论文标题
部分可观测时空混沌系统的无模型预测
Bad Weather, Social Network, and Internal Migration; Case of Japanese Sumo Wrestlers 1946-1985
论文作者
论文摘要
储层计算是预测湍流的有力工具，其简单的架构具有处理大型系统的计算效率。然而，其实现通常需要完整的状态向量测量和系统非线性知识。我们使用非线性投影函数将系统测量扩展到高维空间，然后将其输入到储层中以获得预测。我们展示了这种储层计算网络在时空混沌系统上的应用，该系统模拟了湍流的若干特征。我们表明，使用径向基函数作为非线性投影器，即使只有部分观测并且不知道控制方程，也能稳健地捕捉复杂的系统非线性。最后，我们表明，当测量稀疏、不完整且带有噪声，甚至控制方程变得不准确时，我们的网络仍然可以产生相当准确的预测，从而为实际湍流系统的无模型预测铺平了道路。
Post-World War II , there was massive internal migration from rural to urban areas in Japan. The location of Sumo stables was concentrated in Tokyo. Hence, supply of Sumo wrestlers from rural areas to Tokyo was considered as migration. Using a panel dataset covering forty years, specifically 1946-1985, this study investigates how weather conditions and social networks influenced the labor supply of Sumo wrestlers. Major findings are; (1) inclemency of the weather in local areas increased supply of Sumo wrestlers in the period 1946-1965, (2) the effect of the bad weather conditions is greater in the locality where large number of Sumo wrestlers were supplied in the pre-war period, (3) neither the occurrence of bad weather conditions nor their interactions with sumo-wrestlers influenced the supply of Sumo wrestlers in the period 1966-1985. These findings imply that the negative shock of bad weather conditions on agriculture in the rural areas incentivized young individuals to be apprenticed in Sumo stables in Tokyo. Additionally, in such situations, the social networks within Sumo wrestler communities from the same locality are important. However, once the share of workers in agricultural sectors became very low, this mechanism did not work.
