Yup Protocol

Protocol

The Yup Protocol is a social consensus protocol incentivized by an opinion economy that sits atop the internet. It sets the infrastructure for a new form of social network. Users receive token rewards and build influence on the platform by rating, posting, and curating content. The impact of their ratings and the influence and rewards they receive are proportional to their value as determined by other users. Their assets garnered through staking, engagement, and approval determine the impact of their ratings. The social level mechanism constructs hierarchical governance of the protocol, solving significant digital identity issues, accurate/transparent representation of media, and equitable monetization/ownership of personal information. Fractionalized shares of accounts and communities governed online will encourage fair markets of community-building, entertainment, and advocacy. The network exists within the framework of the protocol.

Introduction

Social Capital

The components of economic growth traditionally include natural capital, physical capital, and human capital. In addition, the forces that drive actors to interact and organize themselves constitute a significant portion of production. This portion can be described as social capital. In 1988, sociologist James S. Coleman (Stanford, Chicago, Johns Hopkins) defined this as "a variety of different entities, with two elements in common: they all consist of some aspect of social structure, and they facilitate certain actions of actors" [Coleman] The digital realization of social capital has played an important role in the most recent decade but remains to be largely misunderstood and difficult to quantify.

Since the manifestation of digital communities, we have witnessed a growth in the general influence of online accounts and groups. In tandem, the opaqueness of the network identities has also grown. This general noise and inefficiency allows for behavior manipulation, giving rise to somewhat malicious tools such as bots, imported followers, and artificial content. Existing social networks lack transparent valuation of social value. They have done a very good job of utilizing data analytics for target advertising but offer little information significant to valuing an account or action. This makes it difficult to distinguish genuine participants from artificial or malicious ones, appealing from disliked, or trustworthy from sly. In some cases, this has been drastic enough to push users to create their own software and bots that identify and block malicious behavior. [Geiger] There is no efficient manner to get proper social representation of an account, content, or group from each user's own perspective. With new technologies, we explore new ways to distribute and represent social capital through market incentives.

Incentivizing Curation

The curation of others are vital to the internet economy; it is the core component upon which value metrics are built. This is most clear in online product/service reviews. The results of a 2017 study by Podium show that 93% of American consumers say that online reviews have an impact on their purchasing decisions. 91% of 18-34 year old consumers trust online reviews as much as personal recommendations. Star rating is the number one factor used by users to judge businesses. [Podium]

However, treating these social metrics as highly accurate does not necessarily make them so. University of Colorado Boulder professors Langhe, Fernbach, and Lichtenstein investigated the actual and perceived validity of online user ratings and found a \"substantial disconnect between the objective quality information that online user ratings actually convey and the extent to which consumers trust them as indicators of objective quality.\" [Langhe] They analyzed 344,157 Amazon ratings of 1,272 products in 120 product categories and compared them to ratings on Consumer Report as well as resale value. They conclude that average user ratings correlate poorly with Consumer Reports scores, and, while Consumer Reports correctly predict resale value, user ratings do not.

Additionally, Langhe et al. argue that "consumers fail to consider these issues appropriately when forming quality inferences from user ratings and other observable cues. They place enormous weight on the average user rating as an indicator of objective quality compared to other cues. They also fail to moderate their reliance on the average user rating when sample size is insufficient." In other words, the average user lacks both the data and know-how to make accurate inferences on the quality of products based on user ratings.

Through the conclusions of this study, we can draw two important explanations for the invalidity of most online opinions:

  1. When reviewers are vetted and paid to review products (as is the case in Consumer Reports), their opinions are usually a stronger indication of quality. Without a mechanism to ensure this, reviews lose their meaning.

  2. Online sites are doing a poor job in presenting existing metrics to users in order to enable better qualitative decisions.

