> For the complete documentation index, see [llms.txt](https://protocol.publishinc.io/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://protocol.publishinc.io/tokenomics/utilities/incentives.md).

# Incentives

PUBLISH 2.0 motivates publishers, reviewers, and readers to actively participate in PUBLISH ecosystem. This mechanism involves earning and distributing rewards through tokens, which are regulated by formulas to ensure fairness and sustainability.

## **Incentive Mechanism Components**

1. **Publishers**:
   * **Reward**: Publishers receive $NEWS tokens for publishing high-quality content.
   * **Evaluation**: Quality is assessed based on peer reviews and reader engagement (likes, shares, comments).
2. **Reviewers**:
   * **Reward**: Reviewers earn $NEWS tokens for providing thorough and constructive reviews.
   * **Evaluation**: Reviews are scored based on their helpfulness and accuracy, determined by both publishers and readers.
3. **Readers**:
   * **Reward**: Readers earn $NEWS tokens for engaging with content (reading, liking, commenting).
   * **Evaluation**: Engagement is quantified by the frequency and quality of interactions.

## **Formula for Reward Distribution**

### Publishers' Rewards

The reward for the publisher $$𝑅\_p$$ is calculated as:

$$
R\_p = \frac{B\_p \times (E\_p + S\_r)}{T\_p}
$$

Where:

* $$R\_p$$: Reward for the publisher.
* $$𝐵\_p$$: Base reward for publishing an article.
* $$𝐸\_p$$: Engagement score (likes, comments, shares).
* $$𝑆\_𝑟$$: Score from reviewers.
* $$𝑇\_p$$: Total number of articles published in a given period.

### Reviewers' Rewards

The reward for the reviewer $$𝑅\_𝑟$$ is calculated as:

$$
R\_r = \frac{B\_r \times Q\_r \times V\_r}{T\_r}
$$

Where:

* $$𝑅\_𝑟​$$: Reward for the reviewer.
* $$𝐵\_𝑟$$: Base reward for reviewing an article.
* $$𝑄\_𝑟$$: Quality score of the review (assessed by authors and readers).
* $$𝑉\_𝑟$$: Volume of reviews (number of reviews submitted).
* $$𝑇\_𝑟$$: Total number of reviews in a given period.

### Readers' Rewards

The reward for the reader $$𝑅\_𝑒$$ is calculated as:

$$
R\_e = \frac{B\_e \times (L\_e + C\_e + S\_e)}{T\_e}
$$

Where:

* $$𝑅𝑒$$: Reward for the reader.
* $$𝐵\_𝑒$$: Base reward for engaging with content.
* $$𝐿\_𝑒$$: Number of likes given.
* $$𝐶\_𝑒$$: Number of comments made.
* $$𝑆\_𝑒$$: Number of shares.
* $$𝑇\_𝑒$$: Total engagement actions in a given period.

### Example Scenarios

#### Publisher Reward Calculation

* **Base Reward (**$$𝐵\_p$$**)**: 100 $NEWS tokens.
* **Engagement Score (**$$𝐸\_p$$**)**: 150 (sum of likes, shares, comments).
* **Reviewer Score (**$$𝑆\_𝑟$$**)**: 80.
* **Total Articles (**$$𝑇\_p$$**)**: 50.

The reward for the publisher is:

$$
R\_a = \frac{100 \times (150 + 80)}{50} = \frac{100 \times 230}{50}= 460
$$

#### Reviewer Reward Calculation

* **Base Reward (**$$𝐵\_𝑟$$**)**: 50 $NEWS tokens.
* **Quality Score (**$$𝑄\_𝑟$$**)**: 90.
* **Volume of Reviews (**$$𝑉\_𝑟$$**)**: 10.
* **Total Reviews (**$$𝑇\_𝑟$$**)**: 100.

The reward for the reviewer is:

$$
R\_r = \frac{50 \times 90 \times 10}{100} = 450
$$

#### Reader Reward Calculation

* **Base Reward (**$$𝐵\_𝑒​$$**)**: 30 $NEWS tokens.
* **Likes (**$$𝐿\_𝑒$$**)**: 30.
* **Comments (**$$𝐶\_𝑒$$**)**: 20.
* **Shares (**$$𝑆\_𝑒$$**)**: 10.
* **Total Engagements (**$$𝑇\_𝑒$$**)**: 200.

The reward for the reader is:

$$
R\_e = \frac{30 \times (30 + 20 + 10)}{200} = \frac{30 \times 60}{200} = 9.0
$$

## Conclusion

This incentive mechanism for PUBLISH 2.0, aims to create a fair and motivating environment for all participants. The formulas ensure that rewards are distributed based on meaningful contributions, encouraging continuous engagement and high-quality content production. By implementing this model, PUBLISH 2.0 can achieve a dynamic and sustainable ecosystem.


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