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Due to the many large earthquakes that have occurred in recent years, the role of seismic risk reduction in building resilient cities has become a matter of concern. The serious disaster damage brought by seismic hazards causes the adoption of migration policies such as building control in the preparedness phase. However, the restricted budget of governments resulting from the global state of economic distress generates a prioritization problem. A decision support framework could be helpful for governments to systematically integrate the complex information when implementing disaster risk reduction policies toward sustainable development. The purpose of this study was to construct an analytical framework based on Geographic Information System (GIS) and Data Envelopment Analysis (DEA) for addressing the prioritization problem by calculating policy efficiency. The spatial DEA-based framework combines indices calculation, spatial database construction, and DEA. Taiwan is an island located in the Circum-Pacific Belt, and has paid long-term attention to adopting policies for earthquake disaster prevention. A policy of earthquake-oriented urban renewal combining enhanced building capacity and city resilience has recently been implemented. A case study of the Yongkang district of the Tainan Metropolis in Taiwan was conducted in this study. The results show an operable framework and propose a suggestion for planning efficient policy priorities in each decision-making unit. In sum, the analytical framework proposed in this study could be a component of a decision support system for governments to adopt disaster risk reduction policies in the process of policy-making and implementation.

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The effects of risky-choice framing are well-established and have been demonstrated in several decision contexts. Recent research has pointed to a role for affect and emotions in risky-choice framing, but those findings may have been influenced by carry-over effects due to the use of multiple decision problems. In a one-off decision, the effects of riskychoice framing on affect and emotions remain unclear. This article extends the risky-choice framing literature by using the Emotion-Imbued Choice model to investigate whether integral fear and anger can account for the effects of risky-choice framing in a one-off decision. In two studies involving a one-off decision about internet connectivity and human lives respectively, we expected higher levels of integral fear in participants who chose the certain option in the positive framing condition as compared to the negative framing condition, and also higher levels of integral anger in participants who chose the risky option in the negative framing condition as compared to the positive framing condition. Our findings did not support these hypotheses and suggest that the effects of risky-choice framing are not due to integral emotions. We explained our findings by proposing that the choice architecture involved in risky-choice framing prevents integral emotions from becoming attached to the choice options because it offers a less effortful decision tactic than considering one's emotional response to those options. We call for future research to investigate this possibility and to also consider the demand characteristics of conducting risky-choice framing problems online.

This paper develops a multi-period product pricing and service investment model to discuss the optimal decisions of the participants in a supplier-dominant supply chain under uncertainty. The supply chain consists of a risk-neutral supplier and two risk-averse manufacturers, of which one manufacturer can provide real-time customer service based on the Internet of Things (IoT). In each period of the Stackelberg game, the supplier decides its wholesale price to maximize the profit while the manufacturers make pricing and service investment decisions to maximize their respective utility. Using the backward induction, we first investigate the effects of risk-averse coefficients and price sensitive coefficients on the optimal decisions of the manufacturers. We find that the decisions of one manufacturer are inversely proportional to both riskaverse coefficients and its own price sensitive coefficient, while proportional to the price sensitive coefficient of its rival. Then, we derive the first-best wholesale price of the supplier and analyze how relevant factors affect the results. A numerical example is conducted to verify our conclusions and demonstrate the advantages of the IoT technology in long-term competition. Finally, we summarize the main contributions of this paper and put forward some advices for further study.