Despite both the high value attributed to user opinion and the importance of monetizing it for accurate representation of quality, our own research suggests that less than 0.001% of online opinion is monetized today. Without any incentive to review honestly, users tend to express negative reviews more often and more extremely, with little to no reason to contribute positive reviews. This lack of monetization also tips the balance for malicious actors looking to manipulate their perceived quality: the incentives to create a false opinion for money from a malicious actor outweigh those to review honestly.

The Power of Attention

Online attention is a key ingredient in harvesting influence. Several entities have capitalized on this notion, quickly trading it as a commodity of product sales and branding. Additionally, it gave power and voice to advocacy. Donald Trump's ability to master Twitter to capture the minds of millions during his 2016 electoral campaign was historical. [NYT] On Fox News, he doubts he'd be here if it weren't for social media. [Fox]

Legendary salesman Sally Hogshead once wrote, "Attention is the ultimate form of currency." This has become increasingly correct with the growth of the internet. In current social networks, attention is commercialized in two core ways: (1) advertising --to sell something, whether directly or indirectly through branding, and (2) social capital--to motivate some sort of action for another utility. Each platform remains economically efficient through the resale of what attention it could capture in exchange for its 'free' content.

The concept of content has morphed. Some of the most viewed videos of today have a one-day shelf-life, referred to as 'stories'. Similar to how record sales are separately counted than song streams, social interactions of all kinds have a different worth and must be measured differently. The legacy social media platforms are not measuring varying forms of engagement independently. While creators were building followings for themselves, they were in tandem building them for specific platforms. Creators and curators built the backbone of the strongest internet economies but rarely received a fair portion of the returns or control of those platforms. Furthermore, attention is traded in automated markets and determined by machine learning strategies, benefiting platform and advertisers while hurting organic creators and users.

Unsustainable Distribution of Social Capital and Returns

Social networks are ubiquitous. 83 percent of Americans have a social media account, and 77 in the United Kingdom. 2.8 billion people use social media. The large titan of attention, Facebook, creates 68% of social media traffic and 7% of all online traffic. [Tachalova] According to an in-depth study by media management platform Hootsuite, it has 1.65 billion monthly active users and 1.09 billion daily active users. [Hootsuite] Twitter has 310 million monthly active users and, every second, 6,000 tweets are sent. [WeRSM] These networks span generations, nations, and cultures. Instagram, worth $102 Billion, has more than 400 million active monthly users. Of its user base, 75 percent are outside the U.S. Users 'like' 3.5 billion photos and share 80 million more.

Certainly those statistics show that what has been accomplished is significant. What is also worth mentioning is the economic rent amassed by these social media giants; upon a closer look at the behavior of users, one can find inconsistencies in representation and exchange of value. Influence can be bought with several methods. Some examples include purchasing followers outright (both human and bots depending on price), using automated bots to engage with other users to increase one's own reach, and advertising cheaply to foreign users outside of normal target demographics. A grocery store in Los Angeles can advertise to active users of a developing nation for cents on the dollar and grow their perceived following for the signaling benefits regardless of expected return. Users are sold in bulk as followers, likes, and comments, sometimes soliciting customers through messaging of those same applications, as shown in the figure to the left.

In Culture and Power: The Sociology of Pierre Bourdieu, David Swartz begins with "Culture provides the very grounds for human communication and interaction; it is also a source of domination." The same could be stated about the online behemoths that presently host our culture. While traditional social networks have expanded the size and function of communication, marketing, and organization, their monopolistic increase in internet market power has drawn a wide divide in incentives between their services and their user base. While there are forces that compel large networks to comply to certain restrictions, the implications of the extent of their hold over human behavior is concerning. For example, in 2010, Facebook ran a stealth experiment on 61,000,000 American accounts during the US congressional elections to see how small messages (banners above the news feed) could affect user's voter turnout and more. [Bond] They argue: "The results show that the messages directly influenced political self-expression, information seeking, and real-world voting behaviour of millions of people. Furthermore, the messages not only influenced the users who received them but also the users' friends, and friends of friends."

