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.     

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。     

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。     

评价指标体系在经济责任审计中的应用

将评价指标体系应用于经济责任审计可避免审计的随意性和不同审计人员审计的不一致性,本文以高等院校为主体从合法合规性、效益性、目标性等方面设计了经济责任评价指标体系。     

Social learning with private and common values

Summary. We consider an environment where individuals sequentially choose among several actions. The payoff to an individual depends on her action choice, the state of the world, and an idiosyncratic, privately observed preference shock. Under weak conditions, as the number of individuals increases, the sequence of choices always reveals the state of the world. This contrasts with the familiar result for pure common-value environments where the state is never learned, resulting in herds or informational cascades. The medium run dynamics to convergence can be very complex and non-monotone: posterior beliefs may be concentrated on a wrong state for a long time, shifting suddenly to the correct state.Received: 6 January 2005, Revised: 5 May 2005, JEL Classification Numbers: C72, D82.Jacob K. Goeree: Correspondence toFinancial support from the National Science Foundation NSF (SBR-0098400 and SES-0079301) and the Alfred P. Sloan Foundation is gratefully acknowledged. We thank Richard McKelvey posthumously for insights and conjectures about information aggregation that helped shape our thinking about the problem. We also acknowledge helpful comments from Kim Border, Tilman Börgers, Bogachen Celen, Luis Corchon, Matthew Jackson and seminar participants at University College London, the University of Arizona, Universitat Autonoma de Barcelona, the California Institute of Technology, the 2003 annual meeting of ESA in Pittsburgh, the 2003 Malaga Workshop on Social Choice and Welfare Economics, the 2003 SAET meetings in Rhodos, and the 2003 ESSET meetings in Gerzensee.     

基于互惠合作的供应链合作关系稳定机制研究

首先应用强化学习算法对供应链合作关系的稳定机制进行研究;其次从生物学的角度研究了供应链合作关系中的互惠利他合作行为;最后利用计算机进行了系统仿真.研究得出：只要供应链合作关系中存在互惠利他型的企业,不管其所占的比重有多大,该企业都会通过强化学习使得互惠利他行为在供应链中扩散开来,最终达到企业间合作的一种稳定状态.