Collaborative Research: Design Decisions under Competition at the Edge of Bounded Rationality: Quantification, Models, and Experiments

The objective of this project is to investigate the impact of information uncertainty on design rationality under competition among teams and to develop a competition-aware artificial intelligence (AI) assistant for design space exploration and exploitation. Communication issues have been broadly recognized as a critical factor that impacts design outcomes in team decisions, because the information shared during communication can be incomplete and imperfect. Therefore, addressing such issues by understanding the role of information uncertainty on human design decisions in teams could lead to better coordination of team decisions, advancement in human-AI collaborations, and significant cost savings in large-scale engineering design projects. However, despite the significant progress in modeling design decision-making in teams, current literature neglects two fundamental aspects of the design process: human designers have bounded rationality, and most design activities happen under competition, whether consciously or unconsciously. This project is motivated to fill this gap by developing theoretical and experimental constructs to computationally model human designers’ sequential decisions in a competitive environment and to experimentally measure their bounded rationality. The expected outcome is a suite of new knowledge, including metrics, models, algorithms, and testbeds, on human behavior in design teams under competition in the presence of uncertainties. Broader impacts will be generated by directly engaging diverse undergraduate students in research activities through the Freshman Introduction to Research in Engineering (FIRE) program at the University of Texas at Austin and the Pinnacle and Clark Scholars programs at Stevens Institute of Technology. This project is driven by answering two research questions: 1) what are the effects of information uncertainty (e.g., when design information shared between team members is incomplete) on design rationality under competition among teams? 2) How would such effects differ across a wider range of team sizes? To answer the two questions, an interdisciplinary research approach is planned that combines descriptive, prescriptive, and predictive analytics. In particular, we will develop game theoretic models to model sequential design decisions under competition, synergistically integrating two types of sequential learning models, i.e., theory-driven prescriptive models (e.g., Bayesian optimization) and data-driven predictive models (e.g., long-short term memory units). The new approach explicitly models designers’ perceptions of their opponents’ past performance and predicts the opponent’s future decisions, thereby providing a quantitative way to study the influence of uncertain information shared among designers in a team on their decisions in competition against other teams. Experiments will be conducted to collect behavioral data to study how human irrationality, benchmarking on rational behaviors predicted by the theoretical models, would change over time during the course of the design competition. The research findings will be validated in a real-world design challenge on solar system design, where multiple teams compete for awards. To benefit a broader research community, this project will build an open design infrastructure to share the project data and findings. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.