Distributed sensing, swarm sensing, adaptive social learning

October 14, 2014 — June 28, 2021

bounded compute
collective knowledge
concurrency hell
edge computing
extended self
game theory
incentive mechanisms
machine learning

Is this is a real field separate from all the things that looks similar to it? e.g.probability collectives (are they a thing?) and the nature-inspired algorithms people get disturbingly enthusiastic about (ant colonies, particle swarms, that one based on choirs…), distributed consistency, and reliability engineering (Byzantine generals etc), …and quorum sensing? How about that?

1 Local versus global design

I’m particular interested in the constraints on what global organising can be done using local information. There are formal approaches to this, e.g. H. Wang and Rubenstein (2020) and more casual, fun ones like Mordvintsev et al. (2020).

2 Incentive design

Figure 1

If your network has autonomous agents that need to cooperate, how do you design their private utility? Although this looks a little bit like collective decisions, I am thinking of more incentive design-oriented questions. When we say “multi agent systems” there is usually a presumption that the individual agents are fairly simple, not whole human beings. Special case: flocking. The barriers betwixt these are permeable.

3 Graph topology

Figure 2

If the graph topology of connections is not 1:N but something more complicated, what happens to learnability? What if you need to discover something controlling for graph topology? I have arbitrarily filed that under inference on social graphs.

4 As metaphor for human systems

See wisdom of crowds versus groupthink, or possible weaponized social media.

5 Incoming

6 References

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