What is it that makes something alive?

The term animate has Latin roots meaning reason, mind, soul, spirit, and breath. In a sense, to be alive means to have energy, and essence. In these terms, to be animated is to be alive and death is the loss of spirit.

How peculiar of a phenomenon life seems to be. How does it come about? What is it that makes a thing alive? Examining the separation between life and death - and beyond - is a human trait that shaping our interactions, values, and choices as we navigate through our own finite time.

Discovering life

In the modern age artificial intelligence is applied to almost every aspect of our day. Smart algorithms use vast amounts of collected data to predict our choices about what to buy, listen to, and spend our money on. When IBM’s Deep Blue computer defeated chess master Gary Kasparov in a six-game tournament a new age of artificial intelligence arrived. Modeling the decision center of the human intelligence seemed in reach.

But what about the living essence? How to create artificial life?

The answer to this question continues to evolve with technology over time. In the 1700’s Jacques de Vaucanson created a mechanical duck that ate, drank, shimmied, and pooped. By the 1940’s, in the halls of Los Alamos National Labs, Jon von Neumann had moved beyond the mechanical idea of life and into computational approach with cellular automata.

Replicating the behaviors of a living organism is a quest of incarnation and human desire to dance with the divine.

But the essence of life is not only a mechanical rote routine. Pondering the qualities of life in order to recreate it artificially provides a way to question what is it that really makes something alive anyway. Simulation can emulate lifelike properties, but true life exists only within the vitality of an organism.

Living organizations

I believe there are three states of being: dead, alive, and inanimate. The scale of liveliness is a measure of how vast the possibilities of evolution and the complexity of information processing system made up of the individual nodes. The mammal species has a wide variety of evolutionary outcomes, and a brain is highly complex.

Scaling beyond the individual to consider groups, these properties of a living system should hold. How do we know whether an organization is dead or alive?

When people gather and work together to solve problems there is certainly a feeling of vitality when things are going well and demise when they are not. Demise gone too far, and the organization ceases to exist; people each move on to find life somewhere else. It’s simple to say that a company is alive when profits are growing, and dead when it stops making money.

However, a profit maximizing company is only one form of organization. Vitality goes much deeper than a balance sheet.

Algorithms of community

In my work at Diamond DAO earlier this year we sought to understand community health across decentralized autonomous organizations in Web3 by building a dashboard to track health metrics over time. What we learned instead is that there is no singular definition of health to apply across different communities. Vitality is defined by the individuals participating in the community. Because of this we shifted our focus from a top down toolkit of community metrics to a bottom up, emergent mindset.

What gives essence to one organization is meaningless to another. Ways that different organizations might measure health, depending on their purpose and values:

• Adoption and adaptation of an open source protocol developed by the community

• Frequency of real-life events and number of people attending them

• Conversations and sharing of ideas within a social network

• Impact made toward a cause by community members

These examples are just a sample of ways that life of an organization can be measured. The more dynamic the community, the more likely it evolves, adapts, and creates new opportunities for members to join, contribute, and communicate their why.

The algorithm of life to create a dynamic organization capable of adaptation and perpetual evolution I break down into five core rules:

• Focus on the individuals and their interactions

• Provide a purpose for people to strive toward

• Choose intentional constraints and enforce them

• Be adaptable and flexible as the community grows

• Communicate clearly and provide efficient information systems

With these principles in mind I urge you to go forth and give life to the communities in which you participate. Proceed with curiosity, wonder, and appreciation for the sacred vitality that gives you life and the power to bring your own community alive.

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Five links

• Complexity Explorer - Courses offered by the Santa Fe Institute on all aspects of complexity science. Dive deeper into automata and genetic algorithms with Intro to Complexity

• Life Inspired - a blog about natural computing and simulated life by Dr. Luis Rocha, who I’m currently taking the course Evolutionary Systems and Biologically Inspired Computing from for my PhD coursework

• Artificial Life - a book by Chris Langton that inspired much of my thinking for this post

• The Adjacent Possible - a transcribed talk by Stuart Kauffman about the possibilities within the biosphere

• Dynamic Mathematics - very cool sketches of mathematical functions using p5.js

Footnotes