So you can see the honey bees use a dance which acts as the “attractor patterns”. That dance pattern signals the “best site”. Swarm intelligence is what we are seeing with bees but we also see it with humans.
If you observe how humans determine which stock to buy or which DApp give their attention to it’s the same sort of process. It’s swarm intelligence. In our case we agree upon a fitness function such as “profitability” or P/E etc and then the swarm moves in response to that.
So the fitness criteria is extremely important and the price feeds / index is the data feeds which direct our swarms. We respond to price signals.
That’s a really interesting video about how bees choose the best location for their nest “democratically”. It really shows how swarm intelligence can work at its best.
What I find interesting is that the bees actually rate the nest sites and they represent that rating by the duration of their waggle dance. Ratings seem to be a crucial component of good decision making systems. I’ve presented such a system in a previous post of mine called Median Rating voting system. What’s really fascinating is that the scout bees don’t actually visit multiple sites, but just visit, rate, and advertise one single site. It’s also worthy to note that the scout bees are among the oldest and most experienced bees and that they are bees that seek novelty a lot. I’ve also find it perplexing that some scout bees don’t actually advertise the location they’ve been to even if it’s a rather good one. There seems to be a large variation of “confidence” within the population of scout bees.
All this seems to indicate that the quality of the rating individuals is an important factor. The scout bees are something like “experts” in their fields. And it’s actually the population of experts which makes the decision for the whole swarm! It’s not like the non-scouting bees actually voted in this process or anything.
Also, it’s interesting that there is negative feedback by “beeping” where some bees intentionally bump into the dancing bees to encourage them to stop. This seems to make the voting process more efficient. It’s easy to interpret “beeping” simply as veto against a location, but the beeping also serves as stop signal for the whole voting process once the quorum for a single location has been reached (in the latter case the beeping becomes indiscriminate).
I think it’s quite useful to incorporate these principles in human or AI swarm intelligence systems. So, please bookmark this topic!
This is correct. In human terms for example a swarm of futurist scouts might be more aware of the potential of a certain technological breakthrough than someone who has never given considerable thought to such ideas. You can also say the crypto-anarchists who stumbled upon Bitcoin were like the scouts in the human swarm who was best able to understand the potential of Bitcoin early on.
Since no human is going to be interested in everything a good idea will diffuse to the humans who are already thinking about similar ideas or maybe even that exact idea in the beginning. So that would mean the people who are thinking about what we might think about may be few right now but in several years we will be seen as the experts who were among the first humans to give serious consideration to certain topics.
I would say instead of experts it’s more a matter of experience. People who seek novelty such as hackers for example will be more likely to discover certain technologies first.