ANN Crowds: Harnessing Collective Wisdom in Design Prediction

This thesis presents and evaluates an approach to early-stage product performance prediction by harnessing the "wisdom of the crowd" embodied in Artificial Neural Network (ANN) Crowds. Unlike traditional crowd wisdom, which leverages responses from large groups of people, this research explores the concept of using 189 distinct ANN architectures, each replicated 100 times, as a collective decision-making entity, thus forming an ANN Crowd. The central inquiry in this study revolves around the notion of whether every agent within the ANN Crowd should possess an equal influence. To address this question, the research conducts a comprehensive exploration of the sensitivity of key influencing factors, including training set selection, the configuration of nodes and layers, and architectural attributes, on the performance of the ANN Crowd. This investigation aims to refine the ANN Crowd, making it more universally applicable. The first aspect explores the impact of training set selection in predictive accuracy. The results clearly demonstrate that training set selection has a statistical and practical influence prediction accuracy, especially when it includes edge cases. This finding provides a crucial guideline for decision-makers, advocating for the strategic inclusion of challenging examples in training sets to improve predictive accuracy. The second facet investigates the complex interplay of architectural attributes within the ANN Crowd. By categorizing architectural attributes based on Normality, Centrality, and Width, the analysis shows that the number of nodes within architectural configurations does not have a statistically significant impact on prediction accuracy. This challenges the conventional belief that complexity leads to improved performance, providing practical insights for architectural design. In conclusion, this research offers valuable insights into the predictive capabilities of ANN Crowds. It extends practical implications to engineering design and decision-making processes, positioning ANN Crowds as a vital tool across diverse industries. The findings and guidelines provide a foundation for data-driven practices, enhancing efficiency, and adding value to businesses. Acknowledging its limitations, this research paves the way for future work, encompassing a broader range of datasets, architectural factors, and validation studies.