
How Blockchains mirror Nature
Certain phenomena in the Blockchain appear to resemble biological processes
How certain events unravel on the blockchain is exciting to watch as a spectator.
Certainly one can approach and explain these events from different viewpoints: game theory, economics, sociology, psychology, etc. However, have you every considered the Biology aspect?
Phenomena in the blockchain may sometimes exhibit an eerie parallelism with biological processes found in Nature.
I am not IANAB (I am not a biologist :D), just an engineer with a passion for biology. But I worked with my good friend & real biologist Natalia Domínguez Reyes, who helped with proof-reading and with parallelism #3. Thanks, Natalia!
Three biological phenomena in the blockchain
A rapid chain of events occurred in Bitcoin Cash (BCH) between August 18th and August 23rd.
Within this period: (1) the difficulty of BCH adjusted becoming lower, (2) new miners swept in to benefit from the lower difficulty, (3) more miners swept in, (4) 2016 blocks were mined again, (5) the protocol adjusted difficulty again [this time it increased], (6) miners left in response to the higher difficulty.
These events display traits of three biological phenomena: Homeostasis, Hormesis and Blooming/Migration.
#1: Difficulty reset is a homeostatic process
Homeostasis is the ability for an organism to self-regulate in order to keep a variable very nearly constant, despite changes in the environment.
In other words, it’s about finding equilibrium.
The human body self-regulates temperature through sweat and breath, it regulates blood glucose through insulin, it regulates cell pH, etc. These are homeostatic processes.

In the case of Bitcoin, difficulty adjustment is an homeostatic mechanism, where the system responds to external outputs [average time between blocks], recalibrating itself by adjusting an internal variable [difficulty/target].
In fact, it’s cyclical homeostasis
Every 2016 blocks, the miners calculate the new difficulty, such that the next 2016 blocks will have an average time of 10 minutes each.
When the average block time deviates from the required value, an internal variable (difficulty) is adjusted to bring the system back to the optimal state (10 min. block time) <== Boom, that’s homeostasis right there!
#2: Hormesis in the hashpower of a network
Hormesis is a term by toxicologists and biologists.
It describes the phenomenon whereby a low dose of a compound or stressor may be beneficial, whereas increasing the dose will bear a toxic or detrimental effect.
Reference: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2248601/
In other words, too much or too little of something is not good, but there is a hormetic zone where the effect is optimal.

Hormesis with hashpower
One could argue that the amount of hashpower in a network responds to a hormetic action, within the context of a difficulty period (important).
Too little hashpower within that period (insufficient to meet the set difficulty target efficiently) is bad, because it makes the network slow.
However, too much hashpower is also bad for the system as a whole, because the blocks will be mined too quickly, quicker than the stipulated 10 minutes, without hardly any transactions on them.
In other words, miners will leech the system reaping the block rewards without providing much nominal transaction value, i.e. almost empty blocks. One could say they are parasites ;-)

The above chart illustrates exactly that phenomenon. It shows the number of transactions per block.
The difficulty on BCH decreased around August 20th 6am UTC, and it increased again around August 22nd at 11.30pm UTC.
You see that blue valley there? That’s it. Followed by a large spike, probably miners cashing out on their BCH earnings as the difficulty shot right up and they left the chain.
Exactly this situation leads me to the next and final phenomenon in this article…
#3: Algal blooming / opportunistic migration
This point has been hard to describe. So I have attached two concepts to it, which are both applicable depending on how you look at the issue: algal blooming and (opportunistic) migration.
As a side note: I think both concepts belong more to ecology rather than microbiology. Why? Because the behaviour I’m describing is one pertaining to colonies rather than individual cells or organisms.
Algal blooming
The definition according to Wikipedia:
An algal bloom is a rapid increase or accumulation in the population of algae in freshwater or marine water systems. […] Freshwater algal blooms are the result of an excess of nutrients, particularly some phosphates.

How does this relate to BCH? Well, BCH miners appeared solely because there was an appropriate environment (low difficulty) and excess of nutrients (block rewards).
So basically the miners would be the algae in this situation 😃
Opportunistic migration
If, on the other hand, you take into account that BCH miners temporarily migrated from one environment to another (from the BTC chain to the BCH chain) to feed on a limited resource while it was available, then you might be looking at a migratory phenomenon, of opportunistic character.
You can see this clearly in the hashpower time series:

About Biology-inspired computing
It isn’t a secret that computer scientists sometimes inspire themselves in biology. Nature is clever, and looking at how Nature solves some of its most complex problems can be enriching for computer scientists.
In fact, there is a whole field of study called Evolutionary Computation — tightly related to the Universal Darwinism — that studies the principles and mechanisms of biological evolution to computing. Algorithms developed within this scheme are called genetic algorithms.
Indeed, lots of technical and computational problems can be addressed with solutions inspired in Nature’s execution, i.e. viral spreading of messages and information, eventual consistency, consensus, anomaly detection, network formation, etc.
The site Algorithms in Nature collates research work performed at the cross of biology and computer science. For example:
- Firefly flashing synchronization to inspire time synchronization in distributed networks.

- Slime mold foraging to plan and design efficient networks according to the required throughputs.

- How E.coli transcriptional regulatory networks form a hierarchical pattern with intermediate managers, much like the Linux call graphs.

All diagrams taken from Algorithms in Nature.
Well, I sure hope that was as entertaining for you as it was for me!
Let me know if you liked this article. If I receive positive feedback, I may consider turning this biology <=> blockchain comparison material into a series of posts.
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