STEEM Price Feed: STEEM Currency Price Feed for Witnesses (https://github.com/steemed/steem-price-feed/)

Introduction

STEEM Price Feed is yet another
STEEM price feed for witnesses.
It has several advantages over simpler feed scripts including:

Extensive configurability through a YAML config file.
The ability to add exchanges through the config file.
Volume-weighted prices that proportionally reduce the influence of low volume markets.
Stochastic update based on variance analysis of price history

Here I propose a method to make curation voting blind to voters, which will reduce swarm voting (voting where later voters blindly follow early voters) and may also reduce the incentive to vote with a bot.

Bot voting is a potential problem for curation

Recently, it has been observed that a number of users have been using bots to automatically up-vote Steem authors who have a good track record of contribution. Steem developer Dan Larimer (@dan, @dantheman) conducted an experiment requesting users to not up-vote his post describing the experiment. The results revealed that many users up-voted the post, most of these likely were bots that did not evaluate the post beyond determining the author. Voters have a strong incentive to use bots because the Steem reward system weights early votes much more than those that come later.Bot voting is a curation problem because it removes any incentive for curators to review submissions. Once a bot has voted on behalf of a user, the user has little reason to visit the post and override the bot's decision.

Curation is a Schelling game

Curation voting can be considered a Schelling game where voters try to identify the post's Schelling point, which is the vote most likely to be cast by future voters. Essentially, each voter must decide from available information whether later voters will vote for the post. This information has three primary sources. The first is the subjective quality of the post content. If curation were a perfect game, post content would be the only source of information. If the post content is subjectively good, then the Schelling point is the up-vote. The second source is the body of decisions revealed by curators who have previously voted on the post in question. This source of information leads to swarm voting because the obvious Schelling point is the consensus of previous voters. The third source is the author's track record, where the Schelling point is an up-vote if the track record is positive. This latter source of information leads to bot voting, which will establish a Schelling point that then leads to swarm voting.

A blind voting scheme requires limited cryptography

All sources of information not directly pertaining to the subjective quality of a post cannot be fully eliminated, although this information may be reduced. One way to do this is to use a blind voting scheme, where a voter does not know the votes of other curators. A blind voting scheme may be accomplished using a system with very little cryptographic overhead. The system I describe is inspired by Vitalik Buterin's proposed system for a price feed.The essence of this blind voting system is to have voting rounds wherein voters commit votes without initially revealing them. After each round is over, the voters reveal their commitments as a requirement to earn rewards.Commitment C would be:

C = Hash(V + R + Hash(R + W) + K)

Here, V is the vote, encoded as 1 for an up-vote and 0 as a down-vote, R is a 256 bit random number, W is the voting weight, and K is the voter's public key. The term Hash(R + W) prevents the voter from changing votes by simply changing W, as would be possible with a commitment of the form C = Hash(V + R + W + K). The large size (256 bit) of random number R prevents other voters from trivially decoding the vote by brute force.At the end of each voting round, voters must reveal V, R, and W. The reveal step requires no effort from the voter because this step can easily be automated by the voter's client software.

Reputation can attenuate voting power

If this system were coupled with a reputation score that attenuated voting power based on vesting, voters could compete for reputation. The mechanism would be one wherein curators that vote with the consensus take reputation from those who vote against consensus. Additionally, Sybil attack would be prevented in the same way it is currently, where voting power is weighted by vests. Inconsistent or incomplete (not revealed) votes would not be counted and would carry a penalty of approximately the same magnitude as minority voters.

A blind voting system can reduce bot and swarm voting

This system would reduce bot voting because it would give time for curators to review material before being pressured to vote--in contrast to the current system where milliseconds matter. Additionally, later voters would not be able to freeload one the work of early voters because the results of the work would not be visible. Each round of voting could be scored independently, without taking into account earlier rounds, reducing (but not eliminating) the influence of earlier rounds on the Schelling points of later rounds.

