"Nature-based solutions", such as green stormwater infrastructure, take advantage of natural systems to tackle the increasing challenges facing the built environment. Green infrastructure is effective in reducing stormwater runoff for urban stormwater management using connected green space. Green infrastructure also delivers multiple benefits to the community (e.g., increased quality of life and public health) and environment (e.g., enhanced biodiversity, less energy use, and reduced urban heat island effect), which is adaptive to the changing climate. However, the pace and the scale of green infrastructure implementation are still not on track with the much-needed change in the urban built environment. Policy barriers, resources barriers, governance barriers, and cognitive barriers are limiting the practice. Cognitive barriers are cited as the most critical barrier because most of the barriers limiting green infrastructure stem from and are intensified by human cognition during the design and decision-making process for infrastructure. Stakeholders involved in the decision-making process for green infrastructure must weigh the perceived risks and benefits that green infrastructure provides. This dissertation aims to better understand how stakeholders perceive green infrastructure, how much they weigh risks and benefits, and test interventions to aid the decision-making process to promote more green infrastructure design. Both a stated preference survey with discrete choice modeling and two sets of experiments using neuroimaging to measure the change in neurocognition were used to explore preference construction and decision-making about green infrastructure. A sample of the public (N=946) across the U.S. participated in the survey and reported their perceptions of risk and benefit about green infrastructure. The result highlights that perceived higher risk of green infrastructure reduced people's preference for green infrastructure. In contrast, perceived higher benefit, age, education, and the use of a rating system to measure sustainability outcomes firstly contribute to people's preference construction for green infrastructure. Engineering students who were trained in stormwater infrastructure design (N=60) participated in a stormwater infrastructure design scenario. Change in students' neurocognition was measured when students made judgments and decisions between a green infrastructure design option and a conventional stormwater infrastructure design option. Two interventions, (1) telling students about a municipal resolution in support of green infrastructure and (2) priming students to think about sustainable design before evaluating design options, were tested to change perceptions about risk and benefit of stormwater design options. The results found that telling decision-makers about a green infrastructure resolution changed their neurocognition when processing perceived risk and reduced the perceived risk they associated with green infrastructure. The results also found that priming decision-makers to think about sustainable design with a rating system for sustainability significantly decreased their cognitive load when evaluating the benefits of green infrastructure and increased their stated benefits associated with green infrastructure. These findings demonstrate the effects of relatively simple choice modifications to promote more green infrastructure. The results provide insights for policy-makers, engineers, and other stakeholders involved in the early-phase decisions on effective practice to modify human choice when facing challenges with sustainable and resilient design. / Doctor of Philosophy / Green stormwater infrastructure uses connected green space to absorb and filter excessive stormwater runoff in the environment where humans live. Green infrastructure also brings multiple benefits, such as increased quality of life and public health, habitats to more creatures, and less energy use. However, the pace and the scale of green infrastructure implementation are still limited. Barriers in policy, resources, governance, and human cognition are preventing the implementation of green infrastructure. Cognitive barriers are believed to be the most critical barrier because they intensify all other barriers during the design and decision-making process for infrastructure. Stakeholders involved in the decision-making process for green infrastructure must weigh the perceived risks and benefits that green infrastructure provides. This dissertation aims to better understand how stakeholders perceive green infrastructure, how much they weigh risks and benefits, and test interventions to aid the decision-making process to promote more green infrastructure design. Both a survey with choice modeling and experiments using neuroimaging to measure the change in brain activity were used to explore preference construction and decision-making about green infrastructure. 946 people across the U.S. participated in the survey and reported their perceptions of risk and benefit about green infrastructure. The result highlights that perceived higher risk of green infrastructure reduced people's preference for green infrastructure. In contrast, perceived higher benefit, age, education, and the use of a rating system to measure sustainability outcomes positively contribute to their preference construction for green infrastructure. 60 Engineering students who were trained in stormwater infrastructure design participated in a stormwater infrastructure design scenario. Change in students' brain activity was measured when they made judgments and decisions between a green infrastructure design option and a conventional stormwater infrastructure design option. Two interventions, (1) telling students about a municipal resolution in support of green infrastructure and (2) priming students to think about the sustainable design before evaluating design options, were tested to change perceptions about the risk and benefit of stormwater design options. The results found that telling decision-makers about a green infrastructure resolution changed their brain activity when evaluating risk and reduced the perceived risk they associated with green infrastructure. The results also found that priming decision-makers to think about sustainable design with a rating system for sustainability significantly decreased their cognitive efforts when evaluating the benefits of green infrastructure and increased their stated benefits associated with green infrastructure. These findings demonstrate such relatively simple choice modifications are effective to promote more green infrastructure. Stakeholders who are involved in the early-phase decisions can take advantage of the findings about the effective practice to modify human choice when facing sustainable design challenges.

