Stakeholder Graph-Based Measure Selection for Consensus Convergence
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Solution Overview
Problem
Existing consensus-building techniques struggle with inappropriate numerical range settings by users lacking expertise, leading to difficulty in establishing consensus among stakeholders and increased processing load and power consumption due to repeated terminal operations by consultants.
Innovation Solution
A measure determination method using a computer system that generates stakeholder-specific graphs for evaluating measures, combines these graphs, and determines recommended measures based on a combined distribution graph, allowing stakeholders to select measures with high convergence.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If users set numerical ranges for evaluation indices without sufficient expertise, then the system allows flexible participation, but the consensus-building effectiveness deteriorates
Solution Approach 1:
The patent introduces a consultant as an intermediary who possesses expertise in evaluation. The consultant sets appropriate numerical ranges for evaluation indices based on their knowledge, mediating between the system's need for reliable evaluation and users' need for flexible participation. This resolves the contradiction by having the consultant guide the evaluation framework while users still participate freely within that framework.
Solution Approach 2:
The patent changes the parameter of who sets the numerical ranges from unskilled users to skilled consultants. This parameter change ensures that evaluation indices have appropriate numerical ranges set by experts, improving consensus-building effectiveness while still allowing flexible user participation in the evaluation process.
2Measurement precision
If consultants repeatedly perform terminal operations to help stakeholders select measures, then measurement accuracy improves, but processing load and power consumption increase
Solution Approach 1:
The patent enables stakeholders to independently perform measure selection by providing them with necessary information and tools. Instead of consultants repeatedly operating terminals to guide selection, stakeholders can autonomously evaluate and select measures based on the evaluation framework, reducing processing load and power consumption while maintaining selection accuracy through the expert-designed evaluation system.
Solution Approach 2:
The patent performs preliminary work by having consultants set appropriate numerical ranges for evaluation indices before the actual measure selection process. This preliminary action by experts prepares the evaluation framework in advance, allowing stakeholders to independently and accurately select measures without requiring repeated consultant intervention, thereby reducing processing load and power consumption.
3Reliability
If consultants intervene repeatedly to facilitate consensus, then consensus quality improves, but operation time increases
Solution Approach 1:
The patent performs preliminary action by having consultants establish appropriate numerical ranges for evaluation indices before the consensus-building process begins. This preparatory work creates a solid evaluation framework that enables stakeholders to independently reach high-quality consensus without requiring repeated consultant interventions, thereby reducing operation time while maintaining consensus quality.
Solution Approach 2:
The patent empowers stakeholders to independently conduct measure evaluation and selection using the expert-designed framework. This self-service approach allows stakeholders to autonomously build consensus based on properly configured evaluation indices, eliminating the need for repeated consultant operations and reducing overall operation time while preserving consensus quality.
Data Source
AI summary
Provided is a measure determination method implemented by a computer, the measure determination method including generating stakeholder-specific graphs representing a distribution of relative goodness for a plurality of measures individually evaluated by a plurality of stakeholders, generating a predetermined graph obtained by combining the stakeholder-specific graphs, and determining a recommended measure from the plurality of measures based on the predetermined graph.


