How Decentralised Autonomous Organisations Leverage Nature’s Complexity Engine
On the Philosophy of Decentralised Autonomous Organisations
Decentralised autonomous organisations (DAO) are somewhat self explanatory, the name describes organisations without a point of central authority, collectively owned and managed. There is generally an incentive based governance system where organisational decisions are in some capacity autonomously managed. At their core, DAOs are a philosophical restructuring of the conventional centralised governance hierarchies seen throughout society, from corporations to government agencies, the vast majority of groups around the world are dictated via a central point of authority. Conversely, the concept of a DAO is more or less to socialise and democratise governance, to flatten the hierarchy. In this article, we will be exploring some phenomena and concepts from the natural world from which this idea may derive inspiration.
The idea of a DAO is a natural progression of the open-source framework. Intended to foster innovation and quality control, open source is a decentralised software development model which encourages open collaboration by making code freely available for modification and distribution. This approach to software development has seen great success on platforms such as Linux, as it ensures transparency in design, extensive peer review of feature implementation and enables enthusiasts to put their skills to work for the greater good. The DAO model borrows heavily from this approach while integrating incentives and formalising quality control.
As we embark into the fourth industrial revolution, or industry 4.0 as some would call it, the nature of our collective endeavours are continuing on their trend away from manual labour, and further towards pursuits of intellect. This brings us to the current state of affairs, the knowledge economy, a system in which the production of goods and services are based principally on knowledge-intensive activities, generally rapid paced technical and scientific innovation.
Many believe that the near future will bring about an end to the employment cycle, as we move further into a landscape of contract work. While not a ‘one size fits all’ solution for every project, the DAO model slots nicely into this predicted world, many of its benefits are clear, such as democratised decision making, transparency, and the enablement of global collaboration at the voluntary discretion of participants.
There are however aspects to this model which may not be as immediately apparent, yet could ultimately be their greatest strength moving forward — DAOs may enable innovation at scales previously not possible under traditional business models.
Complexity Through Emergence
Coined by G.H. Lewes in Problems of Life and Mind (1875), emergence is an etiological concept describing the process by which complex systems can possess novel properties which are not present in their constituent parts. Such properties are said to ‘emerge’ via the interaction of these parts. Put plainly, systems possessing emergent properties can be said to be ‘greater than the sum of their parts’.
There are two fundamental forces at play in all aspects of reality, entropy and complexity. Emergence is the process through which order and organisation arises from seemingly chaotic systems through interactions within those systems.
While the term differs slightly in use across science and philosophy, and ontological lines are blurred between the concepts of emergence, complexity and information theory — the core concepts may be observed everywhere. To further grasp the idea and get a sense for its universality, let’s explore a few examples.
Human Society
Perhaps the most prominent and obvious example of emergence can be found rather close to home, us.
“Human beings, viewed as behaving systems, are quite simple. The apparent complexity of our behavior over time is largely a reflection of the complexity of the environment in which we find ourselves.”
― Herbert A. Simon, The Sciences of the Artificial
While one could rather successfully argue on behalf of the complexity of an individual human, it is through our collaborative synergy that we achieve greatness. Human society has reached levels of complexity incomprehensible by any one person — while many outstanding individuals have contributed more than their share (a concept which we will come back to) no single person could have created the internet or gone to the moon without the help of others. These achievements in themselves can be considered emergent.
Many of the systems by which we abide and in which we participate daily are emergent properties of humanity, brought about purely by the immensely complex network of interaction our species collectively undergoes. Systems such as markets — resultant of many individuals presenting supply and demand or governance — many individuals expressing and acting on ideology in an effort to sculpt society in their idyllic vision. It is the interaction of individuals which brings rise to these structures, creating a system which is truly greater than the sum of its parts.
Technology and Mathematics
From the Antikythera mechanism a 2nd century BC astronomical calculator, to the Jacquard Loom, a computational loom built in 1839, to AlphaGo, a computer program which has beaten world champions at Go, a game with over 10⁷⁰⁰ outcomes (for context, the observable universe is calculated to consist of 10⁸² particles), humans have been making use of emergence for computational purposes.
Since 1936, computers have for the most part been more or less based on the principle of the Turing Machine — a simple model of computation by which any computation possible on a modern computer is possible, given enough time. It is worth noting that in reality, realized Turing Machines are technically only finite state machines, as they are bound by finite storage capacity, and that any Turing Machine based computer is subject to the halting and recursively enumerable problems.
Emergence is observable in computational systems as it is through combinations of extremely simple Boolean functions known as logic gates that complex computation is possible. It is the interaction of hundreds of millions of combinatory logic gates performing simple binary calculations in unison which bring about complexity in software, from the internet and video games to artificial intelligence.
An excellent, visually intuitive example of emergence can be found in cellular automata such as Conway’s Game of Life — which as a side note, is Turing complete.
The rules are simple.