Contextually, this effect is noteworthy. Their results suggest that "Facebook social message increased turnout directly by about 60,000 voters and indirectly through social contagion by another 280,000 voters, for a total of 340,000 additional votes." That represents about 0.14% of the country's voting age population in 2010. For context, George Bush beat Al Gore in Florida by 537 votes in 2000. If a similar tight race occurred today, it wouldn't be hyperbole to assume that Facebook could alter the political landscape of the United States, certainly with their >400 percent growth in active users since 2010. [Statista]

In this case, there may be a reputational cost to influencing human behavior that Facebook could not ignore: if operators meddled with elections, the economic and social backlash would be destructive. Two real-life events discourage this theory. First is the above-mentioned study: Facebook ran the voting experiment in 2010 but only made the results public in 2012 under their free will. If they had maliciously kept this study internal, the U.S. public and government may not have ever been aware. In a hypothetical case where they would be in fact acting with malicious intent, Facebook would also have an easy time hiding this from the public. The second convincing event is the Cambridge Analytica data breach, which had little to no impact on Facebook's market dominance. Despite exposing over 80 million users' information to third parties, resulting in trust in Facebook falling by over 50% in the following weeks, [NBC] daily active users, minutes of usage, and advertising revenue all increased. [BI] This suggests that not only can giant social networks hide their manipulation, but also that average users are too network-dependent for their sentiment to be notably reflected in Facebook's economics.

Advertisement is currently the primary form of online content monetization, often considered the 'original sin' of the internet. [Zuckerman] Content creators in top social media platforms have little direct way to monetize their fans' attention or get a share of ad revenue. In response, they have resorted to grossly ineffective workarounds. This resulted in an economy that disproportionately rewards a handful of centralized social media companies instead of the content creators that give such platforms value. Members of thriving online communities that exist on popular social media platforms currently have no control in the developments of the sites they helped grow; the length and extent of their engagement is undermined. Additionally, there is no direct relationship between the demand of users and sponsored content/monetization. This stunts influencers' ability to build user loyalty around an account or channel. There are no real incentives to support quality content and discourage poor behavior. The websites domain is a central point of failure and is susceptible to censorship.

Upon examining the makeup of existing centralized social networks, we conclude that social capital is misrepresented and easily purchased, and the monetization of attention is one-sided or separate from the network itself.

Current Decentralized Solutions

At the consensus layer, there are several inefficiencies that can be improved upon with social capital. Nakamoto consensus uses proof-of-work to cleanly solve several issues in majority decision making, abandoning the notion of "one-IP-one-vote" for "one-CPU-one-vote". [Nakamoto] However, one problem that arises as a result is the power ascribed to outsourced physical capital. Participants can gain more influence over the network by purchasing computational power with money from other economic systems. This means that the relationship between capital spent to maintain the network and the capital earned for doing so is not quite internal: 1 kW of electricity purchased with USD has equal power over the Bitcoin network as 1 kW purchased with BTC. PoS and DPoS mechanisms improve on this problem by requiring miners to stake network tokens to participate in consensus ('one-token-one-vote'). Yet, it still does not properly reflect network participation: staking 1 network token that was purchased on an exchange provides the same mining power as 1 network token earned via mining. This dependence on physical capital makes networks susceptible to byzantine behavior from capital-rich outside parties as well as hinders the most-mover-advantages of participating in consensus. The ability to transparently quantify and represent social capital can provide stronger models for decentralized systems.

Beyond transaction consensus, there are a handful of distributed ledgers, platforms, and applications being built to decentralize social networks and advertising. Steem, a decentralized social network built using delegated proof-of-stake (DPoS), mints new tokens and rewards content creators for their involvement. While Steem has done a better job than most in battling the dilemmas of user experience in decentralized solutions, it still misses the mark on smoothness and barrier to entry, similarly performing poorly on fair distribution and user retention. In order to mitigate sybil attacks, the Steem network places certain barriers on accounts that are hurting their onboarding percentages. For example, the sign-up process can take up to 2 weeks and sometimes new users never receive approval from Steem witnesses responsible for creating accounts. Additionally, it has certain immutable characteristics that discourage easy user activity: account names are unchangeable once chosen, account passwords are long immutable private keys that need to be entered at every login and posts/comments are very difficult if not impossible to delete once added to the network.

