How Do We Know It’s Complex? Reinterpreting Complexity as an Epistemic Property to Clarify Persistent Ambiguities

Complexity is often framed as an ontological property of systems, with profound epistemological and methodological implications–particularly in the social sciences, where it is invoked to justify fundamental limits on the applicability of natural science frameworks. Building on previous discussions by McIntyre, Ruphy, and more recently Francq, this paper challenges this ontological premise by arguing that complexity is best understood primarily as an epistemic property–that is, a feature of the models we use to represent and study systems, rather than an intrinsic characteristic of the systems themselves. Moreover, complexity depends on pragmatic elements, such as the goals at hand or the specific questions being asked. The foundational premise of our argument is that claims about a system’s complexity are invariably grounded in comparative judgments between models, whether formal or intuitive. Such judgments, in turn, depend critically on pragmatic and epistemic goals (e.g., the questions asked or the scale of analysis). We take this idea seriously and formalize it by defining the epistemic complexity of a system with respect to a given goal as the complexity of the least complex model capable of achieving that goal. Our account relies on a complexity score applicable to models, which we develop specifically for the restricted class of agent-based models (ABMs) in the social sciences. By reframing complexity as primarily an epistemic property, we dissolve persistent ambiguities in the literature–particularly the circular reasoning that arises when ontological claims about complexity are used to justify alternative epistemological frameworks. Our approach shifts the debate from metaphysical assertions to empirical questions about model performance, offering a constructive path forward for complexity research in both the natural and social sciences.