The mathematical structure of experience: the questions

Kolmogorov Theory (KT) takes Experience as foundational and mathematics as its structural aspect. This presentation uses the KT agent model to motivate the study of Structured Experience through algorithmic information theory and dynamical systems. Its questions concern mathematical truth, constructive views, and finite observers, computation as structured dynamics, physical realization and admissible coarse-grainings, macroscopic model discovery, simplicity and reusable regularity, algorithmic information conservation, equivalent programs, persistent patterns, regulation, multiagent systems, kindness and evil, self-knowledge and Lawvere’s limit on exhaustive valence self-scoring, and agentoptosis. Figures from the BCOM working-paper corpus connect these questions to concrete systems and current results. The presentation distinguishes qualified mathematical bounds and information balances from hypotheses about experiential structure. A focused discussion asks when a system can model itself, which blind spots follow from representational limits, and how errors in self-modeling and valence estimation might inform algorithmic neuropsychiatry. Francesca’s questions extend this discussion to the OF–PE conditions for internally available self-prediction, the distinction between exhaustive scoring and the actual OF, conflict between self-model preservation and agent persistence, and a possible feedback mechanism involving negative forecasts and hopelessness. A proposed minimal-agent experiment separates these open hypotheses from the diagonal result and distinguishes suicidal ideation from the transition to action. A linked contents section maps the questions to their slides. The closing proposal retains the comparison of program structures under declared reductions. Prepared for discussion with Gemma de les Coves and Francesca Castaldo.