A Probabilistic Modeling Framework to Support Early-Stage Design Decisions: Comparing Traditional and Nontraditional Solving Approaches

Abstract Existing models for evaluating early-stage designs typically 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 firms increasingly leverage nontraditional sources of innovation and creativity (e.g., through crowdsourcing). To address this, we introduce a modeling approach that enables comparison of design solutions from multiple solving paradigms. It represents engineering design as a problem-solving process, with solutions 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. The quality of each solution 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 particularly significant when evaluating designs from nontraditional solvers, as they are more likely to diverge from dominant solving paradigms. As firms increasingly turn to nontraditional sources of expertise for innovation, this modeling approach could enable comprehensive identification and fair assessment of a range of design solutions.