As a decentralised peer-to-peer network, OriginTrail uses an ERC20 token, Trace ($TRAC), to manage relations between users and nodes that make up the network. The main aim of the network is not to impose technical limitations that would require arbitrary decisions of the technical architects, but rather to have market forces achieve equilibrium within the network and be self-adjusting, ensuring the required longevity. Recently, we published the first version of the incentive model describing how these relations are being formed, sustained and incentivised. In the paragraphs below we look to explain, in more detail, how the incentive model will impact the dynamics of the network.

The model presumes dynamics that occur due to demand created by its protocol level utility for which Trace was created (Trace is a utility token, read more about it here). Key assumptions put forward in the token demand model will be subject to rigorous testing in the period following the test net launch and prior to the mainnet deployment.

The model presented below is the first version created by the OriginTrail team which will pass several third-party reviews over the upcoming months and will be subject to improvements/amendments based on those reviews in the future.

Key Characteristics

There are two characteristics of the OriginTrail Decentralised Network (ODN) that are especially important for the token demand model:

Replication factor ( R ) — in order to ensure data immutability and to avoid the possibility of collusion, all the data on the network is replicated with a replication factor R. The theoretical minimum replication factor R that provides integrity to a particular dataset is 2n + 1, where n is the number of the actors in the observed supply chain in the context of the provided data. For more info on the concept, refer to our White Paper.

R — in order to ensure data immutability and to avoid the possibility of collusion, all the data on the network is replicated with a replication factor R. The theoretical minimum replication factor R that provides integrity to a particular dataset is 2n + 1, where n is the number of the actors in the observed supply chain in the context of the provided data. For more info on the concept, refer to our White Paper. Staking — in order to incentivise nodes in the network to be good actors and provide value, a staking mechanism has been introduced where Data Holder (DH) nodes are required to provide a stake in tokens, according to conditions agreed upon in the agreement which is the result of the bidding process. The minimum required amount of stake is something that is up to discretion of the DC node and can be adjusted depending on the use-case.

Market Forces of the ODN and Key Assumptions

There are two market forces in place within the ODN ecosystem.

Demand is represented exclusively by the total need for ODN’s functionality on the side of stakeholders (companies) who want to use the protocol. This demand is represented in the model by the amount of fiat currency pressure (F) companies create for the TRAC token that is in turn used to compensate the nodes of the system. Supply is represented by the TRAC token supply. As mentioned, TRAC represents a means of compensating the nodes in the network and, in turn, accessing the functionalities of the ODN. The model treats total available TRAC in circulation as the supply side of the graph, assuming there will be sufficient amount of nodes that are willing to offer services (storage and processing power) for TRAC compensation. The supply curve in the model is perfectly inelastic and we are assuming that the entire supply is being used for the protocol level utility.

Dynamics of the ODN

The final dynamics of the ecosystem will be determined by both the market forces and characteristics of the network. Let’s look at these dynamics step-by-step as the network experiences an increase in demand.

Step 1: Initial Equilibrium

In the initial phase, we have a given demand for the network’s functionality D0 and a total available circulating supply of S0. The equilibrium is established at the intersection of both (S0, D0) and price of P0. The available amount of tokens in this state is Q0

Step 2: A Triple Effect

For the next step, let’s analyse the movements when a demand pressure of F enters the ecosystem, creating a triple effect.

The first effect that the new demand pressure F creates is moving the demand curve upward, whereas the total available supply remain the same. The new equilibrium is established at P’.

The second effect a pressure of F creates is an increase in demand by the average factor of staking factor (Sf) used in the contracts created by the DCs, multiplied by F. On the graph, this is shown by the demand curve making another move upward from D’ to D’’, pushing the equilibrium price from P’ to P’’.

The third effect is another consequence of Sf. Once TRAC tokens are staked in the smart contracts, they have effectively been removed from the circulating supply until the agreements have concluded. This means that the total available supply falls from S0 to S’’’ showing the last effect of the demand F entering the ecosystem. The move from S0 to S’’’ changes the price from P’’ to P’’’.

