Autocatalytic Constraint Closure as an Organizational Principle for Machine Consciousness

Abstract

Current AI systems are largely organized around prediction-error minimization and reward optimization. While these frameworks have been highly successful, they do not address how AI systems can develop the kind of integrated, self-maintaining world-model widely regarded as central to consciousness. This paper proposes autocatalytic constraint closure as a necessary organizational principle for machine consciousness. Reflexively Autocatalytic Foodset-derived (RAF) networks provide a general-purpose formal framework for describing and analyzing the emergence of systems whose components catalyze the generation of new components that increase the coherence of the whole. This can result in a phase transition to a self-organizing system with history-dependent dynamics. Applied to cognition, external stimuli and internal representations ‘catalyze’ mental operations yielding new representations, spurring formation of an integrated representational network and coherent world-model. In this initial phase of research applying RAF networks to machine consciousness, we show that (i) AI systems instantiate limited, task-bound forms of autocatalytic organization but lack persistent closure across contexts, and (ii) in-context learning may be due to transient RAF formation. Next steps include fostering sensitivity to internal incoherence and question-asking in AI systems with the aim of fostering endogenously driven self-organization, a prerequisite for conscious systems, and the use of RAF algorithms to analyze candidates for machine consciousness.