5 minute read

With DAOs, the principles of self-organization, creativity, and complexity in natural and biological systems can be used to dramatically change how we work.

I log onto my computer and open Discord to see what my DAO cohort has been working on overnight. This organization never stops; it runs 24 hours a day across the globe, building applications, protocols, and data pipelines on top of a distributed blockchain network to move tokens and crypto through a network representing an ecosystem of ideas, desires, and innovation.

I’ve never met these coworkers in real life, nor do I know their names. All I know about them is their avatar, past votes and proposals, and their immutable transaction history on the blockchain. Some of their iconic representations of self cost more in ETH than the down payment to my home. And yet I trust them to contribute to our mission, respect their knowledge, and learn from their perspectives.

How did we get here, and where are we going?

The Traditional Organization

For the past seven years of my career I’ve been writing algorithms and data feeds to automate, coordinate, and eliminate human decisions within Fortune 500 companies. My data science niche is operations research and workflow; I see the organization an information network - tasks, timestamps, and decisions being consumed, processed, and sent along into a system of feedback and throughput.

Ultimately I view Operations as an entropy reduction function within the company - by reducing variety of mundane situations a person has to respond to I can increase the throughput of high value creative thought in an organization. However, incentives, org structures, and technology often make this idealist mindset a difficult reality to achieve. For example:

  • Optimizing for the individual, or team not always best for the department
  • Matrix management requires energy into communication, navigation, and defining ownership of one’s work
  • Transaction data is often aggregated rather than tied to an individual

As the pace of markets increase, communication feedback loops form with new tools to connect us, and we work in distributed geographies, it is time to define new systems of work and coordination. For this task I tap the world of cybernetics.

The Cybernetic Organization

In the 1940’s English psychiatrist W. Ross Ashby began writing about adaptation, self-organization, variety, and control of systems for amplifying intelligence and intuition of the human mind. He brought together the fields of information theory, communications, game theory, and control systems into a formal structure for mapping operations of mind and machine in his book An Introduction to Cybernetics1.

The developments of adjacent technologies and forces of society have brought us to a dawn of using these frameworks to create governance and incentive systems in DAOs. In doing so, we must leave behind the centralized command and control systems in favor of self-organized economic organisms. Here I lay out the main cybernetic principles to guide creation of a successful decentralized autonomous organization.


To move from the handshakes, verbal agreements, and tacit understandings in the traditional organization to the cybernetic view, you must have a computational mindset and recognize your DAO as a system that:

  • Accepts inputs (proposals) from an environment
  • Responds to those inputs (votes)
  • Updates its state (completion of work)

This process is recorded through a series of smart contracts and blocks on the blockchain. Your DAO is a dynamical information system, not unlike a biological organism observing its environment, deciding next best action, and then interacting with its environment as a complex process of adaptation and evolution. Think deeply about how to assign discrete, measureable structure to the intuition, values, and outcomes we use to navigate organizations today.

@rafathebuilder had an interesting tweet about this concept:

How do you measure love, fun, excitement, fear, and cohesion when your language is data and information?


Now that you’ve done the work to map the values and range of outcomes from the decisions made by your DAO, consider this: how do you summarize the state of your DAO given the distribution over the range of values? If fear is high and cohesion is low, how do you characterize the state of your community? How about if love is high, along with cohesion, excitement, and fun?

What range of possible outcomes do you want possible for your DAO? Define specifically how you will measure the various states to describe your DAO’s operations.

Regulation and Control

The most important shift in mindset is that unlike traditional organizations optimizing for an objective like profit, engagement, clicks, these outcomes become emergent properties of the DAO’s governance, culture, and collective dynamics. With the structure you’ve defined for states and outcomes of DAO operations, now decisions about DAO governance can be explored.

For example, if both excitement and fear are running high, perhaps a governance rule to implement is to slow the pace of proposals until frenetic engagement decreases. Conversely, if love, fun, and cohesion are high, perhaps reduce the friction of passing proposals so that the team benefits from this euphoric state. Then use these controls to correlate with the outcomes you seek to optimize for.

In a decentralized organization, the way to ensure resilience is to build control systems that incentive the behaviors you want and regulate those you don’t.


By having mechanisms for shared incentives, distributed decision making, and autonomous regulation of outcomes, the DAO becomes a cybernetic organization exhibiting self-organization, emergence, and resilience over time.

Dawn is early for the decentralized organization: control systems are not yet well defined, DAO metadata is difficult to capture and correlate to governance, and many governance systems have yet to be explored. However, the thread of adjacent possibilites has woven an ecosystem of technology, interest, and energy in rethinking how we engage and define the next chapter of economic organizations.

Engage in this conversation and go deeper on this Twitter thread


[1] Many other researchers contributed to the development of intelligent control systems: Norbert Wiener, Karl Ludwig von Bertalanffy, and Jon von Neumann, to name a few.