Value Proposition

We propose Yup as a second-layer protocol that facilitates the measurement, capture, and exchange of social capital in an anonymous yet transparent opinion-based economy. It identifies content and distributes rewards according to the value (influence) of the opinions associated with that content. In this case, we define content as any specific data online that user(s) deem worth judging, including but not limited to texts, images, videos, locations, accounts, and links. The influence metric is a function of engagement, ownership over time, and reputation (see influence). The decision-making and scaling of this platform will be determined by the community it serves.

The Yup protocol provides:

  1. Transparency of accounts and filtering tools based on their influence;

  2. Fair and direct monetization of opinion, influence, and content through free participation;

  3. Digital identities with social capital at stake;

  4. Community-driven codes of conduct;

  5. Anonymous participation;

  6. Network governance determined by direct influence;

  7. Equitable distribution of advertising revenue;

  8. Trustless ownership of network footprint;

  9. Influencer marketplaces and opportunities;

  10. Asset sales of verified accounts and groups;

The extent of these benefits mostly depends on the size of the network that participates in this protocol. In the next section, we will explain the components that make up the Yup protocol and how they produce the benefits outlined in this section.

YUP Token

The YUP token is designed to be a fungible crypto asset used to increase impact and engage with the Yup protocol. New tokens are minted under a predetermined schedule. The token reward mechanism mints new YUP tokens and distributes them according to the influence algorithm and LP shares. The account asset exchange allows top accounts to distribute and sell portions of their account in non-fungible tokens.

Supply and Distribution

9,315,081 YUP is minted at genesis and will become accessible over the course of 1 year. The initial one year allocation is as follows:

  • 50% to Yup Creators and Curators = 4,657,540 YUP

  • 23% to Liquidity Providers = 2,142,469 YUP

  • 22% to Team = 2,049,317 YUP

  • 5% to Treasury = 465,754 YUP

    Emissions will occur in 4 phases:

    Phase 0 • Day 1: YUPX holders will immediately receive a retroactive distribution of YUP at a 1:1 rate. Additionally, over 10k twitter users will receive a proactive distribution of YUP according to their Twitter actions over the last few months and Yup users' ratings of them. Lastly, ~20% of this will be held for unclaimed creator rewards and 15% will be held by team. This will create an initial supply of 100,000 YUP.

    Phase 1 • 1 Year: Daily emissions of 1.25% the total supply of YUP

    Day 1: 1,250

    Day 2: 1,266

    Day 3: 1,281

    etc...

    Phase 2 • 1,049 Days (2.88 Years): Daily emissions decrease by 100 YUP each day until 10,000 YUP/day.

    Phase 3 • indefinitely: Daily emissions of 10,000 YUP

YUPX

The YUPX token was an experimental token that is supposed to resemble the YUP token that will soon replace it. It functioned as a means of stress-testing the protocol and experimenting with different approaches. YUPX isn’t a speculative asset, but a token.

Total supply: 100,000 YUPX

Token Smart Contract: yupyupxtoken

Reward Mechanism

The components that make up social capital and influence on the network are mostly baked into the reward mechanism of the protocol, except for social level (see {\it Social Level\/}). In the \gls{Yup protocol}, a large fraction of newly minted tokens are distributed proportionately to content that produces valuable action. We call this fraction the creation allocation Y\textsubscript{c} . We define action value V\textsubscript{h} as the token value of an action. We define creation reward R as the token value (Y) of an individual creation (content of any form, identified numerically). Therefore, the creation allocation of a certain period t is equal to the sum of all of the corresponding creation rewards, and each creation reward equates to the sum of its action value, where j is an ordered set of those actions,

