**PCHAIN PANDA ROI**

Dear community,

We have heard there has been some confusion around the PANDA program and the ROI returns, and we’d like to take the time to explain to everyone in a little more detail how the program works and how calculations are made. We do apologize for any misconceptions that may have occurred. in the mean time however we feel it’s prudent to make sure everyone understands as soon as possible.

In short, 60% returns is a probability estimation instead of a fixed ratio. And our original estimation is based 100 million PAI in PANDA program. While we got 230 million PAI by now. But given this new number, our algorithm still can achieve around 60% returns by probability. It shows the robust characteristic of our algorithm.

As the motivation of PANDA is to reduce the Matthew phenomenon in POS mining. We designed a smoothing curve on ROI and introduced the dynamic machenism to allow opportunity for the candidate with lower PAI. I will explain as best as I can in this article. Section 1–2 is the basic token background. Section 3–4 explained the incentive plan. Section 5 used our current PANDA real data as an example to explain why 60% returns can be achieved with probability. Section 6 is a short conclusion.

### 1. PAI Distribution

PCHAIN will issue a total of 2,100,000,000 (2.10 billion) PCHAIN Token (PAI). Among all PAI, 35% is used for pre-sale(private sale crowd sale), 25% is reserved for the core team and early contributors, 25% is used for community development. The remaining 15%(315 million) is used for POS mining.

### 2. POS Incentive Plan

A total of 315 million PAI is used for POS incentive mechanism，and 5 million PAI is used for reward PCHAIN executive super node. The amount of PAI issued will decrease year by year, and is expected to be issued in 24years. The first year’s reward is 38,750,000, and halved every four years. It will be issued until the 24th year. The specific amount of each year is as follows.

Some one may get the concern on inflation. However, given the POS mining, we estimate over 100 million PAI will be locked. So the total number of PAI in the market will be reduced instead of increasing, when main net is online.

### 3. Epoch and Block reward

There are 12 Epochs per year, every Epoch is about one month(30 days). If the speed of generating one block is 1 second, the number of blocks of the 0th Epoch is 2592000, the reward for each block is about 1.246 PAI (= 38,750,000 / 12 / 2592000). We may adjust the speed of block generation when main net is online. The number of blocks per Epoch and the reward for each block need to calculate. Dividing the remaining time of the year by the remaining Epoch to calculate the number of blocks for the next Epoch, and then calculate the reward for each block.

### 4. Validator Incentive Mechanism

The incentive for the main chain’s validator includes the block reward and the transaction fee (Gas Fee). The child chain’s validator has transaction fee.

### 4.1 Incentives in version 1.0

In the version 1.0 of the incentive mechanism, the reward tokens generated in each block is allocated to each validator according to the proportion of the tokens mortgaged by validator. The more tokens the validator deposited, the more reward will be allocated to him. For example, if the amount of the validator A deposit accounts for 10% of all Validator mortgage amounts, then the account A can received 10% of each block reward (block award + transaction fee).

Assuming that there are 10 validators

Assuming that there are 10 Validators and their Deposit PAI as above Chart, the incentive PAI is 10000.

e.g. We can calculate PAI Validator 1 gained by

First, calculate each Validator’s percentage based on deposit weight.,100/550*100%=18.18%.

Then, the Validator gained PAI is 18.18%* Incentive PAI per year = 18.18%*10000 = 1818

### 4.2 Incentives in version 2.0

If we allocate reward according to the proportion of deposit, it will cause the rich to get richer. Therefore, the smoothing algorithm is added in the version 2.0, which can eliminate this Matthew phenomenon to a certain extent. The specific calculation method is as follows:

**Notice that, we may further adjust the smooth curve to better balance the Matthew phenomenon in future.**

**The difference of incentives in version 1.0 and version 2.0 as below pictures shows:**

### 5. PANDA ROI calculation

As we known, current PANDA locked 230 million PAI. Top 100 account included 201 million PAI. Assume all the PANDA executive nodes are selected based on the number of PAI you deposited (to simplify the calculation, we didn’t take into the date of deposition into account). And you can find the return of each node as following 2 tables. On the one hand, because the smooth function, the higher ranked nodes, the less ROI will get. On the other hand, if you deposit less PAI, you may loss the chance to become an executive node. Given these fact, it’s somewhat fair to introduce this smooth function.

While as we introduced the dynamic mechanism in executive node election. If we replace the last node with a candidate who deposited 100K PAI. Noticed that, in real election, there can be more candidate selected with low PAI. Based on current smooth function, the distribution is as following two tables.

Noticed that the Node 101 got 53.41% as consensus rewarding, which is still over 50% and comparable with our original assumption. And it also got chance to divide the pure deposit award 5 million PAI, which equals to 5/230 * 2 = 4.3%. The total return is 57.71%. If by our original assumption 100 million PAI locked in the PANDA, the pure deposit will be 5/100 * 2 = 10%. The total return will be 63.41%. Given the new deposit number is 230million, even double our estimation, the probability ROI is still around 60%. It shows the robust characteristic of our algorithm.

### 6. Conclusion

PCHAIN is trying our best to innovate on POS incentive mechanism to reduce its traditional problem of Matthew phenomenon in POS mining. We designed a smoothing curve on ROI and introduced the dynamic machenism to allow opportunity for the candidate with lower PAI. This article includes a lot of calculation and algorithm which makes it a little hard to be understood. And we really appreciate the support of our community. Looking forward your constructive feedback. Thanks.