In this multi-part series, titled Steem Power and Governance, I will explain in detail how the distribution of Steem Power (often referred to asvesting shares, or vests) influences the Steem system of government. I will describe the ideals behind this relationship and elucidate how Steem deviates from these ideals by examining the actual distribution and application of governing power in the Steem network (which I will often refer to as the steemosphere). I will explore positive and negative consequences of the current situation of governance as I perceive them, then further suggest remedies to the problems I identify. Finally, I will give predictions for the future of Steem governance under several different models for power diffusion, relating these models to the potential for Steem's continued success.

Centralization and Decentralization

Whales are the Most Visible Steem Power Players

Nearly everyone reading this article likely stumbled on it by navigating the website at steemit.com. This website is a social media sharing portal where authors post articles such as this one. Authors also get paid for posting based on the popularity of the article where popularity is determined by user voting. Steemit.com is unique in that the rewards for a given post can reach into the thousands of dollars in value. This value is shared between the author and voters in an uneven split in favor of the author. Because of this incentive system, the quality of the most visible posts has increased quickly as new authors have joined to share in the competition and rewards.By now it is obvious to most steemit.com users, called Steemians, that some individuals have extraordinary influence on the popularity of posts, dramatically effecting their payouts. These individuals are called whales, a term that can have both negative and positive connotations depending on context. To appreciate the power of whales in the Steemosphere, consider that at the time of writing this article, a vote from a single Steem whale can bump the value of a post from nothing to several hundred USD. Some whale votes can even propel a post's reward into the thousands of dollars depending on the size of the whale and how frequently the whale votes.Upon learning about whales, most readers have a few questions. First who are these whales and how did they get so much power? Second, how is this power represented such that rewards are faithfully distributed according to the power of the votes? Third, who apportions this power and why don't they redistribute it more equitably?Before tackling these questions, I will first describe the relationships between the website steemit.com, the protocol called Steem, and the currency called steem that has the trading symbol STEEM.

The Steem Decentralized Database

The steemit.com website is powered by a database of user content. This database, or rather the computer protocol that describes the database's format and operation, is called Steem. The Steem protocol underlies a special kind of database that exists as identical copies in many computers (called nodes) at once, and for which there exists no single master copy. This type of database is known as a decentralized database because it lacks a central controlling authority. To be synchronized, all nodes must agree on the precise contents of the database at any given time. This state of agreement is called consensus.

Figure 1: Degrees of Centralization

Three different types of centralization are illustrated in Figure 1, and can be distinguished by how consensus information is propagated between nodes. In a centralized system, consensus information originates at a single node and is passed to each lower node, which has no option but to accept the consensus information without dispute. In a decentralized system, nodes exchange consensus information in an egalitarian manner. Each node has the option to accept or reject the information they receive, formulate its own consensus, and propagate its interpretation to its neighbors, or peers. In a quasi-decentralized system, consensus information originates from a small number of top-level nodes (called witnesses herein), and this information is then disseminated to lower-level nodes without an opportunity for conflict.

Functionally, these different types of centralization are distinguished primarily by their relative efficiency. A centralized system reaches consensus very quickly because it offers no opportunity for conflict, meaning that the system does not waste resources resolving disagreements. A decentralized system is much less efficient because each transfer of information provides an opportunity for conflict and the network must expend resources to resolve these conflicts. In a quasi-decentralized system, only a few highly collaborative nodes, called witnesses, participate in consensus building. This system allows for stringent rules of collaboration between witnesses, and those nodes who do not follow these rules risk permanent loss of their witness privileges, which may include generous compensation.

The biggest technical challenge of a decentralized database is to ensure that all copies stay synchronized, a term meaning that all nodes store the same content at the same time. Synchronization would be trivial with a master copy: each database could just update its contents regularly or when prompted, copying faithfully any new changes to the master copy.

Without centralization, the task of synchronization becomes more difficult because of potential disagreements. However, the principal advantage of decentralization is that power is diffused, leading to fewer and smaller opportunities for centralized authorities to corrupt the database for personal gain.

It is critical to underscore that the primary worry is manipulation and corruption of the data in the database, and not how power is distributed among or exploited by those who use the database. The former is a matter of the integrity and viability of the entire system, while the latter effects the secondary, though highly important, component of fairness.

In the next part of this series, I will discuss the importance of data integrity and describe how the network of Steem nodes reach consensus.

@tarazkp My friend is in steemit