[Macroagents] 1. The Macroagent Ontology

I think an important concept for sensibly modeling a part of the world is what I call a "macroagent", which abstracts over the similarity cluster of teams, companies, AI scaffolds, countries, the market, and the scientific community. In this post, I will lay out the basic ontology for modeling macroagents. In the next post, I will lay out an ontology of design lenses through which macroagents can be optimized. (In a sense this can be seen as mostly rearranging ideas from economics into a good ontology.) I might write more posts in this mini-sequence at some point, though the other things I want to write here have lower priority, so not sure whether I will get around to it. I think the frames I lay out here can be useful for shaping both human systems and AI societies to improve their alignment and epistemics. 1. The Macroagent Ontology 1.1 What do I mean by "macroagent"? A macroagent is an optimization process with agentic subsystems. Examples: A team in a company: Optimizes for achieving goals the team is responsible for. Agentic subsystems are the humans in a team. AI scaffolds: Analogous to (1). A company: Usually optimizes for maximizing long-term shareholder value, although not always exclusively. Agentic subsystems are departments, teams, and humans. AI societies like in the "The Terrarium" story. The market as a whole: Optimizing for producing stuff that people will pay for. Agentic subsystems are companies and humans. By "AI macroagent" I mean a macroagent that bottoms out with AIs as fundamental agentic unit, so e.g. a company consisting only of teams of AIs would be an AI macroagent. I use "human macroagent" analogously. There are also hybrid macroagents, e.g. a team consisting of both humans and AIs, which are also of interest to me here. (Btw, on my terminology, macroagents are usually only agentic systems and not agents themselves (though it is plausible that there's no crisp boundary and that macroagents with extremely high internal coherence are agents).[1]) 1.2 The type signature of a macroagent A macroagent can be described by these 3 primitives: (memory, mechanisms, agentic subsystems) Memory: Information that is shared across at least a significant fraction of the macroagent's agentic subsystems due to a reason that is related to the macroagent's existence (rather than just coincidence). Mechanisms: The structures and rules governing subsystem interactions, and how they are enforced. Agentic subsystems: See section 1.1. The programming analogy is: Memory is the database, mechanisms are the code. Here are 4 examples to clarify what counts as memory and mechanism: MacroagentMemoryMechanismsAgentic subsystems Ideal free marketWho owns what (property, money); posted prices; contracts (including a history of transactions)Ownership allocations must be consistent with initial state + executed contracts; how correct ownership allocations and contracts are enforcedIndividuals and companies A car manufacturerInternal documents and databases (product designs, financial records, customer data)Reporting lines (who reports to whom, who decides what); hiring and firing (interview processes, performance reviews); budget allocation (which departments and projects get how much money)Departments, teams, employees The scientific communityPapers and textbooks; datasetsPeer review; authorship and citation norms; grant allocation; Implicit through journal expectations etc: The scientific methodResearchers, research groups, journals, funders A country (ignoring the market that's part of it)Information informing government decisions and records thereofThe law; police procedures; decision hierarchies in government institutions; court procedures; elections; ... (one could go into more fine-grained detail)Citizens; government institutions (agencies, courts, …) Some clarifications: Memory doesn't necessarily mean it's written down. In small teams, information that was discussed at a meeting can also count as macroagent memory. In communities, reputation of people can be part of memory. Mechanisms can be formal (like law or code) or informal (like cultural expectations). They can also leave a lot of freedom to the agentic subsystems (e.g. a hiring process determines who decides whether someone gets hired, but not how they decide). Two special kinds of mechanisms are knowledge aggregation and value aggregation mechanisms, which I'll discuss further in the next post. 1.3 Incentives From the primitives we can derive the macroagent's incentives, at two levels of granularity: Incentive profile: The value differentials of actions faced by each individual agentic subsystem. I.e. for each action available to a subsystem, how good the expected consequences are according to that subsystem's values. E.g.: "Eva is a researcher who is incentivized to try something novel rather than replicate an important result." Incentive structure: The role-level coarse-graining of the incentive profile. It generalizes over common pa