TY - JOUR
T1 - Integrating Sequence Learning and Game Theory to Predict Design Decisions under Competition
AU - Bayrak, Alparslan Emrah
AU - Sha, Zhenghui
N1 - Publisher Copyright:
© 2020 BMJ Publishing Group. All rights reserved.
PY - 2021/5/1
Y1 - 2021/5/1
N2 - Design can be viewed as a sequential and iterative search process. Fundamental understanding and computational modeling of human sequential design decisions are essential for developing new methods in design automation and human-AI collaboration. This paper presents an approach for predicting designers' future search behaviors in a sequential design process under an unknown objective function by combining sequence learning with game theory. While the majority of existing studies focus on analyzing sequential design decisions from the descriptive and prescriptive point of view, this study is motivated to develop a predictive framework. We use data containing designers' actual sequential search decisions under competition collected from a black-box function optimization game developed previously. We integrate the long short-term memory networks with the Delta method to predict the next sampling point with a distribution, and combine this model with a non-cooperative game to predict whether a designer will stop searching the design space or not based on their belief of the opponent's best design. In the function optimization game, the proposed model accurately predicts 82% of the next design variable values and 92% of the next function values in the test data with an upper and lower bound, suggesting that a long short-term memory network can effectively predict the next design decisions based on their past decisions. Further, the game-theoretic model predicts that 60.8% of the participants stop searching for designs sooner than they actually do while accurately predicting when the remaining 39.2% of the participants stop. These results suggest that a majority of the designers show a strong tendency to overestimate their opponents' performance, leading them to spend more on searching for better designs than they would have, had they known their opponents' actual performance.
AB - Design can be viewed as a sequential and iterative search process. Fundamental understanding and computational modeling of human sequential design decisions are essential for developing new methods in design automation and human-AI collaboration. This paper presents an approach for predicting designers' future search behaviors in a sequential design process under an unknown objective function by combining sequence learning with game theory. While the majority of existing studies focus on analyzing sequential design decisions from the descriptive and prescriptive point of view, this study is motivated to develop a predictive framework. We use data containing designers' actual sequential search decisions under competition collected from a black-box function optimization game developed previously. We integrate the long short-term memory networks with the Delta method to predict the next sampling point with a distribution, and combine this model with a non-cooperative game to predict whether a designer will stop searching the design space or not based on their belief of the opponent's best design. In the function optimization game, the proposed model accurately predicts 82% of the next design variable values and 92% of the next function values in the test data with an upper and lower bound, suggesting that a long short-term memory network can effectively predict the next design decisions based on their past decisions. Further, the game-theoretic model predicts that 60.8% of the participants stop searching for designs sooner than they actually do while accurately predicting when the remaining 39.2% of the participants stop. These results suggest that a majority of the designers show a strong tendency to overestimate their opponents' performance, leading them to spend more on searching for better designs than they would have, had they known their opponents' actual performance.
KW - design decision-making
KW - design process
KW - design under competition
KW - game theory
KW - sequence learning
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U2 - 10.1115/1.4048222
DO - 10.1115/1.4048222
M3 - Article
AN - SCOPUS:85096323136
SN - 1050-0472
VL - 143
JO - Journal of Mechanical Design
JF - Journal of Mechanical Design
IS - 5
M1 - 051401
ER -