- A live cell with fewer than two live neighbours dies — underpopulation
- A live cell with two or three live neighbours lives on to the next generation — population equilibrium
- A cell with more than three live neighbours dies — overpopulation
- A dead cell with exactly three live neighbours is born — reproduction
Yet when observed in practise, this seemingly simple system can give rise to patterns of immense complexity as it evolves. For instance, systems have been observed exhibiting self-organisation, where overall order arises from local interactions between parts of a disordered system — such as the self-reproducing universal constructors described by John von Neumann, the father of cellular automata.
Such implementations are intended to mimic the automatic growth of complexity observed in biological organisms, in this case, by self replicating. This is not unlike the process by which DNA and chromosomes are mechanically replicated in organisms.
Biology and Nature
The natural equivalents cellular automata intend to replicate are in themselves exemplary of emergence. The countless, unintelligent cells which comprise your body and that of all complex life, acting synergistically to form and sustain the whole being.
While the topic is somewhat of a hot debate in philosophical spaces, if you subscribe to the ideas of emergentism, it may even be concluded that conscious experience, arguably the most complex system observed, is an emergent property of a system of individually unintelligent cells known as neurons. While this is again, a topic of debate, we shall assume that no single neuron holds complex information such as self awareness or emotion, but acting in unison, qualia is born. Another controversial topic in this space relates back to the previously discussed concept of computational emergence, it is currently uncertain as to whether or not consciousness is a computable phenomenon.
But if the emergentists are correct and the brain can be reduced down to an information processing system, then by extension it may be possible that consciousness itself is theoretically executable on computer hardware. This theory was employed in the development of artificial neural networks, which mimic our models of the way biological neurons process signals — since their creation, artificial neural networks have strayed from biological models. Artificial neural networks are exemplary of bringing about emergent behaviour in artificially intelligent agents, such as OpenAI’s hide and seek agents learning to ‘box surf’ without being explicitly taught anything.
There is also an analogy to be drawn between the incentives of tokenomics in DAOs with the nature of reinforcement learning for artificial intelligence. In essence, the incentive portion of a tokenomics model is intended to cultivate desired behaviour within the given ecosystem, rewarding desirable behaviour and penalising undesirable behaviour — much the same as rewards are devised when training a reinforcement learning model, which in itself is loosely based on models of human and animal learning through reinforcement.
However, as an interesting aside, Sir Roger Penrose asserts that the computation of consciousness may be impossible using conventional computers.
In conjunction with Stuart Hameroff, Penrose founded the concept of orchestrated objective reduction, a quantum mind theory which postulates that consciousness originates at the quantum level inside of neurons, rather than as an emergent property brought about by the interaction of neurons. The theory posits that consciousness originates from non-computable quantum processing using qubits (similar to binary bits, 1 or 0, but also capable of existing in a superposition of both) formed in structures known as microtubules. Microtubules essentially transport substances through cells in processes such as synaptic transmission (the process by which information is carried through a neuron) of neurotransmitters along the axon of a neuron — which can be up to one meter in length. The relevance of these structures to the theory is that neuronal microtubules were observed to exhibit quantum vibrations.
One might then conclude that quantum computers may hold the answer to computing consciousness. In his book The Emperor’s New Mind, Penrose leverages logician Kurt Godel’s proofs that any effectively generated theory capable of proving basic arithmetic cannot be both consistent and complete (learn more about this here: https://youtu.be/HeQX2HjkcNo) to argue that algorithmically unprovable results are provable by humans, and that the brian must therefore be running non-computable algorithms. Classically unsolvable problems by computers, which may be solvable via quantum computing.
Depending on your interpretation of the concept, emergence may even be observed resulting from first principle interactions in physical phenomena when viewed through the lens of information theory. Consider complex physical structures, in this sense, I refer to complexity in regards to the system’s information content — which can be roughly defined as the probability of a system coming into its current state, given its initial conditions. For example, according to Ludwig Boltzman, a group of particles are much more likely to exist in a state of entropy — low complexity, low information content, thermal equilibrium — than it is to exist in the form of something complex like life. This lemma was originally presented as an argument against the chance that complex life randomly ended up the way it is — a proposition made before Darwin’s theory of evolution. We now know that life itself is likely originally resultant of a combination of emergent properties through complex particle interactions, and life as we know it is resultant of an extremely long emergent process of natural selection.
Coming back from this lengthy tangential line of thought, further examples of emergence are observable in the ecology of animals. Similarly to the rise of complexity in behaviour amongst human groups, many animal collectives are prime examples of systems which bring rise to novel emergent properties.
This brings us to our analogy.
Ant Colonies, Nature’s Decentralised Autonomous Organisation
Much of what will be discussed here may be falsified by exception-to-the-rule species of ants, but in general, the analogy holds. Contrary to popular belief, queen ants do not act as dictators guiding the decisions of a colony. Queen ants’ roles are simply to procreate and spread to new nests from time to time, acting less as a leader and more as a founder.
