SimScore Overview

SimScore is a decision-making tool that simplifies group processes by collecting, analyzing, and prioritizing input. It reduces decision-making time, by leveraging collective intelligence.

1. Input Collection:
   • Users share their independent ideas or feedback through various channels, like surveys and forums.
   • The SimScore tool accepts the independent responses for processing.
2. Central Theme
   • After collecting input, SimScore runs an algorithm to identify the Central Theme the most agreed-upon point among all responses.
   • This highlights where the group’s consensus lies, helping focus on the most important responses participants shared.
3. Relationship Graph Calculation:
   • The relationship graph is generated by analyzing how the submitted ideas and feedback are similar to one another calculating Similarity Scores.
   • Each input becomes a node in the graph, and the algorithm determines connections based on a similarity score that measures how closely related the ideas are.
   • It calculates the overlap in keywords, themes, or concepts among responses, drawing edges between nodes where the similarity surpasses a set threshold. This results in a visual map that shows how different ideas are interconnected, making it easy to spot clusters of related thoughts.
4. Prioritization Calculation:
   • For prioritization, it starts with the centroidmost agreed upon statement as a reference point for evaluating the ideas.
   • Each idea gets similarity scored based on its proximity to the centroidmost agreed upon point, reflecting the level of agreement among participants.
   • The algorithm assigns priority scores to inputs, giving higher scores to those that align closely with the centroidconsensus. Once these scores are calculated, the inputs are ranked in descending order, creating a prioritized list that showcases the group's collective agreement and preferences.

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Conclusion

SimScore’s structured approach to gathering and processing input leads to more informed and efficient decision-making. By combining algorithmic analysis with human judgment, it helps groups navigate complex discussions and arrive at actionable conclusions more effectivelyfaster.

Central Theme

Similarity Score