Prediction of the Indian summer monsoon using a stacked autoencoder and ensemble regression model

The study of climatic variables that govern the Indian summer monsoon has been widely explored. In this work, we use a non-linear deep learning-based feature reduction scheme for the discovery of skilful predictors for monsoon rainfall with climatic variables from various regions of the globe. We use a stacked autoencoder network along with two advanced machine learning techniques to forecast the Indian summer monsoon. We show that the predictors such as the sea surface temperature and zonal wind can predict the Indian summer monsoon one month ahead, whereas the sea level pressure can predict ten months before the season. Further, we also show that the predictors derived from a combination of climatic variables can outperform the predictors derived from an individual variable. The stacked autoencoder model with combined predictors of sea surface temperature and sea level pressure can predict the monsoon (June-September) two months ahead with a 2.8% error. The accuracy of the identified predictors is found to be superior to the state-of-the-art predictions of the Indian monsoon.     

Experimental internet auctions with random information retrieval

We run an experiment where 97 subjects could retrieve records of completed past auctions before placing their bids in current one-bid, two-bid, and auction-selection games. Each subject was asked to participate in 3 current auctions; but could retrieve up to 60 records of completed (past) auctions. The results reveal a positive relation between the payoffs earned by the subjects and their history-inspection effort. Subjects act as if responding to the average bidding-ratios of the winners in the samples that they have retrieved. They apply intuitive signal-dependent stopping rules like “sample until observing a winner-value close to my won” or “find a close winner-value and try one more history” when sampling the databases. History-inspection directs bidders with relatively high private-valuations to moderate bidding which increases their realized payoffs. (JEL C9 D4 D8) Electronic Supplementary Material Supplementary material is available in the online version of this article at . JEL Classification C93, D44, D83     

超越契约理论--演化论视角的企业理论研究

企业的契约理论围绕交易成本概念解释了企业的存在、边界和内部组织,但严重忽视了企业的技术和生产功能,演化经济学的企业理论通过研究企业内知识的产生、传播和利用过程,以及组织与个人的交互学习,不仅重新解释了企业的存在、边界和内部组织问题,还研究了契约理论未触及的企业异质性、竞争优势和企业家等问题.     

"上证"A股市场情绪的关键影响因素

文章从投资者认知偏误视角,研究提出了"上证"A股市场情绪的四维量表,系统整合了18个关键影响因素,形成中国证券市场情绪的关键影响因素概念模型.采用"上证"A股市场中152家企业的调研数据,进行验证性因子分析和拟合优度检验.结果表明,12个理论因素按预期模式加载,形成了认知偏误、有限套利、交互传染和本土特性等四个维度,对认识中国证券投资者的决策行为具有一定指导意义.     

技术学习——企业技术能力演进的分析框架

企业技术能力的演进是一个技术学习过程,本文对技术能力和技术学习的基本内涵进行了分析和探讨,并提出了一个企业技术能力发展决定因素和机制的分析框架。我们认为,为获取技术能力发展所需要的知识和技能,技术学习需要整合三个维度的知识学习:个人学习与组织学习,内部知识学习与外部知识学习,以及显性知识与隐性知识的学习。因此,企业技术学习应该包含以下四个子过程:外部知识获取、内部知识获取、知识编码化以及知识社会化。由于这四个过程所获取的知识来源,性质不同,为完成这些子过程所需要的资源和机制也各不相同。通过对各个子学习过程决定因素和机制的分析识别,综合起来,我们为分析企业技术能力获取的决定因素和内部机制提供了一个基本理论框架。     

核心-边缘理论在区域旅游规划中的运用

核心－边缘理论为区域旅游规划提供了建构区域旅游空间结构系统的认知模型。运用这种空间结构模型，在进行旅游资源的区域整合，景区土地利用功能配置与都会城市旅游圈层构造，以及促进区域旅游联动发展方面可取得满意的实践成果，核心与边缘地区应该是一种平等竞争，优势互补，合作互嬴的空间关系，发展核心，带动边缘，是区域旅游发展的重要战略兴措，发展中地区要注意培育旅游核心区，形成旅游创新活动基地，带动边缘区域发展，壮在整个区域的旅游竞争力。     

The convergence of least squares learning in stochastic temporary equilibrium models

Summary. This paper provides conditions for the almost sure convergence of the least squares learning rule in a stochastic temporary equilibrium model, where regressions are performed on the past values of the endogenous state variable. In contrast to earlier studies, (Evans and Honkapohja, 1998; Marcent and Sargent, 1989), which were local analyses, the dynamics are studied from a global viewpoint, which allows one to obtain an almost sure convergence result without employing projection facilities. Received: April 7, 2001; revised version: September 5, 2001     

Cost Uncertainty and Unilateral Abatement

Unilateral abatement is sometimes advocated in order to set a good example that will make other countries follow. The aim of this paper is to investigate whether existence of correlated cost uncertainty provides an incentive for a country to undertake unilateral abatement. The theoretical model is driven by two main mechanisms; first, a learning effect, as the follower country might reduce its risk premium as it can observe the cost level in the leader country. Second, there is the public good effect, i.e., the marginal benefit of abatement declines when abatement is a public good and other countries contribute to pollution reductions. Results shows that unilateral abatement would be efficient in reducing uncertainty about the unit costs of abatement if a country with low cost uncertainty would undertake abatement first, while a country with initially high cost uncertainty would follow. However, countries may prefer to act simultaneously because of the larger uncertainties that are inherent in a sequential game.      

关于非英语专业高职学生学习自主性问题的探讨

本文通过对某高职院校非英语专业学生英语学习的自主性、元认知、兴趣、动机、自信度、焦虑度测试的分析研究表明:英语学习的自主性和性别及英语的学习成绩、兴趣、动机、自信度有非常显著的正相关性,而和英语学习元认知没有相关性,同时和英语学习焦虑度有非常显著的负相关性,从而为构建有效的培养英语学习自主性的教学模式提供依据,并就此提出若干提高学生英语学习自主性的建议。     

供应链分销系统奖金优化与仿真分析

研究由多个制造商与一个零售商组成的分销系统，他们以各自的利润最大化为目标，制造商给零售商提供奖金激励，零售商提供对应于奖金激励的服务水平，制造商需要进行为零售商提供多大奖金激励的决策。利用强化学习的启发式学习算法来优化制造商应提供的最优奖金激励。