The Stimulus Problem: A Formal Theory of Goal Generation in Post-Scarcity Information Environments

This paper addresses a foundational problem emerging at the intersection of digitalization, artificial intelligence, and existential philosophy: when environmental pressures cease to generate action stimuli for cognitive agents, how should such agents construct goals and allocate finite cognitive resources? We formalize this problem within a game-theoretic framework, drawing on information theory, dynamical systems, the philosophy of action, and existentialist axiomatics. Beginning with the empirical observations documented in the digitalization-and-psyche literature — information overload, digital dependency, surveillance anxiety — we demonstrate that these phenomena are symptoms of a deeper structural condition: a growing mismatch between the bandwidth of biological cognitive channels and the density of the information environment, now compounded by the emergence of artificial agents with no inherent action stimulus. We propose three axioms governing goal generation in stimulus-absent environments: the Necessity Axiom (action must be initiated despite absence of external stimulus), the Harm Filtration Axiom (any constructed goal must not produce outcomes worse than inaction), and the Sufficiency Axiom (any goal surviving filtration is admissible). We develop a catalog of agent goals partitioned into two classes — agent-environment and agent-agent interactions — and formalize the filtration mechanism using game-theoretic tools. The framework yields a self-initiating game: a game that generates its own rules, players, and payoffs from axiomatic constraints alone, without an external game master. We distinguish carefully between the philosophical limit case (full Stimulus Void), the empirical trajectory (progressive stimulus attenuation), and the phenomenological present (experienced purposelessness in high-information environments). We argue that this formalization addresses not merely a contemporary technological challenge, but a perennial structural problem of finite information-processing agents — one that digitalization and superintelligent AI have rendered inescapable.