Designers do not merely develop concepts; they are increasingly involved in testing product concepts and learning consumer preference. However, designers’ decision making processes in these tasks have been little studied. In the two essays, I apply decision making frameworks to concept testing and preference learning to study consumer’s and designer’s biases. In my first essay, I study consumer bias in concept testing. When consumers test new products, they are often asked to choose which product they prefer. However, a choice question can elicit biased preference because consumers simply choose the product that is superior on the attribute serving their purchase purpose. My studies show that when consumers are asked to predict which product they will enjoy more, they are more likely to prefer the product that actually reflects their consumption utility. These findings suggest that making trade-offs is avoided in the choice question, but is encouraged in the enjoyment prediction question. Thus, a simple change of question format, in otherwise identical product comparisons, elicits different answers. This holds true when product attributes are easy to evaluate; when product attributes are hard to evaluate, changing question format does not affect consumer choice. My second essay examines designer bias in preference learning. When designers predict consumer preference for a product, they often base their predictions on consumer preference for similar products. However, this categorization-based strategy can result in biased predictions because categorical similarity is not diagnostic for preference prediction. I conducted two studies by applying a Multiple Cue Probability Learning experiment to a designer’s prediction task. I found that when subjects used a sequential learning strategy, making a sequence of predictions and receiving feedback, they increased prediction accuracy by 14% on average. When they made predictions with multiple sets, with a break between each set during which they reflected on what they had learned, their prediction accuracy further improved by 7% on average. In sum, I demonstrate bias and propose approaches to avoid them in two design tasks. My two essays show that the decision making frameworks are crucial in understanding and improving the successful outcome of the design process.

Designers do not merely develop concepts; they are increasingly involved in testing product concepts and learning consumer preference. However, designers’ decision making processes in these tasks have been little studied. In the two essays, I apply decision making frameworks to concept testing and preference learning to study consumer’s and designer’s biases. In my first essay, I study consumer bias in concept testing. When consumers test new products, they are often asked to choose which product they prefer. However, a choice question can elicit biased preference because consumers simply choose the product that is superior on the attribute serving their purchase purpose. My studies show that when consumers are asked to predict which product they will enjoy more, they are more likely to prefer the product that actually reflects their consumption utility. These findings suggest that making trade-offs is avoided in the choice question, but is encouraged in the enjoyment prediction question. Thus, a simple change of question format, in otherwise identical product comparisons, elicits different answers. This holds true when product attributes are easy to evaluate; when product attributes are hard to evaluate, changing question format does not affect consumer choice. My second essay examines designer bias in preference learning. When designers predict consumer preference for a product, they often base their predictions on consumer preference for similar products. However, this categorization-based strategy can result in biased predictions because categorical similarity is not diagnostic for preference prediction. I conducted two studies by applying a Multiple Cue Probability Learning experiment to a designer’s prediction task. I found that when subjects used a sequential learning strategy, making a sequence of predictions and receiving feedback, they increased prediction accuracy by 14% on average. When they made predictions with multiple sets, with a break between each set during which they reflected on what they had learned, their prediction accuracy further improved by 7% on average. In sum, I demonstrate bias and propose approaches to avoid them in two design tasks. My two essays show that the decision making frameworks are crucial in understanding and improving the successful outcome of the design process.