What’s the type signature of an agent?

For instance, what kind-of-thing is a “goal”? What data structures can represent “goals”? Utility functions are a common choice among theorists, but they don’t seem quite right. And what are the inputs to “goals”? Even when using utility functions, different models use different inputs - Coherence Theorems imply that utilities take in predefined “bet outcomes”, whereas AI researchers often define utilities over “world states” or “world state trajectories”, and human goals seem to be over latent variables in humans’ world models.

And that’s just goals. What about “world models”? Or “agents” in general? What data structures can represent these things, how do they interface with each other and the world, and how do they embed in their low-level world? These are all questions about the type signatures of agents.

One general strategy for answering these sorts of questions is to look for what I’ll call Selection Theorems. Roughly speaking, a Selection Theorem tells us something about what agent type signatures will be selected for (by e.g. natural selection or ML training or economic profitability) in some broad class of environments. In inner/outer agency terms, it tells us what kind of inner agents will be selected by outer optimization processes.