Show HN: I modeled personal success as a control system with Bayesian priors

Summary

Mathematica Successūs A Formal Approach on Success, Systems and Self By Mikołaj Mocek Abstract Standard literature on personal achievement often relies on semantic ambiguity, offering motivational heuristics that lack structural precision. This book proposes a syntactic alternative: modeling the "Self" not as a literary protagonist, but as a dynamic control system \( S \) operating within a state space \( X \). Drawing on Set Theory, Control Theory, and Bayesian Inference, the text formalizes the conditions required for stability and goal attainment. It treats "Success" as a constrained optimization problem where the agent must maintain a vector of Essential Variables \( E \) within a viability region \( R \), while steering the system toward high-utility states under stochastic disturbances. Key Theorem: Requisite Variety Stability is mathematically impossible unless the variety of the regulator’s response \( V_R \) matches the variety of environmental disturbances \( V_D \): $$ V_O \ge V_D - V_R $$ (From Chapter 2: Space & Possibility) Key concepts formally defined include: The Topology of Possibility: Defining reachable sets \( R(x_0) \) and the hard constraints of the environment. Bayesian Epistemology: Treating learning as the update of a belief state \( P \) to avoid incoherence (Dutch books). Optimal Control: Deriving decision policies \( \pi \) that maximize Expected Utility \( \mathbb{E}[U] \) over a finite horizon. This book does not offer inspiration; it offers a formal language for debugging the source code of one’s life. It is intended for engineers, scientists, and systems thinkers who require a rigorous framework to navigate high-complexity environments.