Question

a)describe the purpose of an economy and how one can assess how well any economy is fulfilling that purpose?

b)Building on your answer in (a), explain the specific confusion that is described in the CNN article since all the economic indices named are on the rise?.

Answer 1

Part (a)The purpose of economy is to manage the household; to produce and distribute food, water and other needs and goods primarily for preserving the human life or in other words we can say The purpose of the economy should be to distribute the earth’s resources such as food, water, land, minerals etc among human beings in a sustainable manner. Since every human is equal and can claim his/her rights for some or all of the resources how do we collectively decide who gets what?

Economy fulfill the purpose b y focusing on the supply, demand, and allocation of the Earth’s natural resources. It’s goal is to gain a better understanding of the role of natural resources in the economy. Learning about the role of natural resources allows for the development of more sustainable methods to manage resources and make sure that they are maintained for future generations.The goal of natural resource economics is to develop an efficient economy that is sustainable in the long-run.

Part (b) (a) the specific issue in CNN , it is often encountered that the decision boundaries of some image categories are ambiguous and easy to confuse with each other, thus yielding inferior accuracy on image classification. In this paper, a novel confusion-aware convolutional neural network (CNN) is proposed to address this issue. Different from the coarse-to-fine strategy that has been practiced in existing hierarchical classifiers, our proposed method performs predict-then-correct strategy. At the training stage, a conventional classifier (referred to as the prediction classifier) is trained, and its confusion matrix is estimated by exploiting a cross validation process conducted on the training set. Based on this estimated confusion matrix, a confusion-aware model is then established, and it is used as a decision maker to train a set of correction classifiers for those confusing categories. At the classifying stage, the prediction and correction classifiers collaboratively work together via a hierarchical structure, and the confusion-aware model is used again as a decision maker to select a proper prediction classifier for each confusing category. Experimental results conducted on the Mnist and CIFAR-10 datasets show that the proposed confusion-aware network outperforms the existing CNN classifiers.