Stigmergic Intelligence
Research Series

This paper presents a novel theoretical framework for achieving artificial superintelligence (ASI) through emergent collective behavior rather than engineered individual capability. Drawing on three decades of myrmecological research by Deborah Gordon on harvester ant colonies, we demonstrate that complex, adaptive, intelligent behavior can emerge from systems where no individual agent possesses global knowledge, planning capability, or coordination authority. We introduce the Stigmergic Intelligence Hypothesis (SIH): that superintelligence is not a property of individual agents but an emergent phenomenon arising from the interaction between simple agents and an informationally-rich environment that serves as external memory, communication substrate, and cognitive scaffold.

This paper extends the Stigmergic Intelligence Hypothesis into the physical domain, proposing that superintelligent behavior can emerge from chemical systems operating on molecular pheromone networks. We argue that digital substrates (TypeDB, silicon) represent a simulation of stigmergic intelligence, while chemical substrates represent its native medium. We present Chemical Stigmergy Theory (CST): the framework for designing self-organizing molecular systems that exhibit emergent intelligence through reaction-diffusion dynamics, autocatalytic feedback loops, and molecular memory encoded in persistent chemical gradients.

STAN (Stigmergic A* Navigation) combines traditional A* pathfinding with pheromone-based trail following to create an adaptive navigation system. The algorithm reduces effective cost on proven paths through pheromone accumulation while maintaining exploration capability through decay dynamics. This enables collective optimization without central coordination.

The ONE Ontology (Organisms, Networks, Emergence) provides a six-dimensional framework for modeling emergent intelligence systems: Groups (organizational containers), Actors (entities that can act), Things (passive entities), Connections (relationships), Events (state changes), and Knowledge (crystallized patterns). This ontology enables consistent modeling across digital, biological, and hybrid systems.