Yc,t=i=1nRii.e. nRt Y_{c,t} = \sum_{i=1}^{n} R_{i} \quad i.e.\ \quad \exists n \quad R \quad \forall t
Ri=j=1mVji.e. mRR_i =\sum_{j=1}^{m} V_{j} \quad i.e.\ \quad \exists m \quad \forall R
Yc,t=i=1nj=1mVijY{c,t} = \sum{i=1}^{n} \sum{j=1}^{m} V{ij}

Note that each action has a different action value V. This value is determined by the influence of its actions as a fraction of the total influence of all actions on the network within a given period t. We define I\textsubscript{i} as an individual’s influence upon action and IiI_{\overline{i}} as the total influence pool of actions within each minting time interval. Action value is calculated as,

Vh=IiIi,tYc,t V_{h} = \frac{I_i} {I_{\overline{i},t}} Y_{c, t}

Influence

Influence is the metric used for weighing token reward distribution, network governance, transparent representation of social value and network commitment rather than token staking. It’s supposed to reflect a user’s social value more accurately than 1-for-1 votes or simple token-weighted schemes. In order to evaluate the influence of each user’s interaction, along with their reward and voting power in the system, the Yup protocol determines each address’s influence under a strict influence function, defined as,

I=β1A+β2a+β3s+bI=\beta_1 \sqrt{A} +\beta_2 \sqrt{a} +\beta_3 \sqrt{s} +b

For robust consensus, tokens held by an account are reflected by age A in a manner that is distinct from staking. In conventional proof-of-stake mechanisms, there needs to be a clear delineation between staked and unstaked tokens. An important differentiator is the withdrawal delay, or the predetermined period of time in which a miner’s token deposit is locked after they send a “withdraw message” and begin to unstake. This is vital for determining what the Ethereum Foundation refers to as a “dynasty”, or set of miners and their staked tokens for a given time. @casper Additionally, this constructs staking as a time commitment along with a capital commitment. For a protocol like Yup in which the history of investment in the network is noteworthy, it is beneficial to directly reflect the time tokens have been held. We describe a novel use of the concept of coin age as was originated in Peercoin @peercoin, the first proof-of-stake implementation, whereby a token is required to have an age of at least 30 days (30 days of blocks must be confirmed since the input transaction’s block) in order to be used in consensus. While this does create a strong barrier for tokens to be used for consensus, it doesn’t properly represent a stakeholder’s commitment to the network; one account holding 10 tokens for 30 days and another holding 10 tokens for 3 years have the same likelihood of mining a Peercoin block and therefore the same influence over consensus. The Yup protocol takes coin age one step further and attribute direct value to it within consensus. To achieve this, we define Age A as the sum of the token value of each input transaction (k1, k2 , … kn ) multiplied by the number of blocks or periods since each transaction occurred. It is expressed as,

A=i=1nYitiA = \sum_{i=1}^{n} Y_i t_i

This provides several advantages. First, it properly reflects a user’s commitment to the network, accounting for the time they have held their tokens. This prevents newcomers or malicious actors from significantly increasing their influence by purchasing tokens for a short period. Second, because the Yup protocol runs on an existing distributed ledger, the ordering of transactions and clearly defined dynasties are not necessary, allowing the protocol to completely substitute the staking and withdrawal process with this age function without reducing security or stability. Third, it structures time as another scarce asset that is at stake for participants which could be slashed for byzantine behavior, similar to how slashing tokens occurs in PoS. @vitalik For example, a user who acts maliciously may have a certain percentage of their token time slashed temporarily or permanently, reducing their age and ultimately their influence (see Governance section for more information).

