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.     

Forecasting the diffusion of photovoltaic systems in southern Europe : A learning curve approach

Most analysts disagree upon whether photovoltaic systems (PV) will be able to play an important role in the energy scenarios of the future. A few scholars also question the appropriateness of policies that envisage the use of public subsidies to stimulate the growth of this industry and to accelerate market penetration. This paper contributes to this debate by examining whether carefully designed policies may initiate a process of large-scale diffusion of grid-connected PV, even without the deployment of external subsidies. Building upon a disaggregated characterization of the electricity market, it takes endogenously into account the learning curve phenomenon and simulates the diffusion of PV building-integrated systems in five European countries. The analysis is restricted to crystalline silicon systems and is repeated under four different macroeconomic scenarios corresponding to four different energy policies. The results suggest that already today there are opportunities for PV diffusion in many islands of the Mediterranean region, which may trigger sufficient scale economies to render the technology competitive in larger markets. They also show that the diffusion process could be accelerated through the implementation of carbon-tax policies that support initial penetration. The environmental benefits (net avoided CO₂ emissions over the system life cycle) associated with the forecasted penetration are also evaluated.     

An empirical investigation of the roles of prior knowledge and learning activities in technology transfer

This study analyzes 120 university–industry technology transfer projects. A significant positive relationship was found between the learning activities performed by the firm during the development and implementation stages of the technology transfer project and the benefits to that firm from the project. In contrast, prior knowledge of the firm about the existing technology was found to have only a marginal contribution to the project benefits. However, further exploratory analysis based on high and low levels of technical and organizational uncertainty revealed more provocative relationships.     

The role of capital investment project teams in organisational learning

The management and organisation of capital projects in the British National Health Service (NHS) is dependent upon project teams. An analysis of four case studies shows how these teams also act as agents of learning for individuals and the organisation. This article considers the process by which learning came about. In particular it identifies the ability of the project teams to develop a parallel organisation within a wider organisational context. These teams develop specific rules, roles and relationships which help individual project team members to more effectively share their knowledge with others and their organisation.     

Technological change in energy systems: Learning curves, logistic curves and input-output coefficients

Learning curves have recently been widely adopted in climate-economy models to incorporate endogenous change of energy technologies, replacing the conventional assumption of an autonomous energy efficiency improvement. However, there has been little consideration of the credibility of the learning curve. The current trend that many important energy and climate change policy analyses rely on the learning curve means that it is of great importance to critically examine the basis for learning curves. Here, we analyse the use of learning curves in energy technology, usually implemented as a simple power function. We find that the learning curve cannot separate the effects of price and technological change, cannot reflect continuous and qualitative change of both conventional and emerging energy technologies, cannot help to determine the time paths of technological investment, and misses the central role of R&D activity in driving technological change. We argue that a logistic curve of improving performance modified to include R&D activity as a driving variable can better describe the cost reductions in energy technologies. Furthermore, we demonstrate that the top-down Leontief technology can incorporate the bottom-up technologies that improve along either the learning curve or the logistic curve, through changing input-output coefficients. An application to UK wind power illustrates that the logistic curve fits the observed data better and implies greater potential for cost reduction than the learning curve does.     

Learning by doing,spillovers and shakeouts

This paper studies industry evolution driven by non strategic learning by doing and spillovers. We characterize a dynamic process of cost and output changes and its effect on welfare and industry profits. The paper gives conditions for shakeouts to occur and analyzes the key factors affecting these conditions. Since shakeouts could lead to a long-run social loss due to higher market concentration, there is a role for a government to play in limiting unnecessary shakeouts. The most effective way to do so is to enhance spillovers.JEL Classification: L11, L13, O31Correspondence to: Michael TroegeWe would like to thank Hans Mewis, Christophe Moussu and an anonymous referee for valuable comments and suggestions. We also benefited from comments of seminar participants at WZB, Humboldt University, Northwestern University and the EEA/ESEM 1999 meetings. Part of the research was carried out while Michael Tröge was visiting Northwestern University. Financial support by the German Research Council (DFG) is gratefully acknowledged.     

Selection dynamics and adaptive behavior without much information

This paper uses the experimental method to investigate behavior in a coordination game when the information available to subjects is limited to their feasible choices and their experienced payoffs. In the experiment subjects converge to an absorbing state at rates that are orders of magnitude faster than reinforcement learning algorithms, but slower than under complete information. This state is very close to a mutual best response outcome. All cohorts converged to the market statistic predicted by the interior equilibrium regardless of the information conditions or the stability conditions. Eric Battalio programmed the graphical user interface. The National Science Foundation and Texas Advanced Research Program provided financial support. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation or the Texas Advanced Research Program.     

中国企业的技术选择

本文以“企业技术能力”研究范式为基础 ,运用技术学习战略决定的数理模型 ,从理论角度剖析了现实企业选择技术学习战略的动因。并以案例为实证 ,探究了中国企业技术选择的行为机理 ,提出了相关对策。     

Strategic trading and learning about liquidity

Many practitioners point out that the speculative profits of institutional traders are eroded by the difficulty in gauging the price impact of their trades. In this paper, we develop a model of strategic trading where speculators face such a dilemma because of incomplete information about time-varying market liquidity. Unlike the competitive market makers that they trade against, informed traders do not know the distribution of liquidity (“noise”) trades. Instead, they have to learn about liquidity from past prices and trading volume. This learning implies that strategic trades and market statistics such as informational efficiency are path-dependent on past market outcomes. Our paper also has normative implications for practitioners.     

Collectivist values for learning in organizational relationships in China: the role of trust and vertical coordination

Partners must engage in integrative interaction in order to combine diverse expertise and experiences into effective learning. Results from 103 pairs of customer and supplier organizations in China indicate that trust and vertical coordination are useful ways to characterize this integrative interaction and together they promote learning. Structural equation analysis suggests that collectivist but not individualist values are important foundations for integrative interaction between partners that result in learning. These findings were interpreted as reaffirming the value of effective relationships for coordination between partners and suggesting that collectivist values can be a source of effective organizational relationships.