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   Singham, Dashi I.; Lam, Henry (January 2020, Winter Simulation Conference)

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   https://doi.org/10.1523/JNEUROSCI.2337-17.2018  

   Qi, Song; Footer, Owen; Camerer, Colin F.; Mobbs, Dean (February 2018, The Journal of Neuroscience)
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   Bayrak, Emrah A.; Sha, Z. (October 2020, Journal of mechanical design)
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   https://doi.org/10.1017/pds.2025.10024  

   Li, Xiang; Chen, Siyu; Bayrak, Alparslan Emrah; Sha, Zhenghui (August 2025, Proceedings of the Design Society)

   Design decision-making under competition is a critical challenge in real-world engineering design. These challenges are compounded by bounded rationality, where cognitive limitations and imperfect information influence decision-making strategies. To address these issues, we develop a game-theoretic research platform to investigate team-based design under competition. This platform abstracts and simulates real-world competitive design scenarios through controlled experiments. It features a user-friendly interface to collect behavioral data, which supports the analysis of team and individual strategies. Additionally, we validated the platform through a pilot study, demonstrating its ability to capture realistic design features and generate meaningful insights into competitive design behaviors. 

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5. A Quantitative Analysis of Rational Decisions Under Uncertainty in Engineering Systems Design

   https://doi.org/10.1017/pds.2025.10039  

   Mutlu, Kaan; Javadpour, Nikoo; Sha, Zhenghui; Bayrak, Alparslan Emrah (August 2025, Proceedings of the Design Society)

   Rational decision-making is crucial in the later stages of engineering system design to allocate resources efficiently and minimize costs. However, human rationality is bounded by cognitive biases and limitations. Understanding how humans deviate from rationality is critical for guiding designers toward better design outcomes. In this paper, we quantify designer rationality in competitive scenarios based on utility theory. Using an experiment inspired by crowd-sourced contests, we show that designers employ varied search strategies. Some participants approximate a Bayesian agent that aimed to maximize its expected utility. Those with higher rationality reduce uncertainty more effectively. Furthermore, rationality correlates with both the proximity to optimal design and design iteration costs, with winning participants exhibiting greater rationality than losing participants. 

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