In some of my older discussions I put forth the idea that attention is something DACs should pay participants for. The bee pollination system/algorithm rewards participation and attention.
The bee pollination system metaphor works like this:
- Users can be represented as the bees.
- DACs can be represented as the flowers.
- Pollen can be represented as the shares/tokens.
- Resources like computation, storage, electricity, bandwidth can be represented as the land.
A deeper discussion of the bee pollination system as part of an evolutionary computation algorithm
To fully analyze the bee pollination system we have to think of DACs as existing as part of a symbiotic evolutionary algorithm. The bee pollination (also called the flower pollination algorithm) is an optimization algorithm designed to produce symbiosis between human participants and the DACs. It is modeled after the flower pollination algorithm in the same way SAFE Network is modeled after the ant colony algorithm and both of these algorithms both are familiar optimization algorithms.
DACs as candidate solutions in an evolutionary algorithm
Each DAC design is a candidate solution in the evolutionary algorithm we call the altcoin ecosystem. That evolutionary algorithm can measure DAC fitness.
DAC fitness can be measured in economic efficiency/profitability, and they can also be measured in how effectively they produce the intended results inherent in their design. So if we look at Bitcoin we can see for example it’s not economically efficient because the price is trending down along with constant inflation or dilution, we can also see that it doesn’t achieve it’s long term social goal if Proof of Work ultimately centralizes to the point where we have to trust mining pools, large industrial hashing companies, etc as much as we have to trust banks today.
If viewed as artificial lifeforms DACs can reproduce in several ways.
There is cloning which is just a fork, a new brand, and a relaunch where hardly any mutation takes place. This isn’t very desirable because it can split resources up between two identical designs weakening them both. This would be like having two identical SAFE Network DACs with exactly the same features.
There is reproduction. This is when you have some new innovative feature added so that it’s not a direct clone. This would be like a SAFE Network with a transparent Proof of Work style Bitcoin blockchain for example.
Mutations are some random variable(s) which allow each DAC in the family chain to have a distinct character. This allows for feature diversity across the family chain which can allow each DAC to optimize for different purposes. Litecoin for example is Bitcoin with subtle changes so that it has a distinct character but is clearly in the same family and more like the younger brother of Bitcoin than a completely different species of DAC such as SAFE Network.
DAC Crossover allows for existing solutions to combine together. An example being the fact that you can take a snapshot of one DAC or community of token holders, split it up, and merge it with another DAC.
Evolutionary methods can be used to allow us to tame and domesticate DACs (algorithms) similar to how we domesticated cats and dogs. The evolutionary process provided by the market if we can keep it vibrant and free will let us use the survival of the fittest methods to produce DACs which evolve to be most fit (optimized) for human wants and needs.
It is not possible to know ahead of time what the perfect design will be or what human wants or needs will be. We need an ecosystem which allows many new DACs to be tested on a continuous basis. We need the testers to have the chance to be richly rewarded for participation in the process because only with human feedback can the DACs evolve to meet the needs of humans.
Individuals brave enough to participate early and frequently should receive the majority of the shares/stakes in the DAC. This would encourage the human beings to compete to be first to test new DACs allowing DACs to also compete to get attention from human beings (symbiosis). The bee pollination system/algorithm is the first step in attempting to produce a symbiotic optimization process but there has to be case studies in the form of DACs which try out that model to observe and measure the effects.