DATA SCIENCE AND AI - EMERGING TOOLS FOR ECONOMICS
Economics for long and even today for most, is a Social Science and do not constitute true “sciences”, such as physics or mathematics. In other words, there exists an apparent distinction between natural sciences and social disciplines that makes conventional scientific method inapplicable in social disciplines. But the fact is, unlike physics, economics deals with people, their perceptions and irrationalities. A model that worked one day will not work the next time because of the rapidly changing moods of players. That is exactly where Data Analysis steps in to evaluate the Big data set to study the perceptions of the population on which the modern Economics stands. In this context Adam Smith, considered as "the father of economics," in his five-book series of 1776 ‘An Inquiry Into the Nature and Causes of the Wealth of nations' presents his theory that nations attain wealth and operate best when people are free to use their skills and capital in their own self-interest.
Economics may be better defined as a social science concerned with the production, distribution, and consumption of goods and services. It studies how individuals, businesses, governments, and nations make choices on allocating resources to satisfy their wants and needs, trying to determine how these groups should organize and coordinate efforts to achieve maximum output.
Most simply put, economics is the analysis of how people use the resources available to them, those resources include the time and talent people have available; the land, buildings, equipment and other tools on hand; and the knowledge of how to combine them to create products and services.This is aptly contributed by Data Analytics and Machine Learning as subsets of Artificial Intelligence in Economics.
History has shown that, when it comes to predicting economic conditions, the only certainty is uncertainty. Economists are terrible at forecasting. Prakash Loungani’s (IMF) analysis, for instance, revealed that economists had failed to predict 148 of the past 150 recessions. There are many impediments to efficient economic forecasting. One is that it is almost impossible to predict large swings in the moods of consumers. AI algorithms can also analyze how media headlines influence sentiments about the economy. In fact, JP Morgan already uses an algorithm that tracks the effects of President Trump’s tweets on financial markets. Central banks and fiscal authorities, by knowing when a recession hits, will be more effective and rapid in enacting monetary and fiscal tools, thereby mitigating the effects of business cycles. We might even predict changes in supply and demand to implement necessary changes in order to avoid economic downturns.
Having convinced of the efficasy of the applications of AI, which is considered to be complex with avant-garde jargons we need to step into peek at them with a beginners curiosity and inquisitiveness.
