No abstract

Driven by rapid digitization and expansive internet access, market-driven platforms (e.g., Amazon, DoorDash, Uber, TaskRabbit) are increasingly prevalent and becoming key drivers of the economy. Across many industries, platforms leverage digital infrastructure to efficiently match producers and consumers, dynamically set prices, and enable economies of scale. This increasing prominence makes it important to understand the behavior of platforms, which induces complex phenomenon especially in the presence of severe market shocks (e.g., during pandemics). In this work, we develop a multi-agent simulation environment to capture key elements of a platform economy, including the kinds of economic shocks that disrupt a traditional, off-platform market. We use deep reinforcement learning (RL) to model the pricing and matching behavior of a platform that optimizes for revenue and various socially-aware objectives. We start with tractable motivating examples to establish intuitions about the dynamics and function of optimal platform policies. We then conduct extensive empirical simulations on multi-period environments, including settings with market shocks. We characterize the effect of a platform on the efficiency and resilience of an economic system under different platform design objectives. We further analyze the consequences of regulation fixing platform fees, and study the alignment of a revenue-maximizing platform with social welfare under different platform matching policies. As such, our RL-based framework provides a foundation for understanding platform economies under different designs and for yielding new economic insights that are beyond analytical tractability.

Objective: This study examined the utility of the Chinese-language translations of the word list memory test (Philadelphia Verbal Learning Test) and story memory test (Logical Memory subtest of the Wechsler Memory Scale) for differentiating cognitive diagnosis in older U.S. Chinese immigrants. Method: Participants were ≥60 years old, with Chinese language proficiency to complete a diagnostic workup at the Mount Sinai’s Alzheimer’s Disease Research Center. The workup included an evaluation by a geriatric psychiatrist and cognitive testing with a psychometrician. Diagnosis of normal, mild cognitive impairment (MCI), and dementia was made independent of the cognitive tests at consensus led by a dementia expert physician. Multivariable logistic regression models were used to assess the sensitivity of story and word list memory tests for distinguishing between groups. Receiver operating characteristic (ROC area/area under the curve [AUC]) was used to compare the predictive accuracy of the two tests. Results: The sample included 71 participants with normal cognition, 42 with MCI, and 24 with dementia. The MCI group was older and less educated than normal controls but younger and more educated than the dementia group. Delayed recall of both memory tests, but not immediate recall of either test, predicted diagnosis. While composite memory score of word list (AUC = 0.90) predicted diagnosis slightly better than that of stories (AUC = 0.85), the difference was not significant in this small sample (p = .14). Conclusions: Chinese-language translations of verbal memory tests, in particular delayed recall scores, were equally sensitive for classifying cognitive diagnosis in older U.S. Chinese immigrants.