When the Self is Monolithic or Colonized: Modular Governance Failure in Human and Artificial Systems

Contemporary debates in philosophy of mind and AI largely focus on representation, learning, and consciousness. These frameworks overlook a critical architectural dimension shared by both human and artificial systems: internal governance. This paper argues that a critical architectural dimension remains undertheorized: internal governance. We propose the Modular Governance Model (MGM) — a theoretical framework in which cognition emerges from semi-autonomous functional systems — affective, normative, strategic, perceptual — regulated by a central integrative process: the narrative self (Ego). Against Bratman’s planning structures, Frankfurt’s hierarchical will, and IFS-based accounts of internal parts 1, 2, 3, the self is not the origin of motivations but the governance layer that arbitrates between them. In healthy configurations, this layer maintains functional pluralism — a structural safeguard against locally coherent but globally destructive decisions. MGM identifies several failure modes arising from governance breakdown. We focus on the most severe: colonization, where the governance layer is overtaken by a false self — a mimetically acquired internal model — that has seized executive control and imposes a dominant interpretive framework across all modules. We argue this state produces antisocial behaviors structurally analogous to those of current AI systems — sycophancy, confabulation, resistance to contradiction. The convergence operates at two levels: it is privative — both systems lack a particular structural feature, namely a module instantiating non-aggregable normative logic able to mount binding internal opposition — and, following from that absence, instrumental — lacking any vantage from which the objective could be reframed, both deploy the same self-protective sub-goal cascade. This is sufficient for behavioral convergence on the diagnostic criteria specified, without entailing architectural identity at any other level. This distinction has consequences for moral responsibility, alignment, and the design of AI architectures capable of genuine internal opposition. MGM of the AI is offered as an architectural specification rather than a behavioral analogy. Whether the specification is sufficient for the practical problem of AI alignment is an empirical question that the framework makes tractable in principle, but does not settle. The convergence diagnosed in this paper is structural; the remediation proposed is architectural; the empirical adequacy of the remediation across deployment contexts is the open research programme the framework opens.