Compression Efficiency and Structural Learning as a Computational Model of DLN Cognitive Stages

Abstract

We propose a computational instantiation of three cognitive stages from the Dot-Linear- Network (DLN) framework, grounded in a compression-efficiency thesis. DLN stages are characterized as graph-structured belief-dependency representations used to evaluate options: Dot as no persistent belief graph (reactive policies with negligible internal state), Linear as a null graph over option beliefs (K independent option estimates with no information sharing), and Network as shared latent structure (a bipartite factor graph in which F latent factors connect to K options), augmented by a temporal exposure state and an explicit structural learning cycle (hypothesis [->] test [->] update/expand). We distinguish two compression targets--option-factor structure (shared components in expected outcomes) and stakes-factor structure (shared drivers of consequence-bearing exposures)-- whose intersection yields jointly efficient actions that simultaneously improve expected outcomes and marginal exposure impact. In a bandit-like simulation (100 seeds, K [isin] { 20, 50, 100, 200 }, F =5), Network policies dominate Linear policies in cost-adjusted utility at large K, with the empirical crossover occurring much earlier than an analytic cost-only prediction (K* = F + cmeta/cparam), revealing that the advantage is primarily statistical (shrinkage-like estimation gains from factor pooling) rather than purely computational. Under stakes, all non-DLN agents--including Linear-Plus agents with identical factor structure and Network-standard agents with hierarchical Bayesian learning--collapse due to unmodeled cumulative exposure, while Network-DLN maintains positive utility. Within-stage consistency tests (two algorithmically distinct agents per stage) confirm that the collapse pattern is determined by representational topology, not algorithmic choice. These results evaluate internal consistency of a DLN-to-computation mapping under explicit assumptions; they do not validate a developmental theory in humans.