This shift helps students see that economics is a set of tools that can empower them, providing insight that will guide them toward better decisions. I've adopted this perspective in the introductory classes I teach at the University of Michigan. My students describe a sense of wonder at discovering that their daily decisions -- how much to spend, how many hours to work, or how to allocate household tasks -- are the stuff that economists study. Take the theory of comparative advantage, which is a critical concept in international trade. It is traditionally taught as a series of calculations that can explain why England produces cloth that it trades for Portugal's wine.
You want to do a true division and obtain a result 0.5. But by this code you will get a result of 0, a round integer of the true result. Because by assigning 10 to a, a is declare to be an integer(b also), thus the result is of course an integer. You have to assign 10.0 to a( 20.0 to b) so as to obtain a floating point number result. In Java you don't need to worry about this, because you will always be very clear of the data type.
The Lasso is a cornerstone of modern multivariate data analysis, yet its performance suffers in the common situation in which covariates are correlated. A direct comparison of these and similar Lasso-style algorithms to the original Lasso is difficult because the performance of all of these methods depends critically on an auxiliary penalty parameter $\lambda$. In this paper we propose an agnostic, theoretical framework for comparing Preconditioned Lasso algorithms to the Lasso without having to choose $\lambda$. We apply our framework to three Preconditioned Lasso instances and highlight when they will outperform the Lasso. Additionally, our theory offers insights into the fragilities of these algorithms to which we provide partial solutions.
Multi-Agent Systems (MAS) promise to offer solutions to problems where established, older paradigms fall short. In order to validate such claims that are repeatedly made in software agent publications, empirical in-depth studies of advantages and weaknesses of multi-agent solutions versus conventional ones in practical applications are needed. Climate control in large buildings is one application area where multi-agent systems, and market-oriented programming in particular, have been reported to be very successful, although central control solutions are still the standard practice. We have therefore constructed and implemented a variety of market designs for this problem, as well as different standard control engineering solutions. This article gives a detailed analysis and comparison, so as to learn about differences between standard versus agent approaches, and yielding new insights about benefits and limitations of computational markets. An important outcome is that local information plus market communication produces global control''.
Increasing volume of Electronic Health Records (EHR) in recent years provides great opportunities for data scientists to collaborate on different aspects of healthcare research by applying advanced analytics to these EHR clinical data. A key requirement however is obtaining meaningful insights from high dimensional, sparse and complex clinical data. Data science approaches typically address this challenge by performing feature learning in order to build more reliable and informative feature representations from clinical data followed by supervised learning. In this paper, we propose a predictive modeling approach based on deep learning based feature representations and word embedding techniques. Our method uses different deep architectures (stacked sparse autoencoders, deep belief network, adversarial autoencoders and variational autoencoders) for feature representation in higher-level abstraction to obtain effective and robust features from EHRs, and then build prediction models on top of them. Our approach is particularly useful when the unlabeled data is abundant whereas labeled data is scarce. We investigate the performance of representation learning through a supervised learning approach. Our focus is to present a comparative study to evaluate the performance of different deep architectures through supervised learning and provide insights in the choice of deep feature representation techniques. Our experiments demonstrate that for small data sets, stacked sparse autoencoder demonstrates a superior generality performance in prediction due to sparsity regularization whereas variational autoencoders outperform the competing approaches for large data sets due to its capability of learning the representation distribution