In April 2018 the largest blockchain hackathon in the world will be organized in the Netherlands by Blockchaingers. On 8 February a Deep Dive was held in Amsterdam for the track Machine to Machine economy (M2M) to prepare and excite the teams and other interests parties. I was asked to inspire the participants with a few examples from nature.
Machine2Machine Deep Dive
All speakers at the Deep Dive can be seen in this video. Below you can view from the moment my part starts, or read the story in text below (slightly adjusted for readability).
I will be showing three examples from nature to inspire the teams and help you strengthen your story. The teams are asked to create ecosystems where machines work together to benefit society. Looking at nature as inspiration makes sense because it has had billions of years to develop smart ecosystems.
The path of least resistance – smart swarming
Bacteria gather information collectively to find the optimal path to growth. When an individual bacteria finds a successful path, it pays less attention to the signals from others. But when encountering resitance, the individual will increase its interaction with the group and learn from its peers. Based on confidence in their own information and decisions, bacteria can adjust their interactions with the group. This way, the group will optimize its pathfinding, following a very simple algorithm really.
Smart swarming in machine ecosystems
This is relevant for a Machine to Machine economy because why would you use a resource intensive complex prediction model when a simple low memory, low computing power algorithm that reacts to supply and demand in real time could do the trick too. An example could be how self-driving cars find their passengers or delivery routes. Or a car deciding to switch between transportation and being a battery because that is more useful at that moment – which leads me to my next point: adaptability.
Adaptability – Complex Adaptive Systems
It is known that some species – like some frogs – can switch gender when there are too many males (or too many females). Nature responds and adapts to the current situation. Some think that nature is perfectly balanced, but actually a healthy ecosystem is always on the verge of chaos. What saves it is adaptability, often through extreme diversity. In science such systems are called Complex Adaptive Systems, and you’ll find them everywhere and on every scale.
If you are designing a Machine to Machine ecosystem, will your solution be able to evolve with its surrounding? I promise you, you will not be able to foresee any and all possible scenarios. Can you devise a machine-learning algorithm that is able to adapt to changing circumstances and perhaps even change its core defining characteristic? It might be a challenging proposition, but it’s buildable.
Reciprocity on nature’s own DLT
Did you know that trees communicate with each other? Via mycelium. A vast underground fungal network. They can share information such as defense signals and allele chemicals and hormones, but also send nutrients. Turns out the old, big trees send carbon to smaller trees that for example don’t get as much sun, because the big neighbour blocks it. They do have a preference for their own offspring, but overall help all trees: even other species. Why? If they’re not getting paid for it, what do they get back?
The answer is a healthier neighbourhood; a functioning ecosystem that benefits them too. A bigger chance for their genes to live on. Or help when they need it themselves. Even the biggest trees can’t cut it themselves all the time: researcher Suzanne Simard has shown that paper birch, a leafy tree, communicates and cooperates with Douglas fir, an evergreen. They send nutrients back and forth. In the summer more carbon goes from the birch to the fir. But when the leaves fall in the autumn, carbon is sent back during the coming months to help it through the winter. Long term reciprocity.
Inspiration for DLT from natural ecosystems
In a Machine to Machine economy how will the machines be designed? Will they for example need energy 24/7, 365? Can they cooperate for the good of the ecosystem? Will the machines and the ecosystem be guided by current economic principles of short term profit or can it be a system based on paying forward? Can it be a system where resources are not wasted on useless competition? An example of energy intense competition is Bitcoin mining compared to the energy efficient IOTA confirmation process.
There is much more to say about mycelium – nature’s own DLT. We’ve expanded on this in the longread Talking forests: inspiration for resilient machine ecosystems.
