For years, violence against doctors and healthcare workers has been a growing social issue in China. In a recent series of studies, we provided evidence for a motivated scapegoating account of this violence. Specifically, individuals who feel that the course of their (or their family member's) illness is a threat to their sense of control are more likely to express motivation to aggress against healthcare providers. Drawing on existential theory, we propose that blaming and aggressing against a single individual represents a culturally afforded scapegoating mechanism in China. However, in an era of healthcare crisis (i.e., the global COVID-19 pandemic), it is essential to understand cultural variation in scapegoating in the context of healthcare. We therefore undertook two cross-cultural studies examining how people in the United States and China use different scapegoating responses to re-assert a sense of control during medical uncertainty. One study was conducted prior to the pandemic and allowed us to make an initial validating and exploratory investigation of the constructs of interest. The second study, conducted during the pandemic, was confirmatory and investigated mediation path models. Across the two studies, consistent evidence emerged that, both in response to COVID-related and non-COVID-related illness scenarios, Chinese (relative to U.S.) individuals are more likely to respond by aggressing against an individual doctor, while U.S. (relative to Chinese) individuals are more likely to respond by scapegoating the medical industry/system. Further, Study 2 suggests these culture effects are mediated by differential patterns of primary and secondary control-seeking.
The current data explosion poses great challenges to approximate aggregation with high efficiency and accuracy. To address this problem, we propose a novel approach to calculate the aggregation answers with a high accuracy using only a small portion of the data. We introduce leverages to reflect individual differences in the data from a statistical perspective. Two kinds of estimators, the leverage-based estimator, and the sketch estimator (a "rough picture" of the aggregation answer), are in constraint relations and iteratively improved according to the actual conditions until their difference is below a threshold. Due to the iteration mechanism and the leverages, our approach achieves a high accuracy. Moreover, some features, such as not requiring recording the sampled data and easy to extend to various execution modes, such as the online mode, make our approach well suited to deal with big data. Experiments show that our approach has an extraordinary performance, and when compared with the uniform sampling, our approach can achieve high-quality answers with only 1/3 sample size.
Existing offline reinforcement learning (RL) methods face a few major challenges, particularly the distributional shift between the learned policy and the behavior policy. Offline Meta-RL is emerging as a promising approach to address these challenges, aiming to learn an informative meta-policy from a collection of tasks. Nevertheless, as shown in our empirical studies, offline Meta-RL could be outperformed by offline single-task RL methods on tasks with good quality of datasets, indicating that a right balance has to be delicately calibrated between "exploring" the out-of-distribution state-actions by following the meta-policy and "exploiting" the offline dataset by staying close to the behavior policy. Motivated by such empirical analysis, we explore model-based offline Meta-RL with regularized Policy Optimization (MerPO), which learns a meta-model for efficient task structure inference and an informative meta-policy for safe exploration of out-of-distribution state-actions. In particular, we devise a new meta-Regularized model-based Actor-Critic (RAC) method for within-task policy optimization, as a key building block of MerPO, using conservative policy evaluation and regularized policy improvement; and the intrinsic tradeoff therein is achieved via striking the right balance between two regularizers, one based on the behavior policy and the other on the meta-policy. We theoretically show that the learnt policy offers guaranteed improvement over both the behavior policy and the meta-policy, thus ensuring the performance improvement on new tasks via offline Meta-RL. Experiments corroborate the superior performance of MerPO over existing offline Meta-RL methods.
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