Meng Li - US grants

This dissertation research examines the decision processes underlying how people value lives saved in situations of resource scarcity. Three policies a person could use are examined: (1) treating all lives are equal, (2) prioritizing people who will gain the most benefit (e.g. additional life years) from an intervention, and (3) prioritize young people regardless of the number of years they have left to live. These metrics imply different strategies for health resource allocation, especially when such resources are scarce. Vaccination scenarios are used to probe which metrics lay people use in different situations and how the type of question influences the metric used. In direct questions, people are asked about their abstract principles (e.g., all lives are equal, prioritize the young, etc.). In indirect questions, people are given an allocation problem (e.g., there are 1000 people at risk but only 500 vaccines; who should get the vaccines?). The co-PI will test different psychological accounts for why people might express different metrics in these two types of questions.
The broader impacts of this research derive from the fact that the public's support for health policies may be malleable: While the pro-young tendencies may drive support for specific policies for how to prioritize scarce health resources (i.e. the 2009 H1N1 vaccine was prioritized for people under age 25), they depart from the oft-cited moral standard that "all lives are equal". Such tendencies may be concealed in more direct measures, such as in questions directly asking whether lives of young people are more valuable than those of older people, because answering yes in this case is a more apparent contradiction to the deep-rooted "all lives equal" moral standard. Studying these inconsistencies provides important information on how to design public health policies and how to present them to the public.

Healthcare resources can be described either in terms of money, or in terms of health outcomes. Yet, past research suggests that people tend to use principles consistent with market norms (e.g., efficiency) to allocate money, but are reluctant to do so for
"sacred values" such as health and lives. This research examines how people's allocation preference for healthcare resources can shift, depending on whether allocation policies are framed in terms of financial cost or health outcomes. It also explores whether people apply different allocation strategies to different types of healthcare resources based on how scarce they perceive the particular resource to be.

The United States faces an eminent need to curb healthcare spending. However, there is no obvious consensus among members of the public on how to allocate the limited healthcare resources, nor is there agreement about how to balance efficiency versus equality in such allocations. The proposed research untangles public opinions on healthcare allocation by examining the influence of policy framing and perceived scarcity. This research studies large scale national probaility samples of American adults and will provide rich data on public opinions for a critical health policy issue; the distribution of limited healthcare resources. It will also provide guidance to policymakers for how to design different allocation policies for different healthcare resources, as well as how to frame allocation policies to gain the most public support for proposed policies.

Rice University researchers engaged in groundbreaking data-intensive science and engineering increasingly depend on access to real-time data analysis facilities required for their research. These research activities include image processing, computer vision, and machine learning, spanning multiple fields, such as geological sciences, statistics, computer science, and physics. Each of these problems areas or use cases can be addressed by shared computational infrastructure leveraging GPU accelerators for interactive computing. The system provides a significant resource for enabling science but also for educating the next generation of computational scientists in the latest GPU-computing techniques through the outreach of the Center for Research Computing.

The resource includes nine compute nodes, each with 40 cores, 384GB RAM, 4TB NVMe storage, and 8 NVIDIA Quadro RTX 6000 GPUs. The systems are interconnected via high-performance networking and hosted on a Science DMZ integrating them with the Open Science Grid as well as commercial cloud allowing both increased utilization as part of national OSG efforts and the ability to utilize cloud resources for load bursting. The system leverages an open-source software stack designed to support containerization, enabling each researcher to utilize their own unique set of software and toolkits while sharing common hardware and a common cloud access platform. Moreover, the infrastructure is part of a larger technology ecosystem that leverages federated identity and access management as part of InCommon, advanced networking with science DMZ, and Information Security Office that supports not only university data and technology security but includes targeted outreach for research data and protocol security.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.