Looking back on my undergraduate days in Civil, Urban, and Geosystem engineering, I was deeply engaged in the mindset that the key to solving big environmental problems was mainly technical — better designs, stronger materials, and more efficient man-made hard and soft infrastructure. However, as I took liberal arts courses (e.g., political science, economics, aesthetics, and history) alongside my engineering classes, I began to see a different side of problem-solving. Learning about institutions (rules and norms) and human behavior made me realize that even the most brilliant engineering solutions do not work in isolation — they are not like “plug-and-play” technologies but only fully function if they fit within complex governance systems that can determine their success or failure.
Take a small, local solar power system, for example — one designed to generate, store, and share electricity within a community. Technically, it might be well-designed, but its sustainability depends on more than just solar panels and batteries. If there are not clear and fair rules for how the electricity is shared and the infrastructure is maintained (institutions), or if people do not cooperate in using the energy responsibly and taking care of the system (human behavior), it can quickly break down. Without proper coordination, some might overuse electricity while others don’t get enough, or maintenance might be neglected, leading to system failure. This realization of the role of institutions and human behavior led me to shift my focus from engineering alone to the bigger picture — how to govern natural environments and man-made infrastructure in a more sustainable way. That is why I pursued an M.A. in Political Science and later earned a Ph.D. in Environmental Social Science.
During my postdoc in Singapore and the U.S., I had the incredible opportunity to work on interdisciplinary research projects with civil and environmental engineers, geohydrologists, public policy experts, and resource economists. These collaborations were driven by two key factors.
First, we shared a fundamental perspective: social, ecological, and technological components are deeply interconnected and constantly evolving through complex interactions. This common understanding pushed us to tackle pressing environmental challenges together. For instance, we explored how water reservoir governance should adapt to climate uncertainty and how dam operators’ biases toward past climate events might influence their responses to future extreme weather. We investigated ways to design better policies for managing groundwater depletion by fostering cooperation between farmers, governments, and scientists. We studied how people’s memories of past natural disasters could be leveraged to reduce the social impact of future climate events. Other projects focused on balancing traffic safety and environmental protection by reducing the overuse of de-icing materials and finding ways to counter the spread of climate misinformation on social media.
The second reason these collaborations flourished was our ability to integrate diverse research methods and learn from one another. Different approaches offer unique strengths. For example, mathematical modeling using ordinary differential equations (ODEs) helps us examine how key system variables evolve over time and whether they reach a stable state. Computational modeling, particularly agent-based modeling (ABM), allows us to simulate interactions among various agents — such as humans, biological organisms, environmental elements, and technologies — and analyze how new patterns emerge over time. Statistical analysis is essential for testing relationships between variables and determining their impact on specific outcomes. If we have a smaller dataset but need to identify which combinations of factors influence an event, we turn to Qualitative Comparative Analysis (QCA), which applies mathematical set theory. Controlled behavioral experiments offer direct insights into human decision-making by examining how participants adjust their responses to different experimental conditions. Meanwhile, institutional analysis of environmental policy documents helps categorize governance rules and map out interactions between key stakeholders and man-made infrastructure.
Beyond their individual strengths, these methods also complement each other. For example, data from behavioral experiments can inform mathematical and computational models, and in turn, modeling results can be tested through further experiments. This cross-method approach enables a deeper understanding of social-ecological-technological systems dynamics, making interdisciplinary collaboration both necessary and rewarding.
Through my multidisciplinary journey, I have refined my initial understanding of the critical role institutions and human behavior play in making technological solutions work. The updated insight is that human behavior and environmental governance are often deeply shaped by the biophysical world — something engineers and natural scientists work hard to improve. This means that, whether you realize it or not, you are a key decision-maker — allocating limited resources like materials and energy, negotiating trade-offs between efficiency and fairness, and collaborating with stakeholders, from governments to local communities and NGOs.
If I may suggest, the sooner you begin to recognize this, the better prepared you might be to develop technological solutions that align with human-environmental systems. Embracing this broader perspective not only enriches your problem-solving toolkit but also empowers you to make a tangible difference. By acknowledging the social dimensions of your technical work, you can bridge the gap between innovation and real-world impact. So, dare to think beyond engineering as just a technical challenge — see it as a way to shape a more sustainable and equitable future. You have the potential to lead change, and it starts with seeing the bigger picture.
