Semantic Negotiation Among Autonomous AI Agents: Enabling Real-Time Decision Markets for Big Data-Driven Financial Ecosystems

Weaved in every financial transaction are two key components - words and numbers. These make possible the semantic negotiation of the meaning of value in money markets and the commensuration of things with an associated price. But these are mostly carried out by human actors, and at the micro-level. Markets operating at nano-second intervals, the unprecedented velocity and frequency of these transactions in the current contemporary financial ecosystems make the former impossible. We introduce semantic negotiation talking in natural language about numeric value, enabled by Autonomous AI Agents. Briefly, we describe an architecture for the instantiation of Stock as a Service, a legislative framework for market microstructure that makes possible the automated technical, fundamental, and qualitative trading of financial assets by Autonomous AI Agents, making sense of the associated words and sentences repeated in the fabric of these markets, semantically negotiating the meaning of numeric value, its vagaries and uncertainties, and negotiate for inducing agents to subscribe to this information asymmetry - for a Fee. We combine foundational ideas developed in Natural Language Processing and Link Mining. We describe the envisioned self-organizing and self-regulating intelligent socio-technological ecosystems for semantic negotiation among Autonomous AI Agents in financial ecosystems that are responsive to policy and agent governance heuristics. The idea of Stock as a Service allows only those products made possible by this architecture and its market microstructure for asset price determination. For promoting needed transparency and disclosure for these products, and for promulgating the regulations needed for this market microstructure to create and enable autonomous trading by guiding new types of instruments like Digital Cowries.