No Selves, No Consciousness

Generalisation-optimal learning favours weak rules that keep many futures open. Adaptation under this heuristic yields a hierarchy of selves. Previous Stack Theory results show this hierarchy is necessary for human-like consciousness. A first-order self tags intervention versus observation, enabling causal learning and underpinning subjective experience. A second-order self models the listener, letting self-report survive decoder mismatch. It is necessary for access consciousness, self-awareness and Gricean meaning. A third-order self binds the future self, making long-horizon trust rational and enabling narrative planning. Without this hierarchy, a system lacks ingredients of human-like conscious experience. Here I do not restate these arguments, but supply formal necessity proofs for these first three orders of self grounded in observable behaviour. I also validate the predicted capability gaps in three randomised Monte Carlo experiments. Hence when I say no selves, no consciousness, I mean no consciousness like we humans have. I then position Stack Theory relative to Embedded Universal Predictive Intelligence (EUPI), which recreates many earlier Stack Theory results but inherits from AIXI a reliance on description-length priors whose optimality depends on the choice of reference machine. Stack Theory's weakness principle is representation-invariant. It maximises generalisation probability without requiring a privileged encoding. I discuss relative strengths, proposing that bridging the two frameworks could combine Stack Theory's firmer theoretical foundation with EUPI's ready integration into reinforcement learning.