<p>Because of the increasing internal interconnections and unpredictable social behaviors, the design of a socio-technical system (STS) is more challenging than the design of conventional technical systems. The challenges are manifested in two aspects: the complexity of human behavior predicability and the complexity of system controllability due to the uncertainties resulting from socio-technical interactions. In response to these challenges, my research focuses on applying the complex network- based methodology to model STS and support STS robust design. While the long-term goal of my research is to create a network-based framework for the robust design of STS, my more imminent objective includes three parts. The first part is to gain an in-depth understanding of STS through analyzing the interplays between the local-level network structures and global-level system performance using network motif theories and exponential random graph models (ERGM). The second part is to establish a design approach that can support the design decision-making (e.g., capacity planning decisions) for improving the robustness of STS against uncontrollable noise effects (e.g., seasonal changes). The third part is to develop a predictive model of predicting the social- technical interactions within an STS to support the validation of the proposed design approach by applying network representation learning (Chen et al., 2018). In order to do so, in my previous work, I have proposed a network motifs-based method to study the features of local-level structures of STS and its correlations to the system-level performance (Xiao &amp; Sha, 2020). Along with this, I have also developed a network motifs-based design method to supporting the capacity planning of STS for improved seasonal robustness. A set of network-based metrics are created for quantifying system robustness and the decision-making criterion for capacity planning. The utilities of these two methods are demonstrated in bike-sharing systems (BSS). Currently, I am working to build a model to predict the social-technical interplays within an STS to verify the effectiveness of the network-based robust&nbsp;design method. To establish the predictive model, both statistical regression methods and network embedding techniques are under consideration.</p>

Robust Design of Complex Socio-Technical Systems Using Complex Networks

Paper Proceedings: https://asmedigitalcollection.asme.org/IDETC-CIE/IDETC-CIE2021/volume/85369

ASME 2021 International Design Engineering Technical Conferences & Computers and Information in Engineering Conference (IDETC/CIE2021)

Supporting the need among researchers worldwide for additional online and multimedia content, ASME now offers the ASME Conference Video Collection. This platform will collect and provide long-term access to conference presentation videos, encompassing all technology areas within ASME's technical conference and events program. About 30,000-40,000 engineers across all mechanical engineering and related disciplines participate in ASME conferences each year. This includes authors of approximately 6,000 to 8,000 technical papers published by ASME, the majority of whom also submit video presentations for our virtual conferences. The video collection will be available as an add-on to existing product packages or as a separate subscription.