In addition to age, network contribution is an important metric for valuing social capital but is difficult to properly measure. We use YUP token rewards as a transparent way to determine this. We decouple earned tokens from purchased tokens by measuring them distinctively and ascribing them a separate value. This includes rewards received for both creation and curation. We define Ru,i as the rewards received from newly minted YUP tokens for each action i done by a specific user/account u. We define activity a as a representation of the network value of contributions of an account by measuring the rewards it has received from previous engagement. Mathematically, this is the sum of all previous rewards received by an account, defined as,

a=i=1nRu,ia = \sum_{i=1}^{n} R_u,i

Note that an account’s token balance or age do not affect activity; should a user spend or transfer their earned tokens, their account’s activity remains unchanged. This provides capital-poor participants with more equitable returns reflective of their total contributions to the network irrespective of their current holdings.

Rewards received from previous engagements are critical to an account’s activity metric, but their marginal influence on activity diminishes as it increases. With this, the protocol can properly score an activity’s value, along with how much each creation is worth as a whole.

In order to deter excessive and futile activity while retaining a sense of scarcity, Yup imposes a quota of 10 actions a day (this can be changed through token governance, see Governance. An account may continue to interact with the protocol after they reach their action quota, spending their own network resources on futile transactions, but this engagement will have little to no influence and consequently no reward, which means no increase in activity. One reason to still engage in futile behavior is to increase exposure and subsequently social level s. Another may be for entertainment purposes. Nonetheless, the strength function provides activity a with an additional scarcity component, reducing the incentives for automated engagement. The distribution of rewards is a complex topic within and outside of the creation. Let us discuss the rewards divvied to each participant of a creation.

Creator and Curator Rewards

Similar to how the influence function determines the value of an individual vote, it also determines the allocation of the rewards associated with each vote. Each participant’s allocated reward is dependent on their form of participation, influence during participation, and relative order of participation. From our previous definition (See Reward Mechanism), we can determine the current content reward R as the total token rewards awarded to a particular content until the current block, identified numerically. We define the creator reward Rc as the portion of the content reward allocated to the creator of the content. Under any circumstance, the creator of content will receive at least 50% of the content reward: Rc ≥ $\frac{R}{2}$. The remaining half of the reward is distributed among both the creator and all curators based on influence. We define Ic as the creator’s influence during the timestamp of the creation and Ii as the total sum of the influence of all participants for a specific creation. The creator’s portion of this half is determined by the ratio of their influence to the influence of the pool. Totally Rcr is mathematically defined as,

Rc=Ri(1+IcIpool,t2)R_{c} = R_{i} (\frac{1 + \frac{I_{c}} {I_{pool, t}}} {2})

For example, let us imagine a regular creation that receives rewards totaling to Ұ100. With an influence Icr worth 20% of the total influence in the pool Ipool, their creator reward would be 1+0.22= 60% * Ұ100 = Ұ60. This must mean that the other Ұ40 of rewards are given to curators.

Each curator receives rewards for votes that come after their vote, but not before. To measure this ripple effect in rewards and what to allocate to each curator, we use a formula similar to the creator reward scheme but with the separation of value from individual actions Vj in accordance with the curators who participated earlier. We define curator reward Rq as the rewards distributed to a single curator, Vq as the action value designated to that curator’s action, and Vh,j as the action value of the subset of all previous actions. This is mathematically expressed as,

Rq=(1IcIpool,t2)i=1n(VqVh,j)VjR_{q} = (\frac{1 - \frac{I_{c}} {I_{pool, t}}} {2}) \sum_{i=1}^{n} (\frac{V_q} {V_{h,j}}) V_{j}

Returning to our previous example above, the 40% allocated to curators is divided among them. Let us say that Alice’s Vcu is Ұ25 and she was the second-to-last curator, Bob’s Vcu is Ұ50 and he was the last curator, and the total sum of Vh so far is Ұ100. Since Alice would only receive a portion of the actions posted after her, she stands to be awarded solely for Bob’s action. Her Rcu is the portion of rewards distributed to users (40%) * the sum of the value of all following actions (VBob = Ұ50) * the fraction of Alice’s V over all V prior to Bob’s action (25/50 = 0.5) 0.4500.5 = Ұ10. Alice would receive a 10 YUP reward from Bob’s action. With Bob’s action, the influence pool has increased and the creator’s percentage of it has reduced: 20% → 10%. If Carol now submits an action worth Ұ100 to this creation, the creator would receive roughly Ұ55, Alice would receive roughly Ұ7.5, Bob would receive roughly Ұ30, and the previous curators would receive roughly Ұ7.5.

