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Last updated December 30, 2021


Decentralization Diagram

3 Axes of Decentralizatiaon

  1. Architectural Decentralization: how many physical computers is a system made of? how many computers can fail before the network fails?
  2. Political Decentralization: how many individuals/organizations ultimately control the computers that the system is made up of?
  3. Logical Decentralization: are the data structures used to represent the system more monolithic or swarm-like? if you cut the system in half (both providers and users), will both halves continue to fully operate as independent units?




Follow a predictable life cycle along the S-shaped adoption curve.

At the beginning, will do everything they can to garner usage and appear more valuable as platforms with multi-sided positive network effects. However, when they move up the S-curve, their power grows. Eventually, the relationships turn from positive sum to zero sum. Thus, to continue growing, they must extract from users (e.g. selling user data, taxing profits, etc.)

Centralized systems often start out fully baked, but only get better at the rate at which employees at the sponsoring company improve them. Decentralized systems start out half-baked but, under the right conditions, grow exponentially as they attract new contributors.

Why Decentralize?

  1. Fault tolerance, less likely to fail accidentally because they rely on many separate redundant components
  2. Attack resistance, no central point to attack
  3. Collusion resistance

Decentralization as Activism


Decentralization doesn’t work in a vacuum, mainstream decentralized systems require a degree of activism to keep the system working

“BitTorrent seems to represent the minimum viable decentralization required to stay alive as defined by the law at the time”

Decentralization is a tactic for diffusing risk for many and lowering the risk for the activists that operate the most sensitive parts of the system.

Over applying decentralization isn’t a strategy unless your goal is obscurity


Fault Tolerance

Common mode failure, all pieces can fail for the same reason. (e.g. all nodes in a blockchain run the same software but that software has a bug)

To counteract this,

Attack Resistance

From a purely mathematical and game theory perspective, decentralization may not even matter. In a finality reversion (e.g. 51% attack), a huge loss of say $50M is still $50M regardless of whether you have validators in 1 org or 10.

However, after considering coercion, decentralization becomes much more important. It’s much harder to threaten 100 people than 1.

Collusion Resistance

Collusion is “coorindation that we don’t like”

consensus model relies on uncoordinated choice model, the asusmption that the game consists of many small actors that make decisions independently

Yet, 90% of the entire Bitcion network’s mining power can show up at the same conference (as 7 men). Yet, some coordination is good (e.g. strong community spirit and banding together to implement patches and fix bugs)

Ways to counteract:

  1. Find a happy medium that allows enough coordination for a protocol to move forward but not enough to enable attacks
  2. Try to make a distinction between beneficial coordination and collusion and make the former easier and the latter harder


Imagine the surface of the internet as a representation of its potential activity. A few heavyweight players have dug into the web surface, dragging activities down their slopes, activities that could have remained independent and decentralized.

Instead of creating a new webpage, Internet professionals and private users tend to go to a Facebook Page and therefore open content hosted on the slope of a dominant curve.

Interactive Graph