In this multi-part series, titled Steem Power and Governance, I will explain in detail how the distribution of Steem Power (often referred to as vesting 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.