Learning from Outcomes Shapes Design Innovation Strategies: A Cross-Disciplinary Approach

Abstract

This paper explores how learning from the outcomes of design decisions influences the adoption of different innovation strategies, specifically contrasting rule-based design with cost-benefit reasoning in interdisciplinary contexts. Drawing inspiration from metacognitive learning principles, we propose a framework where design teams adapt their strategic reliance on established design rules versus utilitarian cost-benefit analysis based on the perceived success of past project outcomes. Through computational modeling and design scenarios, we demonstrate that adaptive learning mechanisms can lead to individual and team-level differences in design strategy preferences. This learning is shown to transfer to novel design challenges and impact the overall effectiveness of innovation processes. Our findings suggest that the dynamic interplay between experiential learning and strategic decision-making is crucial for fostering adaptable and successful design innovation in complex, cross-disciplinary environments.