Epistemic Limits of Management Instruments in Modelling Non-Ergodic Environments

The contemporary organizational management is based on a mechanistic paradigm that understands the world as linear and supports deterministic forecasting and efficiency maximization. However, advancements in fields like the science of complexity, risk engineering, and probability suggest that these practices are invalid in understanding and modelling the real world, which is fundamentally non-linear and non-ergodic. The purpose of this paper is to critique the validity of standard management instrumentation, such as strategic budgets, balanced scorecards or probability matrices, by evaluating the effects of uncertainty and meta-uncertainty in estimating these types of indicators. The existing management practices often overlook the fact that most parameters through which an organisation attempts to understand the world are not fixed points but stochastic variables subject to uncertainty and recursive variability, whose collective behavior structurally thickens the tails of future distributions of probable events. We hypothesize that organizations that are optimized for predictive accuracy and operational efficiency in thin-tailed models paradoxically maximize their fragility in real-world environments. To test this hypothesis, the research employs an initial version of an agent-based simulation, which contrasts optimizer agents against epistemic hedger agents within a stochastic volatility environment. The results indicate that while efficiency-maximizing agents can outperform in the short term, they face asymptotic ruin with a probability approaching certainty as time progresses. Conversely, agents utilizing heuristic instrumentation, such as strategic buffers and redundancy, capture a long-term survival premium. This paper aims to present a new theoretical framework for real-world management, concluding that valid strategy in complex systems requires a fundamental shift away from predictive optimization and toward the adoption of structural convexity functioning principles.