Step 3: The New Equilibrium

The new equilibrium therefore gets established at P’’’ as an effect of demand pressure F, demand for the required stake depending on the S0, and the lowering of supply as the tokens get staked in the smart contract.

The move from equilibrium point of E0 in time t-1 to E’’’ in t, is described by the equations below.

D(t) = F(t)P(t-1)

P(0) = $0.1

Q(0) = 500 000 000 TRAC

Q(t) = Q(t-1) — D(t)⋅Sf

The Demand Model

In addition to the dynamics, we have produced a simplified model that can give you a rough estimation as to how much demand for the TRAC token (in USD) a certain level of adoption can bring. All figures have been factored in for minimal values (i.e. theoretical minimum of 2n+1).The model is available here and has the following variables as inputs:

S — Observed supply chains are a (relatively) constant group of partners that are involved in a supply chain of particular products that you wish to create estimations on;

M — Average number of months that data will be stored on the ODN;

D — Average size of data size per DC import;

Pw — Average price of data stored per kb per month in $;

n — Average number of stakeholders per observed supply chain (S);

Pr — Average price per data read;

V — Monthly velocity (repetitions) for the observed batch;

Sf — Average stake factor;

F — Estimated fiat demand for the token based on the assumptions;

TD — Estimated total fiat demand, including staking.

The variables have a linear correlation that approximately follow this equation:

F(t) = S x N x M x D x V x (2N x Pw + (N-3) x Pr)

TD = F + F * Sf

Let’s look at it one part at the time:

General variables — All general variables influence the model as a whole. Increasing their values means increasing the entire value by that same factor. With a number of observed chains (S) you are able to describe the group of supply chains you would like to observe within the model. It can be linked to a company, a region, an industry, a country or other estimations. The second variable is the average number of stakeholders (N) within the observed supply chain. The next is the number of months (M), representing the average time that data has to be stored based on your estimation and the use case. Data size (D) is what the estimate would be for average input per stakeholder in the observed supply chains for one batch (in kBs). Velocity (V) represents the frequency (repetitions) of a particular observed product supply chain within a month. This number greatly depends on the type of product/service.

— All general variables influence the model as a whole. Increasing their values means increasing the entire value by that same factor. With a you are able to describe the group of supply chains you would like to observe within the model. It can be linked to a company, a region, an industry, a country or other estimations. The second variable is the average within the observed supply chain. The next is the representing the average time that data has to be stored based on your estimation and the use case. is what the estimate would be for average input per stakeholder in the observed supply chains for one batch (in kBs). represents the frequency (repetitions) of a particular observed product supply chain within a month. This number greatly depends on the type of product/service. Write (storage) variables — The next group of variables is linked to writing data to the protocol. The premise of the 2N x Pw x N is to describe the number of inputs to the network based on the key characteristics described above. 2N is the number of replications every actor has to perform to reach the integrity minimum (it is 2N instead of (2N+1) because one replication is the DC node itself). Pw is the price of storing 1kB of data per month and is the established price between DCs and DHs achieved through the bidding mechanism.

The next group of variables is linked to writing data to the protocol. The premise of the 2N x Pw x N is to describe the number of inputs to the network based on the key characteristics described above. 2N is the number of replications every actor has to perform to reach the integrity minimum (it is 2N instead of (2N+1) because one replication is the DC node itself). Pw is the price of storing 1kB of data per month and is the established price between DCs and DHs achieved through the bidding mechanism. Read variables — The third group of variables are related to reading from the network. The premise is that every stakeholder will at least want to read the data from other stakeholders. Because the protocol automatically replicates data one-step-back and one-step-forward, N is lowered by 3. Pr is the price of reading 1kB of data.

The third group of variables are related to reading from the network. The premise is that every stakeholder will at least want to read the data from other stakeholders. Because the protocol automatically replicates data one-step-back and one-step-forward, N is lowered by 3. Pr is the price of reading 1kB of data. Staking — The last variable is the stake factor (Sf), which is determined within the created agreement. It increases the total demand by F x Sf.

The formula can be populated with your estimates here.

We will keep a close watch on how the dynamics will unveil once the network mainnet is released in September and adapt the model with the additional feedback we collect in the process.

Trace on!