SKILLONOMY

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 (Visit SKILLONOMY site).

 

Model Overview - Behaviours

 

SKILLONOMY project is an educational online platform that tokenizes productive activities in the learning process and is focused on gaining monetized online knowledge and skills. The SKILLONOMY ecosystem is built around an IT platform that allows participants to effectively build and administer the relationships that are related to training, investing and sharing experience.

Developing the SKILLONOMY project required a set of essential functions of the blockchain for ensuring a stable and efficient system that works.

The main purposes of the tokenomics model formalization of the SKILLONOMY project are:

  • the search for modeling errors, such as finding failings or possible contradictions;
  • the search for effective scenarios of the system in the model, etc.;
  • the possibilities for analyzing and predicting the model; and
  • the possibilities for analyzing the feasibility of project financing.

The process of the formalization of the tokenomic project consists of the following steps: the selection of the agents and definition of their attributes corresponding to the level of abstraction demanded, definition of agents’ actions and the design of agents’ behavior.

According to the project requirements, we determined the next set of agents for this model: coaches, managers, node owners, the platform owner, holders of tokens and the stock exchange, and students.  Six categories of students are described:

  • the first agent includes students whose average grade is equal 1,
  • the second agent includes students whose average grade is equal 2,
  • the third agent includes students whose average grade is equal 3,
  • the fourth agent includes students whose average grade is equal 4,
  • the fifth agent includes students whose average grade is between 3 and 4 and the sixth agent includes students whose average grade is equal 5.

Environment

 

The agent description will be present in the IMS language in the following form:

NODE_OWNER{

   tokenAvailable    : real,

   tokenSkillMining : real,

   tokenForSale      : real

   ….

   income: real

  }

where NODE_OWNER is an agent name and tokenAvailable, tokenSkillMining, tokenForSale and income -  it’s attributes:

  1. tokenAvailable- available tokens of the node owner;
  2. tokenSkillMining - number of tokens for the common skillmaining pool (tokens that are mined);
  3. tokenForSale - number of tokens allocated for sale;
  4. income - profit of the node owner (in arbitrary units).

 

Actions

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).

tokenOpenICOSale - Sale of tokens in the period TGE OPEN SALE. Open sale period. 1.The platform sells a monthly rate of tokens. 2.The platform contributes to the reserve fund what is not sold. 3.Platform receives cash income. 4.Students (student good) buy their part of tokens according to the pOICOXX ratio.

All purchased locked until the end of the open sale. Tokens are locked.

 

Behaviors

At the highest level, the SKILLONOMY model can be represented as a sequential and parallel composition of behaviors in the following form (this is the expression of behaviors algebra):

  • UnlockBeh — behavior of token unlocking,
  • EmissionBeh — behavior of token emissions,
  • SaleBeh — behavior of token sale,
  • ReductionBeh — behavior of recounting the number of students when they reach their limit of purchasing ability;
  • StockExchange — behavior of buying and selling tokens for the agent holders,
  • SkillMiningBeh — Skillmining behavior,
  • PriceBeh — behavior of token price change,
  • B2 — behavior for the month counter,
  • AddNewStdBeh — behavior of recounting the number of students after the arrival of new users on the platform.

 

Results

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.  The 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.

 

Simulation results

We can analyze the change in the number of available tokens for students groups depending on their academic performance. In our minds, the most interesting trajectory of the available tokens for students is the trajectory for students whose marks are equal (1), because the trajectory depends more on the bitcoins and the token prices than for other categories of students:

When the price of tokens has increased and is quite high, students in this category begin to buy the missing tokens for mining. When they buy tokens, they exceed their purchasing ability and begin to leave the system. The number of students becomes so small that they have enough of what they need for mining. Therefore, we do not see volatility on this chart.

Verification of a specific model made it possible to detect some findings, the correction of which got us an opportunity to optimize the model.

 

Findings

1. Informal profits of students, who have marks i (4 <= and <5). These students accumulate some numbers of accumulated tokens (in the period between 2 and 6 months). Since, (according to the requirement) after the 6th month these students receive a number of tokens that is equal to their basic need, then the numbers of accumulated tokens in the period from 2 to 6 months are preserved to the end of the project.

2. Mining coefficients for each group of students* are large. Using these coefficients, we get the number of tokens (on the period from 2th to 6th month) that cover the basic need until the end of the project. Thus, students haven’t a need to buy tokens.

 Mining coefficients:

  • for students whose marks that are equal 5
  • for students whose marks are equal i (4>=i>5)
  • 9 for students whose marks are equal 4
  • 8 for students whose marks are equal 3
  • 7 for students whose marks are equal 2
  • 1 for students whose marks are equal 1

3. The owner of the node receives a profit only once, because the students buy the missing tokens on the exchange (According to the request of the customer. Initially, students bought the missing tokens in the node). The owner of the node receives a profit on the 6th month after the funds sellings to managers.

4. Managers buy the available tokens by money and buy funds by tokens. Owners of the nodes sell the tokens and then take the tokens back. These actions are hidden because they are mutually opposite. Money to the node comes from the managers. How many funds is the manager buying?

5. According to the survey, we obtained the following indicators (weren't defined previously):

  • the minimum purchased ability of students;
  • the frequency of cashing profits by students;
  • number of tokens that students want to cash out monthly;
  • the profit that the students would like to receive from the platform for continuing to use this platform.

 

References

  1. O Letychevskyi, V Peschanenko, M Poltoratskyi, Y Tarasich Our Approach to Formal Verification of Token Economy Models //International Conference on Information and Communication Technologies in Education, Research, and Industrial Applications. – Springer, Cham, 2019. – С. 348-363.
  2. O Letychevsky, V Peschanenko, V Radchenko, M Poltoratzkyi, P Kovalenko, S Mogylko "Formal Verification of Token Economy Models." 2019 IEEE International Conference on Blockchain and Cryptocurrency (ICBC). IEEE, 2019.
  3.  Y Letychevskyi, O., Peschanenko, V., Radchenko, V., Poltoratskyi, M., Tarasich. Formalization and Algebraic Modeling of Tokenomics Projects.//CEUR Workshop Proceedings
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