Wu's Three Principles of Complexity Epistemology—— A Post-Rationalist Framework for Knowledge and Representation

This paper develops Wu’s Three Principles of Complexity Epistemology, a framework designed to explain why human knowledge, scientific theories, and artificial intelligence systems systematically fail to achieve monotonic growth, axiomatic closure, or global consistency. I begin by arguing that all epistemic activity involves mapping an open, continuous, and nonlinear domain D into a discrete representational system R. This structural mismatch forces representational compression and generates unavoidable information loss. From this, I derive three epistemic principles. The Anti-Monotonicity Principle (AMP) states that epistemic progress cannot proceed through linear accumulation: new distinctions in D often invalidate prior commitments in R, yielding intrinsically revisionary theory change. The Anti-Closure Principle (ACP) generalizes Gödelian incompleteness to all open domains, showing that no recursively enumerable system can exhaustively encode an indefinitely extensible target. The Tension Principle (TP) explains why representational systems routinely exhibit contradictions, dualities, or structural incompatibilities: compression forces distinct domain-level structures into overlapping symbolic forms whose inferential roles may conflict. I demonstrate the explanatory power of this framework through four case studies: Gödel’s incompleteness theorems, the Einsteinian restructuring of spacetime, the recurrent failure of utopian social designs, and the systematic limitations of large language models. I further address potential objections concerning self-reference, relativism, over-formalization, ontological commitment, and instrumentalism. The resulting picture is not skeptical but post-rationalist: rational inquiry remains possible and objective, yet all representational systems are structurally partial, dynamically revisable, and capable of sustaining localized tension. The paper concludes by suggesting how this framework reframes scientific methodology, AI research, and epistemic normativity under conditions of complexity.