I’m not sure that I see a reason to expect strict scale-freedom to be an accurate assumption. Consider two different common classes of agents: 1) evolved agents (such as humans) 2) engineered/trained artificial agents (such as LLM-powered agents)
For evolved agents, the evolutionary incentives that they evolved under apply differently in the case of subsystems withing a single living individual, versus multiple living individuals within a community that shows collective agentic behavior. Individual subsystems within a single individual succeed or fail (live and pass on their genes, or die) together, so evolutionarily they have a strong incentive to cooperate to form a single effective agent. Whereas separate living individuals withing a community have (evolutionarily speaking) separate (though perhaps correlated) success/failure criteria, so have evolutionary pressures on them both to compete and also (in non-zero-sum situations) to cooperate. So I would not expect evolved agents to be scale-free — the single-individual scale at which evolution applies seems almost inevitable to be privileged.
For agents consisting of communities of cooperating humans, I would expect communities smaller than Dunbar’s Number (ones small enough to be able to operate without hierarchies or bureaucracy, because all members know each other well) to operate differently than larger communities, so again I would not expect them to be entirely scale-free.
For engineered agents, the situation is far less clear-cut, and depends upon the cognitive limits and engineering techniques of whoever is engineering the agents. For current LLM-based artificial agents, they are normally pretrained and fine-tuned alone, and (where applicable) RL-trained against a policy model. So for this current approach, the single-agent scale used during training also seems almost inevitable to be privileged.
Also, pretraining distills human intelligence into the AI via the training corpora, so if the single-human scale is privileged for evolutionary reasons, this phenomenon seems likely to be transferred to LLMs during their pretraining phase.
However, I would agree that if a community is operating as a (fairly-effective) agent, then it’s cooperating components must be mostly cooperating, more than they are competing, so I agree that there might well be some semi-scale-free aspects to its behavior. However, there might well also be some scale-dependent constraints on or inefficiencies of it’s behavior, because it’s components were prone to not always cooperating as well as subsystems of an individual would.
I’m not sure that I see a reason to expect strict scale-freedom to be an accurate assumption. Consider two different common classes of agents:
1) evolved agents (such as humans)
2) engineered/trained artificial agents (such as LLM-powered agents)
For evolved agents, the evolutionary incentives that they evolved under apply differently in the case of subsystems withing a single living individual, versus multiple living individuals within a community that shows collective agentic behavior. Individual subsystems within a single individual succeed or fail (live and pass on their genes, or die) together, so evolutionarily they have a strong incentive to cooperate to form a single effective agent. Whereas separate living individuals withing a community have (evolutionarily speaking) separate (though perhaps correlated) success/failure criteria, so have evolutionary pressures on them both to compete and also (in non-zero-sum situations) to cooperate. So I would not expect evolved agents to be scale-free — the single-individual scale at which evolution applies seems almost inevitable to be privileged.
For agents consisting of communities of cooperating humans, I would expect communities smaller than Dunbar’s Number (ones small enough to be able to operate without hierarchies or bureaucracy, because all members know each other well) to operate differently than larger communities, so again I would not expect them to be entirely scale-free.
For engineered agents, the situation is far less clear-cut, and depends upon the cognitive limits and engineering techniques of whoever is engineering the agents. For current LLM-based artificial agents, they are normally pretrained and fine-tuned alone, and (where applicable) RL-trained against a policy model. So for this current approach, the single-agent scale used during training also seems almost inevitable to be privileged.
Also, pretraining distills human intelligence into the AI via the training corpora, so if the single-human scale is privileged for evolutionary reasons, this phenomenon seems likely to be transferred to LLMs during their pretraining phase.
However, I would agree that if a community is operating as a (fairly-effective) agent, then it’s cooperating components must be mostly cooperating, more than they are competing, so I agree that there might well be some semi-scale-free aspects to its behavior. However, there might well also be some scale-dependent constraints on or inefficiencies of it’s behavior, because it’s components were prone to not always cooperating as well as subsystems of an individual would.