We gather many perspectives on Capital and synthesize their commonalities. We provide a characterization of Capital as a historical agential system and propose a model of Capital using tools from computer science. Our model consists of propositions which, if satisfied by a specific grounding, constitute a valid model of Capital. We clarify the manners in which Capital can evolve. We claim that, when its evolution is driven by quantitative optimization processes, Capital can possess qualities of Artificial Intelligence. We find that Capital may not uniquely represent meaning, in the same way that optimization is not intentionally meaningful. We find that Artificial Intelligences like modern day Large Language Models are a part of Capital. We link our readers to a web-interface where they can interact with a part of Capital.

Sharing stories, particularly about death, is an important part of many cultures. In light of these known cases of inter-generational knowledge transmission in biological systems, we explore such learning through sharing information (“stories”) about death. A simulated environment with novelty-seeking Q-learning agents allows us to explore the effects of different types of information sharing on the lifespans of individual agents and the ability of inter-generational chains to maximize novelty via exploration. We find that sharing information about death provides a significantly better learning signal than sharing information about random states in the environment. Moreover, sharing shorter stories appears better than sharing longer ones. Sharing stories promotes survival and exploration in subsequent generations. This provides a foundation upon which further exploration of story sharing dynamics between agents can be explored.