The live demo and results of Skillonomy Tokenomics Modeling
This example clearly demonstrates Garuda AI's approach and methods used for modeling, analysis, and research of token economy systems properties. We use Behavior Algebra specifications for the formalization of the systems and the Insertion Modeling technique for verification. The Skillonomy's tokenomics solution is considered as an example of the Algebraic Approach application. The formalization and properties analysis was performed with the Garuda AI Platform.
Skillonomy project brief description
Skillonomy is a decentralized protocol for talent management and skill monetization. It enables one to tokenize the education process, creates motivation and economic stimuli for students, tutors, and course creators on an online educational platform. Integrate real task form market and company requests in the educational process and create a new business model in education based on a performance and success fee. Change the platform organization to a decentralized structure. Involve the community in management and governance.
Model Overview - Behaviours
The high-level behavior of the system agents is defined by Use Case Maps (UCM) in terms of actions sequences.
Model Overview - Actions
The transactions between agent states are specified by a set of actions(MSC diagrams). A particular action consists of the three components: algebraic formula over attributes in the precondition, postcondition that has formula over changed attributes and illustration of the process(events, etc).
Usually, the process of formalization finds as much as 70% of bugs and weaknesses in the specifications. A lot of findings were spotted and possible fixes discussed with stakeholders. Next, experiments on the model provided an understanding of system trends and thresholds dependant upon the parameter values and agent actions. Corresponding calibration of the parameters values have been done to achieve the expected behavior. The results of modeling are shown on the charts that have their own settings of observed attributes and actions.
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