Ecoadaptive intelligence theory: A framework for spatial knowledge production under environmental complexity

How is spatial knowledge produced when the effectiveness of planning interventions cannot be reliably known in advance? This paper addresses this question through the development of Ecoadaptive Intelligence Theory (EIT), a framework theory of spatial knowledge production for a bounded class of urban environmental-planning problems. While planning scholarship has long recognised the significance of complexity, uncertainty, and learning, it has offered limited explanation of the epistemic conditions under which knowledge cannot be fully specified prior to action and must instead emerge through engagement with the system being planned. The paper argues that certain environmental-planning contexts are characterised by three structural properties: causal opacity, contextual contingency, and temporal recursion. Together, these properties generate a structural knowledge deficit, whereby the information required to configure effective interventions cannot be derived solely from predictive modelling, precedent, or expert judgement before implementation. Under such conditions, spatial knowledge is not simply applied but produced through iterative interaction with environmental feedback. From this premise, EIT develops an integrated epistemological framework comprising a set of epistemic principles, an adaptive cycle of intervention and learning, and six falsifiable propositions that specify the theory’s empirical commitments. EIT conceptualises planning not as the application of complete prior knowledge to environmental problems, but as a process through which actionable knowledge emerges through successive cycles of intervention, observation, interpretation, and adjustment. The paper contributes to planning theory by explaining when anticipatory analysis becomes insufficient as a basis for decision-making and why iterative spatial learning becomes necessary. Rather than displacing predictive approaches, EIT situates them within a broader epistemology of environmental adaptation that clarifies the relationship between prediction, experimentation, and learning. The theory is advanced as a bounded but generalisable framework for understanding how spatial knowledge is generated under conditions of environmental complexity and as a foundation for future theoretical refinement and empirical testing.