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

本项目的目标是研究团队竞争下信息不确定性对设计合理性的影响，并开发一种竞争感知的人工智能（AI）助手，用于设计空间探索和开发。沟通问题已被广泛认为是影响团队决策中设计结果的关键因素，因为在沟通过程中共享的信息可能是不完整和不完美的。因此，通过理解信息不确定性对团队中人类设计决策的作用来解决这些问题，可以更好地协调团队决策，促进人类与人工智能的合作，并在大型工程设计项目中节省大量成本。然而，尽管在团队中建模设计决策方面取得了重大进展，但目前的文献忽略了设计过程的两个基本方面：人类设计师具有有限理性，大多数设计活动都是在竞争中发生的，无论是有意识还是无意识的。这个项目的动机是通过开发理论和实验结构来填补这一空白，以计算模拟人类设计师在竞争环境中的顺序决策，并通过实验测量他们的有限理性。预期的结果是一套新的知识，包括指标，模型，算法和测试平台，在竞争中存在的不确定性的设计团队中的人类行为。更广泛的影响将产生直接从事不同的本科生在研究活动，通过新生介绍工程研究（FIRE）计划在得克萨斯大学奥斯汀分校和品尼高和克拉克学者计划在史蒂文斯理工学院。该项目是通过回答两个研究问题驱动：1）什么是信息不确定性的影响（例如，当团队成员之间共享的设计信息不完全时）对团队间竞争下设计合理性的影响？2)在更大的团队规模范围内，这种影响会有什么不同？为了回答这两个问题，计划采用一种跨学科的研究方法，将描述性、规范性和预测性分析相结合。特别是，我们将开发博弈论模型来模拟竞争下的顺序设计决策，协同整合两种类型的顺序学习模型，即，理论驱动的规定模型（例如，贝叶斯优化）和数据驱动的预测模型（例如，长短期记忆单位）。新的方法明确建模设计师的看法，他们的对手的过去的表现，并预测对手的未来决策，从而提供了一种定量的方法来研究的影响，设计师之间共享的不确定信息在一个团队中对他们的决定，在竞争中对其他团队。实验将收集行为数据，以研究人类的非理性，基准的理性行为预测的理论模型，将随着时间的推移在设计比赛的过程中发生变化。研究结果将在太阳能系统设计的现实世界设计挑战中得到验证，多个团队将争夺奖项。为了使更广泛的研究社区受益，该项目将建立一个开放的设计基础设施，以共享项目数据和发现。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.