Emergence of Meaning in the Sensor Evolution of Anteorganisms

Semantic Information Theory is a recent framework that codifies meaning as the efficacy with which a living system maintain themselves far-from-equilibrium, viability being the most primitive form of meaning. It has been succesfully applied to agent-based foraging models, identifying how morphology influences the quantity of information about the environment needed to maintain viability. Anteorganisms are proposed autopoeitic systems that bridge the gap between non-life and proto-organisms, with simple dynamics resembling complex viability-based behavior requiring neither agency nor sensors. In this work we apply the framework of Semantic Information Theory to a model of an anteorganism whose behavior is chemotactic, a sensorless forager. We allow for an initial sensisitivity to environmental degrees of freedom correlated to the presence of "food," and use a competetive genetic algorithm, to drive the evolution of a refined sensor. Keeping track of the information flow from the environment to the agent using transfer entropy, we quantify the number of bits that are meaningful and how that amount changes as the morphology of the sensor evolves. Our results elucidate how evolutionary processes drive the emergence of meaning, and how the growtth of this informational architecture may be the jump in complexity between abiotic processes and proto-organisms.