When economics and machine learning collide, what kind of spark will be produced?

Artificial intelligence can be said to be the master of automation in the past 200 years. In recent years, it has been developing rapidly, providing high precision and accuracy while maintaining productivity. < p > < p > machine learning has been widely used in various fields of data science and automation, but not in economics. The purpose of this paper is to introduce the application of machine learning in current economic structure and its future possibilities. Econometrics refers to the application of statistical methods to economic data in order to provide empirical content for economic relations. More precisely, it is “on the basis of simultaneous development of theory and observation, quantitative analysis of actual economic phenomena through appropriate reasoning methods”. An introductory economics textbook describes econometrics as allowing economists to “sift simple relationships from a mountain of data.”. Behavioral economics studies the influence of psychological, cognitive, emotional, cultural and social factors on individual and institutional decision-making, as well as the differences between these decisions and the contents of classical economic theories. However, machine learning enables economists to process larger data sets faster to solve big problems. < p > < p > in economic forecasting, we usually hope to forecast the GDP of the future economy by applying indicators such as interest rate, retail sales and unemployment rate to the statistical model used. When using machine learning in the same scenario, take a look at this result: < / P > < p > Hugh dans and John hawksworsley built a real-time analysis model using a machine learning technique called elastic net regularization and variable selection. Although it still relies heavily on the input of human experts, it can achieve an accuracy rate of about 95% in predicting GDP growth. < / P > < p > machine learning models can analyze hundreds of millions of bytes while minimizing external interference. Unlike standard econometric models, they are based on causal reasoning to analyze data. Machine learning models are not designed to determine the causal relationship between variables, but to make reasonable predictions. These models have advantages and disadvantages. Banu Pratap and shovon sengupta, economists at the Central Bank of India, hope to find ways to improve macroeconomic forecasts in machine learning. They compared the machine learning model with the traditional model, and finally found that the machine learning model produced better results. After all the comparisons between machine learning model and econometric model, the question may arise: “does this mean that the two frameworks cannot work together?” The answer is no, it is necessary to implement machine learning and Econometrics in the same project. As machine learning applications become more proficient in granular prediction, their developers will face the problem of causality. Therefore, the coordination of econometrics in machine learning system enables machine learning developers to understand what drives the prediction success of their models. The powerful pattern recognition ability of machine learning makes it widely used in behavioral economics. Machine learning plays an outstanding role in predicting future behavior using existing data. < p > < p > human beings are pattern explorers. In the field of behavioral economics, if we can make good use of machine learning applications, we will be able to predict people’s decisions. < / P > < p > the following figure is a decision tree model, which is a branch of machine learning algorithm, including the observation of the project to the final result of finding the target value of the project. The goal of this algorithm is to develop a model that can predict decisions based on input variables. < / P > < p > we will not go into the specific steps behind this algorithm, but what we can get is that these manufacturers can formulate marketing strategies based on this model and bring benefits to their business, which will benefit the overall economy. Many of the things we do with machine learning are done under the surface. Machine learning driven algorithm for demand forecasting, product search ranking, product and transaction suggestions, commodity sales arrangement, fraud detection, translation, etc. While less obvious, most of the impact of machine learning will be of this type – quietly but meaningfully improving core operations. < / P > < p > data collection and storage is becoming cheaper and more efficient. With the help of machine learning, manufacturers can keep the same quality while reducing manufacturing costs. The basic goal of manufacturing industry is to produce high quality products at the lowest cost. In the manufacturing process, machine learning algorithm obtains information from the manufacturing layer, and manufacturing data is used to describe the synchronization between the machine and the production speed. One of the biggest advantages of AI and machine learning is that it provides more flexibility in the industry. < / P > < p > there is a shampoo factory in southern Germany with only one production line, but its production function is realized by receiving orders online. After receiving the order, it attaches a custom RFID tag to the bottle and uses sensors on the manufacturing machine to add different components. This not only greatly shortens the production time, but also customizes the products completely according to the needs of consumers. According to PricewaterhouseCoopers, machine learning in economics can increase productivity by 14.3% by 2030. The potential of machine learning is infinite. It has made contributions to various fields and added additional benefits to the current industry. With the development of artificial intelligence, I believe that the combination of existing economic model and machine learning model will open a new door for economists. 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