A Bounded-Optimality Framework for Evidence-Informed Decisions in Resource-Constrained R&D Organizations

Consequential decisions shape industrial research and development productivity, yet organizations make them with limited time, evidence and expertise. This problem is acute in startups, where the immediate cost of decision effort is visible but the benefit of avoiding future failures is uncertain and delayed. Organizations must balance decision quality against process burden while retaining traceability to assess decision practices. Here we introduce the concept of bounded optimality and an operational Best Decision Practice (BDP) framework for managing this trade-off. A predefined evidence budget sets the proportionate effort available for identifying, appraising and applying reasonably accessible evidence. Within this boundary, the bounded-optimal action is the feasible action with the highest expected performance. A traceable decision record and audit permit assessment of structural adherence and substantive adequacy, while the shift from opt-in to opt-out adherence supports delegation and accountability. The framework translates functions established in decision-analysis, evidence-appraisal, quality-system and audit frameworks into a lean and actionable format scalable across projects, departments and organizations. Because most practices are already used in industrial R&D, the practical change is their consistent application and written capture. The framework provides testable constructs for improving the transparency and consistency of resource allocation and decision practices to empirically validate and quantify its reliability and effectiveness.