Abstract Existing models for evaluating early-stage designs assume that solutions follow dominant solving approaches in the problem domain. While effective for comparing alternatives within dominant solving approaches, these models often undervalue or overlook solutions that use atypical or novel approaches, especially when these differ significantly in key design variables. This bias prematurely constrains the design space and becomes a pressing problem as atypical approaches are increasingly used as firms increasingly leverage non-traditional sources of innovation and creativity. To address this, we introduce a modeling framework that enables fair comparison of design solutions from multiple solving paradigms. It represents design as a problem-solving process, and solutions are generated by selecting concepts and embodiments to achieve specific functions. The model simulates how different solvers navigate this process based on their expertise, producing a variety of solutions rather than those limited to dominant strategies. Each solution's quality is represented as a probability distribution over performance and cost. The model's effectiveness is demonstrated using a robotic arm design problem, leveraging a dataset from a large-scale field experiment. Results show that the model can estimate performance and cost across different solving approaches, capturing valuable solutions that traditional models would miss. This is significant when evaluating designs from non-traditional solvers, as they are more likely to diverge from dominant solving paradigms. As firms increasingly turn to non-traditional sources of expertise, this type of modeling approach could enable comprehensive identification and fair assessment of a variety of design solutions.
Dharmarajan et al. (Thu,) studied this question.