Although ants have passed the mirror test, suggesting that they may in some capacity be self-aware, it can be generally said that each individual ant is not particularly intelligent on its own, but when operating en masse, intelligence arises. Individual ants process partial information available to them from their environment, they use this information to decide which role they should play in the larger colony.
These colonies are essentially decentralised societies, sometimes referred to as superorganisms — many organisms operating in synergistic unison.
In harvester ants, these roles consist of:
- Foragers
- Patrollers
- Nest maintainers
- Midden workers
Each ant must individually decide on which role to play based on the local information available to them, they then must decide whether to actively or passively carry out this role. It is the culmination of these local decisions made by each ant that makes up the coordinated behaviour of the whole. This is a natural example of decentralised governance, as there is no central intelligence controlling the actions of the collective.
Analogous to both the DAO model and human society, harvester ant colonies must distribute assets amongst the community. In the case of ants, the primary asset of concern is food, which is shared in a peer-to-peer manner via a process known as a ‘communal stomach’. Individual ants consume liquid food and feed one another via trophallaxis, mouth to mouth. Solid foods are brought back to the nest for consumption by larvae.
There are also several species of ants which have exhibited agricultural-esque behaviour and mutualism, “farming” aphids and cultivating fungus in their nests.
Unlike humans under capitalism, asset distribution in ants is not incentive based, the closest semblance of an incentive model for ant colonies is found in the co-dependency of individual ants — a lone ant has little chance of survival without its colony.
Depending on the species, ants actually exhibit inclusive fitness as an emergent feature of natural selection. That is, they will distribute care and resources amongst the offspring of any member of their own species with no direct genetic relation, as it benefits the colony as a whole. Though one could argue that the communal stomach is indicative of reciprocal altruism, as feeding fellow ants may be done in anticipation of being fed in return by another ant.
Ant colonies are prone (as most systems are) to the pareto principle, also known as the 80/20 rule — approximately 80% of consequences are resultant of 20% of workers. While closer to 60/40 for ants, a minority of workers contribute to the vast majority of work in a colony. As ants lack the concept of reinforcement models, this means that hard working ants are not rewarded, and laggards are not penalised.
How DAOs Can Build Upon This Framework
While over a hundred million years of natural selection has harboured quite efficient decentralised systems, we are not ants, and thus, to adapt this framework, we must incorporate some of our own intelligent design to humanise the process.
While philosophers may debate this to no end, generally speaking, humans are not altruistic, and it can be argued that most things people do are in an effort to attain some form of incentive. These incentives take many forms of varying tangibility, from seeking validation from buying breakfast for colleagues to refraining from committing traffic offenses. Consider this, what concerns you more when contemplating jaywalking, getting hit by a car, or receiving a fine? Would you work for your employer if not for monetary incentive?
This is the primary area on which the DAO framework builds on the open source framework, while enthusiasts contributing to a project out of passion or for the greater good is highly commendable, it is not sustainable for many. DAOs offer a model which retains the benefits of open source while enabling contributors to be rewarded for their efforts (often via cryptocurrency tokens), often employing governance tokenomics models which allow for participants to vote on the project’s direction in an effort to further retain the benefits of open source.
Similarly to ants, it is the individual selection of roles and tasks, and the work of individuals culminating into a larger whole which see DAO projects to fruition.
One area in which a DAO differs to collaboration in ant colonies is the presence of information pertaining to the system, while ants can only see the small picture of their immediate surroundings, generally, a participant of a DAO can see the entire structure and its direction, enabling for more educated and specialised role and task selection.
A DAO is also generally governed via some form of democratic system, and thus, unlike ants, where the colony’s collective instrumentality is purely emergent of individual, disjointed interactions, DAO projects derive direction from votes on curated topics.
How innovation emerges through decentralised collaboration
In conclusion, to integrate the amalgamation of ideas presented above, I propose that the DAO model for scientific and engineering endeavours cultivates a superior environment for innovation by leveraging the emergent properties of an evolving global workforce.
By enabling participants from across the globe to contribute towards projects, while rewarding them for doing so, an organisation opens up a myriad of possibilities from specialists who are now able to devote more time than they would likely have to offer a standard open source project.
One of the key areas through which complexity emerges through this decentralised process of collaboration is by a process known as knowledge spillover, put simply, the exchange of ideas. The interaction of enthusiastic domain experts unlaiden by geography allows for the emergence of innovation which would likely have been beyond the reach of those individuals were they not collaborating in such a way. This creates a positive feedback loop of healthy internal competition via knowledge diversity, granting a huge boost to innovation and productivity.
“Knowledge-based economy competitive success is related to the ability of firms to create and increase their knowledge rather than pursuing static efficiency”
— Ilaria Giannocaro, Vito Albino, The influence of heterogeneity on knowledge-based agglomeration economies: Emergent patterns of geographical clusters
Decentralised autonomy is set to make waves in the tech industry, as in this ever changing knowledge economy, rapid, revolutionary ideas are king — and this framework may be the key to fast racking this process.
