this post was submitted on 29 Aug 2023
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If you've seen other posts that I have made you might've noticed that I'm interested in this exact problem and I have been super focused on research that shows having efficient information networks (i.e. centralized networks, a network where only one or a few voices matter, or fully connected networks, a network where everyone can see everyone else opinions) can lead to much lower collective intelligence for the group and having inefficient information networks (networks that have fewer connections, maybe 4-6, and everyone has an equal amount of connections) can lead to a group being able to solve more complex problems.
So in relation to what you pointed out big online communities actually might be making our collective intelligence weaker even though it makes us more connected.
Link: https://ndg.asc.upenn.edu/wp-content/uploads/2022/10/Centola_2022_TICS_Network_Science_of_Collective_Intelligence.pdf
Interesting paper. Haven't read it all yet (saving it for later), but... are lowly connected networks "less efficient" though, or do highly connected networks end up drowning in noise?
As I understand it, each person has a certain limit on input and output transmission speeds (reading is faster than writing, but speaking sits between them both), and communication quality declines with density. So the most efficient network, would be that which has as many connections as possible, up to a threshold of desired communication quality. Different people can have different speeds, and form part of several networks, each with a different threshold for quality.
That suggests an ideally efficient network structure, would be formed by a stack of overlapping networks with different topologies and unequally connected nodes depending on each one's in/out capacity and quality requirements of the networks they form part of. If we add different data processing quality and capacity at the nodes, each node would have a particular combination of networks with which it would perform ideally, for maximum total performance.
A further problem to solve, would be the evolution of parameters over time, which could require nodes switching to different combinations of networks and a different number of connections on each. Different types of periodic cycling over different configurations could be ideal for distributing information to maximize problem solving... and different types of problems could benefit from different setups of the whole system.
I wonder if instead of trying to run a simplified network version of a static problem on MTurk, it wouldn't benefit more from a series of initial simulations, and only then run a static or evolving problem with MTurk, adapting the setup based on signals from nodes, networks, and a fit function for the whole system.
Interesting.
I think that the individual's ability to process information does play a role in how many connections that individual should have but the more important role in having fewer connections is to provide protection from social influence which can hinder the creativity process and help stabilize adoption.
So for example, if I had 50 connections and 4 people adopted a new behavior / shared new information I would still be influenced to not take up the behavior because so many of my other connections aren't taking part and it could lead to negative feedback from my other connections, but if I had 6 connections instead that behavior/information would be much more appealing allowing for newer ideas/behaviors to spread in a much more stable way.
Similarly with creativity. If you have a lot of connections that are giving you answers to everything you could think of (and they are decent answers) then there is less of a need to find creative solutions to those problems meaning that new ideas are less likely to be thought of or proposed. Alternatively being surrounded by that much external information siloes you to think about finding a new solution within the things that have already worked.
This is something that they have sort of studied but not in the way you have suggested. They have allowed individuals to change their social connections over time and have noticed that the connections become more centralized and/or the connections are to people who are like them in relevant ways (This point isn't in this paper but it is in some related research). It would be interesting to see what would happen if they actually optimized the network over time to make everyone smarter.
Yes, I find it very interesting too.