Simulation as Inquiry: Reframing Methodological Knowledge Through Boundary Mapping

Abstract

Methodological statistics has developed under the pervasive belief that the value of a simulation study lies in its ability to demonstrate the superiority of a proposed method. This belief has distorted the epistemic role of simulation research by encouraging selective reporting, narrow design spaces and the suppression of results that fail to show improvement. Drawing on philosophical accounts and on meta scientific critiques by several studies, this paper argues that non superiority is not a failure but an essential source of methodological knowledge. It marks the boundaries at which a method performs adequately, where it begins to degrade and where it breaks. Using empirical patterns evident in contemporary methodological literature and drawing on the widely cited case in which machine learning methods often failed to outperform logistic regression in clinical prediction, this paper demonstrates how non-superiority clarifies expectations and guides methodological refinement. The paper proposes a structural remedy that pairs the Registered Reports model with principled simulation design following the ADEMP framework of \citet{morris2019using}. This alignment protects the visibility of non-superiority and restores the integrity of simulation research. Boundary mapping is therefore not a modest contribution but a scientific and ethical imperative for methodological transparency and applied reliability.