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The hospital system is a complex service system in which patients and hospitals interact and make their decisions based on bounded rationality and information. In this paper, we develop a generative agent-based model to simulate the behavior of a hospital service system. Our model combines agent-based simulation and queueing models to mimic the hospital service processes. Our goal is to simulate and understand the growth and size distribution of hospitals. This simulation model includes agents for supply elements (i.e., hospitals with different resources and expansion strategies) and demand elements (i.e., patients with different preferences for their hospital selections) in a hospital service system. Three important questions are investigated: 1) what is the emergent size distribution of hospitals? 2) what key factors influence the size distribution? and 3) how sensitive is the size distribution to these key factors? Simulation results show that the size distribution is neither power law nor lognormal. Rather, the distribution is leptokurtic, and more skewed than normal but less skewed than lognormal. This result contradicts those in extant literature on the size distributions of human and natural systems, including cities, firms, power-grids, and citation network. We conduct a set of experiments to identify the generative mechanisms for the hospital size distribution and to test the robustness of the results. The model was validated empirically by using a U.S. hospital size dataset. [ABSTRACT FROM AUTHOR]

The successful management of crowdsourcing risk has gained considerable attention because of the challenges involved in obtaining the satisfactory performance from crowdsourcing activities. However, the mechanism of how various critical risk factors affect crowdsourcing performance has been poorly investigated. Empirical evidence based on a dataset of 169 samples from China shows that three technical (i.e., complexity, requirement, and task) and three social (i.e., seeker, solver, and relationship) subsystem risks not only directly but also interactively influence the crowdsourcing task performance. Technical subsystem risk factors, while worsen performance, diminish the negative effects of social subsystem risk factors on the crowdsourcing task performance. The diminishing effects of three technical subsystem risk factors are also differentiated. High requirement and task risks lessen the negative effects of seeker, solver, and relationship risks on the task performance, whereas the high complexity risk only weakens the negative effect of the solver risk. Therefore, various crowdsourcing risk factors affect the performance in different ways and directions (either negatively or positively). Risk can perform dual roles in influencing the performance. Hybrid strategies that integrate social and technical subsystem elements may be effective in achieving a superior task performance for the crowdsourcing risk management. Managers of crowdsourcing platforms can also optimize social and technical environments to improve the fit between social and technical systems. [ABSTRACT FROM AUTHOR]