Steemit Crypto Academy Contest / S10W3 - Reward System on Steemit Blockchain

• Quadratic Linear Reward (f(x) = x^2)

When Steemit was for the first created in 2016, the platform used a reward system called Quadratic Linear Reward. Under this system, rewards were distributed based on a quadratic equation, f(x) = x^2. Essentially, users who held more Steem Power (SP) earned more rewards. However, this approach was seen as unfair to newcomers and for users to platform with limited SP, as it heavily favored those with larger investments.

• Transition to Linear Reward (f(x) = x)

In response to these concerns, the rewards system was changed in 2017 from quadratic to a Linear Reward model, which used a simple equation, f(x) = x. This deals that the input (SP) directly correlated to the output (rewards), ensuring a more equal distribution. However, this change led to other issues, such as the misuse of rewards through self-voting & a decreased emphasis on discovering high-quality content.

• Convergent Linear Reward (f(x) = x^2 / (x+1))

To address these problems, a new reward system called Convergent Linear Reward system was introduced with the 2019 hard fork. This model uses the following equation: f(x) = x^2 / (x+1). The Convergent Linear Reward system aims to strike a balance by rewarding users based on both their stake power (SP) and their engagement with the platform. It encourages users to interact, engage, and upvote on content using the Proof of Brain (PoB) mechanism.

Generally , these changes in the rewards attributtion system on Steemit were implemented to promote fairness, encourage active participation, and improve the overall quality of content on the platform.

The changes in the reward attribution system on Steemit platform were primarily driven by the need for fairness, improved engagement, & enhanced content quality. Here are the causes behind each phase:

1. Quadratic Linear Reward (f(x) = x^2):
   The initial reward attribution system favored users with more Steem Power (SP), leading to an imbalance and inequality among content creators in platform . This prompted the need for a change to ensure a fairer distribution of rewards based on contribution rather than just power stacking .

2. Transition to Linear Reward (f(x) = x):
   The transition to a linear reward model was motivated by the desire to create a more equitable steem platform. This change aimed to provide equal earning opportunities to all platform users, regardless of their SP, thereby encouraging broader participation and reducing the dominance of high SP holders.

3. Convergent Linear Reward (f(x) = x^2 / (x+1)): The introduction of the Convergent Linear Reward methodology aimed to address issues such as self-voting and incentivize quality content creation. By merging the influence of Stem stake and engagement, this model encourages users to actively interact, vote, and engage with other platform participants, ultimately fostering a more dynamic and high-quality content ecosystem.

The Convergent Linear Reward system has played a important role in improving and developing the Steemit platform. One of its most important contributions is discouraging self-voting among users. Previously, some users with large Steem Power (SP) holdings could manipulate the system by voting for their own posts , but this system has reduced such practices in platform .

This reward system also helps reduce the dominance of whales (higher power stacker ), who have substantial SP. In the past, they had big control over the platform due to their higher rewards. However, with the Convergent Linear Rewards attribution Curve, their influence is limited as the rewards become linear after a certain point. This encourages them to use their SP to support and curate content from other users on platform .

Increased user engagement in platform is another major benefit of this system, aligning with the platform's goal. When users are more engaged, the platform becomes more active & experiences growth. The Convergent Linear Curve incentivizes users to actively participate in various activities , interact, and upvote each other's content, fostering a vibrant and thriving community.

Furthermore, the Convergent Linear Reward methodology ensures that high-quality content creators have an equal chance to earn rewards compared to users with large SP holdings. This system also encourages users to create valuable content and actively engage with the community to receive solid upvotes and support from others on the platform. Ultimately, it promotes fairness, quality, & engagement on Steemit.

When it comes to curation on the Steemit platform, there are two types: Isolated Curation and Community Curation. Let's explore the two types differences between these two approaches.

Isolated Curation is where individual curators independently select and vote on any choice posts based on their own judgment and preferences. It relies on the voting power of individual users to curate any particular content. In this type of curation, a single user can choose to curate a post they personally find it interesting or of high quality.

On the other hand, Community Curation include a collective effort from a community or a group of users to curate posts on the platform. The curation process in a community is guided by community standards & curation trails established within the community. It utilizes the combined Steem stacking of community members to support selected posts, taking into consideration various factors and certain criteria set by the community.

As for my preference both isolated curation and community curation have their own merits. Isolated curation allows for individual taste and diverse perspectives, while community curation benefits from collective wisdom and specific rules base criteria. Ultimately, the choice between the two depends on the specific context & goals of the platform and its users.

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• References :
• Steemit Corner#7 : How does convergent linear reward affect post payout in Steemit?
• Steemit Crypto Academy Contest / S10W3 - Reward System on Steemit Blockchain

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@artist1111

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