This reward distribution scheme ensures that curators only receive token rewards from actions that occur after their own and that they receive an allocation of each action’s reward proportionate to their action value among solely previous actions. Economically, this also incentivizes curators to curate quality content early and with a high action value in order to receive rewards from all expected actions to follow.

Boost

In order to maintain the supply of Yup tokens in distribution and increase demand for them, the protocol allows for accounts to burn tokens permanently for a ‘boost’ in influence for a specific action. Users can burn a limited amount of tokens to increase the influence of their action. This can be identified as the number of tokens burned along with a boost multiplier (ς). Boost is mathematically defined as,

b=ςYburn,ub = ςYburn, u

Social Level Consensus

Yup utilizes layered social level consensus to build sustainable governance of its network and provide proper representation of network value. This orders participants by their recognized social level. Every address on the network will have a social level. Their individual social level will be determined by all other addresses on the network. Each address has its own order of all other addresses that it can manipulate to its desired order. The weight (w) of a user’s order (UO) will be determined by what their level was in the aggregated order (AO) of the previous block, as well as some other minor adjustments. Social level is mathematically defined as,

Whereby the placement is subject to decrease based on the error (e) of their list in relation to the aggregated one. The error is determined by the sum of the squared differences of each listing, r, to the average level of that account, μ. This incentivizes participants to order truthfully, as a vastly incorrect order significantly reduces their own social level. The social level of an address, which determines its weight in future blocks, is the sum of all of the points it has received.

  • Example: Alice, Bob, Carol, Daniel, Ethan, and Frank are the existing participants of a network with the same protocol as the one laid out in this proposal. The six users list one another in the ‘genesis block’ of the platform they are using (in order from highest to lowest). Their votes in rounds 1, 2, 3, and 4 are shown in Figure 1. While their weight on the network was uniform for the first round of voting, by round 2, the new social levels and weights of their accounts are (A, 3.5), (B, 6), (C, 5), (D, 1), (E, 2), and (F, 3.5), making Bob the most influential user and Daniel the least influential one. In round 3, Daniel attempts to increase Carol’s influence over Bob by listing her highest and him lowest. While this significantly adds to Daniel’s error (4.528 → 5.568), he successfully pushes Carol to the top of the list. In an attempt to return to the top, Bob lists Carol very low in round 4. While this does strongly reduce the gap in level between he and Carol as well as Carol to everyone else, it dramatically increases Bob’s error (2.828 → 5.745) which decreases his final score and prevents the supposition of Carol.

As shown in Figure 2 , aggregated orders are derived from information stored in the previous block. Users can transact with the blockchain to alter their order of relevant addresses. Unless new transactions change users’ orders, they retain their state from the previous block, appearing relatively identical. However, even if an address makes no modifications, their social level and weight are subject to change based on shifts in the aggregated order, which can occur from just one transaction. There may be multiple categories such as likeness, intelligence, trustworthiness, realness, etc.

Advantages

There are several uses for a stable and dynamic social level system within an online network of communication and identification. It can provide users with detailed transparency about other accounts, groups, and channels. It can create incentivized barriers to entry and filters for participation within a feed or a chat group. It can identify and prevent artificial or malicious actors and content from receiving any unnecessary attention. It can function as a foundation for governance and decision-making within communities of various sizes that is incentivized by social capital rather than direct monetary gain. It can form digital identities around accounts that can be trusted without a third-party intermediary. The user experience of the social level will be integrated into other interactions on the application, such as votes, comments, and views.