Peak shifting is a key method to enhance the resilience of water supply infrastructure. Due to limitations related to the costs of smart meters, experimentally investigating the impact of a time-of-use tariff on inducing peak shifting in water usage has been challenging. Having introduced smart meters across 1,890 households, this study investigated behavioral responses to reduced water prices during off-peak hours (i.e., by increasing the relative cost of peak times). This was achieved by implementing a pricing scheme offering a 60% discount from 23:00 to 05:59 hours and a 20% discount from 10:00 to 16:59 hours. The results revealed significant changes in water usage behavior near the boundary between off-peak and peak times, suggesting a shift in water usage behavior towards the off-peak period. Moreover, a time-series analysis demonstrated the peak shifting induced by off-peak price reductions. Furthermore, this study examines cognitive processing and its impact on altering water usage behaviors on an hourly basis. These findings suggest that reducing prices during off-peak periods can effectively induce peak shifting across a broad range of times.

This study demonstrates a new approach for boosting peoples decision-making abilities. Previous studies have demon-strated that those who have less knowledge are more likely to make a scientifically biased decision. Therefore, we canexpect that providing them with more knowledge can reduce their biases. However, since people have difficulty changingtheir minds in response to knowledge that contradicts their opinions, it is unclear whether people accept and appropriatelyunderstand the provided knowledge. To more efficiently help these people, this study focused on the illusion of explanatorydepth, which means that the knowledge people think they have is greater than the knowledge they actually possess. Weconducted two experiments and demonstrated that (i) those who had a stronger illusion of explanatory depth were morelikely to make a logically biased decision, and (ii) by informing them of their illusion and providing them with objectiveknowledge, we could reduce their bias.

People apply more frequently when “apply” is the default choice (Apply Default architecture) than when “do not apply” is the default choice (Not-Apply Default architecture). However, Apply Default architecture might let them make choices inconsistent with their preferences as this architecture is counterintuitive. Those trying to apply might mistakenly choose to not apply under Apply Default architecture. In this study, we hypothesized that people's choices under No-Default architecture (i.e., a choice architecture without a default option) are less consistent with those under Apply Default architecture than those under Not-Apply Default architecture (Hypothesis 1). We also hypothesized that people who spent more time on making decisions would make choices consistent with their preferences because when people spend sufficient time to understand the construction of Apply Default architecture, they can make choices consistent with their preferences (Hypothesis 2). We recruited 997 participants and asked them to make decisions under No-Default and Default architectures (Apply Default or Not-Apply Default architecture). The results supported both Hypothesis 1 and Hypothesis 2. A method to help applicants make choices consistent with their preferences is finally discussed.