tailored, Embedded, Peer-reviewed, Integrated, Well-known |
| 37 | Software development/Features | C1, C2, C3, C4, C5, C6, C7, C8, C9 | Public projects: Availability of results to communities |
| 38 | Statistical methods/Methodology | C1, C5 | Decision Trees (machine learning): A method that uses a tree-like model of decisions and their possible consequences for identifying a strategy most likely to reach a goal |
| 39 | Statistical methods/Methodology | C1, C5 | Random forests: An ensemble learning method for classification, regression, and other tasks that operates by constructing a multitude of decision trees |
| 40 | Statistical methods/Methodology | C1, C5 | Sensitivity analysis: Evaluates the effect of changes in input values or assumptions on a model’s results |
| 41 | Statistical methods/Methodology | C1, C5 | Uncertainty analysis: Investigates the effects of lack of knowledge and other potential sources of error in the model |
| 42 | Statistical methods/Methodology | C1, C5 | Logistic regression: A predictive regression analysis that can be used to describe data and to explain the relationship between one dependent variable and one or more independent variables |
| 43 | Statistical methods/Methodology | C1, C5 | Neural networks: An alternative to regression models and other related statistical techniques in the areas of statistical prediction and classification |
| 44 | Statistical methods/Methodology | C1, C5 | Stable results: Calibration of models to be used in decision-support activities (sensitivity analysis and performance testing) |