While people often think they veridically perceive much of the visual surround, recent findings indicate that when asked to detect targets such as gratings embedded in visual noise, observers make more false alarms in the unattended periphery. Do these results from psychophysics studies generalize to more ecologically valid settings? We used a modern game engine to create a simulated driving environment where participants (as drivers) had to make judgments about the colors of pedestrians' clothing in the periphery. Confirming our hypothesis based on previous psychophysics studies, we found that subjects showed liberal biases for unattended locations when detecting specific colors of pedestrians' clothing. A second experiment showed that this finding was not simply due to a confirmation bias in decision-making when subjects were uncertain. Together, these results support the idea that in everyday visual experience, there is subjective inflation of experienced detail in the periphery, which may happen at the decisional level.

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A ccessibility of electric vehicle (EV) charging stations is an important factor for adoption of EV, which is an effective green technology for reducing carbon emissions. Recognizing this, many governments are contemplating ideas for achieving EV adoption targets, such as constructing extra EV charging stations directly or offering subsidies to entice automakers to construct more EV charging stations. To achieve these targets, governments need to coordinate with automakers to ensure that the total number of charging stations is planned optimally. We study this coordination problem by considering the interactions among the government, automakers, and consumers, our equilibrium analysis yields three major results. First, both the government and the automaker should build extra EV charging stations when their construction costs are independent. Simultaneously, the government should offer a per-station subsidy to the automaker only when the adoption target and the construction cost are both high. However, when the construction costs are dependent, the government should delegate the construction to the automaker by offering a per-station subsidy. Second, when the government considers consumer purchase subsidy as an extra lever, we find that the purchase subsidy for consumers is more cost-effective than offering a per-station subsidy to the automaker. Third, the structure of the optimal government policy remains the same regardless of whether the government's goal is to improve EV adoption or consumer welfare. Our results can serve as guidelines for governments when contemplating coordination with automakers for the construction of EV charging stations to improve EV adoption as well as consumer welfare further.

The chaotic response of the US Strategic National Stockpile to COVID‐19 during 2020 highlighted the inadequacy of the inventory‐based approaches to disaster response. This paper examines the integration of stockpile inventory, backup capacity, and standby capability to meet the disaster‐related surge in demand in the future. We present a two‐period model of such an integrated system for consumable items with uncertain demand that follows a general probability distribution. Our model incorporates standby capability in period 1 that can be converted to additional capacity for use in period 2, with the conversion yield being deterministic or stochastic. Our main results are: (1) Adding capacity in addition to inventory is beneficial only when the capacity reservation‐related costs are relatively lower than the inventory‐related costs. In this case, adding capacity will decrease the inventory needed in both periods, the shortfall probability, and the total expected cost. (2) Adding capability in period 1 is cost‐effective only when the ratio of capability‐development cost to conversion yield is lower than the capacity reservation cost. In this case, investing in capability results in less inventory and less reserved capacity in period 2. (3) Higher uncertainty in capability conversion yield reduces the attraction of developing capability in period 1. Consequently, less capability would be developed in period 1, while more inventory and capacity would be needed in period 2 in the face of a higher shortfall probability.

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