Utilizing Markov Chains to Understand Operation Sequencing in Design Tasks
C. McComb, J. Cagan, and K. Kotovsky
2016, Design Computing and Cognition '16
Design often involves searching for a final design solution by iteratively modifying and adjusting a current design. Through this process designers are able to improve the quality of the current design and also learn what patterns of operations are most likely to lead to the quickest future improvements. Prior work in psychology has shown that humans can be adept at learning how to apply short sequences of operations for maximum effect while solving a problem. This work explores the sequencing of operations specifically within the domain of engineering design by examining the results of a human study in which participants designed trusses. A statistical analysis of the data from that study uses Markov Chains to show with high confidence that meaningful operation sequences exist. This work also uses an agent-based modeling framework in conjunction with Markov Chain concepts to simulate the performance of teams with and without the ability to learn sequences. These computational studies offer confirmation for the conclusion that sequence-learning abilities are helpful during design.