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Social technologies have enabled the emergence of online platforms that provide offline service consultations and recommendations. In this environment, economic inefficiency arises when customers are not fully aware of their horizontally differentiated preferences. With its expertise or data dominance, a platform can be more informed about customers' hidden preferences. We focus on an instrumental social technology, that is, targeting, which is a type of data-driven personalized information provision to manipulate customers' beliefs about service quality. We propose a Hotelling model wherein customers are sensitive to the delays for service while making Bayesian belief updates based on a platform's recommendations. When customers self-select their favorite service, their choices impose negative externalities through congestion and welfare loss. Our results indicate that service recommendations allow customers to navigate toward the more appropriate service, thus improving matching efficiency, reducing congestion costs, and enhancing aggregate customer welfare. We further identify the role of "information transparency" and study how the platform should strategically release information by making personalized service recommendations to customers. Interestingly, when a customer-centric platform maximizes aggregate customer welfare, we identify the "value of opaqueness" by strategically withholding service recommendations from a subset of customers and notice that this effect is more pronounced for a profit-seeking platform. Our results offer a better understanding of information transparency policies in the joint design of service recommendation systems and pricing mechanisms. [ABSTRACT FROM AUTHOR]

Motivated by a widely practiced strategy to combine the growth of the solar energy sector with poverty mitigation, we propose stylized models of households selling extra solar energy back to the grid, which generates a steady stream of income to overcome adoption barriers for solar panels, that is, high adoption cost and generation variability. By considering households' strategic consumption shifting behavior in response to varying intertemporal market electricity prices, we have presented the equilibrium adoption number and households' equilibrium profit. We have also demonstrated the government's optimal subsidy to reach a socially optimal adoption level. Furthermore, we investigate the popular Public Private Partnership (PPP) model in developing countries to promote the investment of solar panels. Under the PPP program, the private firms share both upfront cost and electricity revenue with households, while the government may provide subsidy to further encourage adoption. Despite the popularity of the PPP scheme, our model documents an unexpected negative implication by inducing a lower overall adoption level than the traditional scheme: As the PPP scheme relieves budgetary burden of households in adopting solar panels (infrastructure cost), anticipating the long‐term revenue loss, they are less likely to participate in the first place. However, combined with the government intervention via adoption subsidy, the PPP scheme can lead to a win‐win solution for all. [ABSTRACT FROM AUTHOR]

This study aims to examine and design operational strategies for mixed flows of autonomous vehicles (AVs) and human‐driven vehicles (HVs). We propose a stylized model wherein utilitarian individuals either drive HVs or take privately operated AVs that are collectively dispatched. In the baseline mixed policy where AVs and HVs share the same lanes, we find that AVs under‐join the traffic (queue) while HVs over‐join. We identify a "crowding‐out effect" such that AVs will tend to mitigate the over‐joining HVs and reduce overall congestion/throughput. To improve the traffic efficiency of the baseline mixed policy, we consider a fully dedicated policy in which both AVs and HVs are segregated to different lanes and a partially dedicated policy in which only AVs enjoy dedicated lanes. We find that dedicated policies outperform the mixed policy in both social welfare and the aggregate throughput when the platooning effect is moderate or strong. Exact conditions are derived for the selection of fully dedicated policy and partially dedicated policy. Furthermore, we find that a carefully designed subsidy is necessary for the dedicated policy to simultaneously improve the social welfare and the throughput when the platooning effect is very weak. These results shed interesting light on the policy regulation for the emerging mixed autonomous paradigm: a dedicated policy with proper lane design and subsidy (if necessary) will improve both social welfare and aggregate throughput. [ABSTRACT FROM AUTHOR]

Inspired by new technologies to monitor parking occupancy and process market signals, we aim to expand the application of demand-responsive pricing in the parking industry. Based on a graphical Hotelling model wherein each garage has information for its incoming parking demand, we consider a general competitive spatial pricing in parking systems under an asymmetric information structure. We focus on the impact of urban network structure on the incentive of information sharing. Our analyses suggest that the garages are always better off in a circular-networked city, while they could be worse off in the suburbs of a star-networked city. Nevertheless, the overall revenue for garages is improved and the aggregate congestion is reduced under information sharing. Our results also suggest that information sharing helps garages further exploit the customers who in turn become worse-off. Therefore, policy-makers should carefully evaluate their transportation data policy since impacts on the service-providers and the customers are typically conflicting. Using the SFpark data, we empirically confirmed the value of information sharing. In particular, garages with higher price-demand elasticity and lower demand variance tend to enjoy larger benefits via information sharing. These insights support the joint design of parking rates structure and information systems. [ABSTRACT FROM AUTHOR]