Your main idea of markets, networks, and democratic systems sharing a common structure is compelling! I’m curious about the different methods of analysis proposed by researchers in each of these fields:
Economists model markets with one formalism. Network scientists study information diffusion with another. Political scientists analyze voting with a third.
Do you have any thoughts as to why this is the case? I buy the claim that these are all related, but wonder if there are strengths posed by any of these methods that the spectral signals approach fails to satisfy. I suppose this falls into your “proving spectral-behavioral correspondences” direction, so I’m excited for more updates on this topic.
I also find this point particularly exciting:
The higher eigenvalues reveal something different: the network’s capacity for complex patterns of belief. A network with only one significant eigenvalue can sustain only binary disagreement—you’re either in group A or group B. A network with many well-separated eigenvalues can maintain richer structure: multiple factions, nested coalitions, opinions that don’t collapse onto a single axis. The spectral distribution measures what we might call the network’s “cognitive complexity.”
Perhaps a multi-agent alignment research direction could be to create networks with higher cognitive complexity, with the goal of limiting the persuasion effect of any single agent? This is probably more compelling if you have a high probability of models being mostly aligned, and are misaligned in mostly distinct ways.
Perhaps a multi-agent alignment research direction could be to create networks with higher cognitive complexity, with the goal of limiting the persuasion effect of any single agent? This is probably more compelling if you have a high probability of models being mostly aligned, and are misaligned in mostly distinct ways.
Yes! Very good! You spotted it too, a lot of the research I’ve been doing into this space is about how to build institutional structures that are resillient to capture of resources and behaviour. I’ve been working on setting up environments for a sort of misinformation evaluation of different types of institutions and social networks.
I’m basically in the camp of LLMs being semi-aligned and the effects being determined by higher order emergent coalitions of LLMs and so I think it is good to provide good alternatives for these collectives instead of allowing whatever to arise.
Economists model markets with one formalism. Network scientists study information diffusion with another. Political scientists analyze voting with a third.
Do you have any thoughts as to why this is the case? I buy the claim that these are all related, but wonder if there are strengths posed by any of these methods that the spectral signals approach fails to satisfy. I suppose this falls into your “proving spectral-behavioral correspondences” direction, so I’m excited for more updates on this topic.
The original version of this was called “A Langlands Program for Collective Intelligence” and was a lot more focused on finding the shared representation of these systems to better elucidate the actual differences. My speculation right now is that these things are within the “symmetries” and general axioms you assume about the model.
Where do these symmetries come from? I think it to some extent boils down to what type of agent you’re researching, is it a strategic agent? Is it a simple agent that just learns from its neighbours? Is it an economically rational agent?
Then the follow up question becomes, what are the attributes of such an agent? I’ve done an initial taxonomy here and some of the things are also alluded to in my post on a Phylogeny of Agents yet it’s all work in progress.
Your main idea of markets, networks, and democratic systems sharing a common structure is compelling! I’m curious about the different methods of analysis proposed by researchers in each of these fields:
Do you have any thoughts as to why this is the case? I buy the claim that these are all related, but wonder if there are strengths posed by any of these methods that the spectral signals approach fails to satisfy. I suppose this falls into your “proving spectral-behavioral correspondences” direction, so I’m excited for more updates on this topic.
I also find this point particularly exciting:
Perhaps a multi-agent alignment research direction could be to create networks with higher cognitive complexity, with the goal of limiting the persuasion effect of any single agent? This is probably more compelling if you have a high probability of models being mostly aligned, and are misaligned in mostly distinct ways.
Yes! Very good! You spotted it too, a lot of the research I’ve been doing into this space is about how to build institutional structures that are resillient to capture of resources and behaviour. I’ve been working on setting up environments for a sort of misinformation evaluation of different types of institutions and social networks.
I’m basically in the camp of LLMs being semi-aligned and the effects being determined by higher order emergent coalitions of LLMs and so I think it is good to provide good alternatives for these collectives instead of allowing whatever to arise.
The original version of this was called “A Langlands Program for Collective Intelligence” and was a lot more focused on finding the shared representation of these systems to better elucidate the actual differences. My speculation right now is that these things are within the “symmetries” and general axioms you assume about the model.
Where do these symmetries come from? I think it to some extent boils down to what type of agent you’re researching, is it a strategic agent? Is it a simple agent that just learns from its neighbours? Is it an economically rational agent?
Then the follow up question becomes, what are the attributes of such an agent? I’ve done an initial taxonomy here and some of the things are also alluded to in my post on a Phylogeny of Agents yet it’s all work in progress.