Artificial Intelligence in Economics | Adamas University

Artificial Intelligence in Economics

Artificial Intelligence, Economics

Artificial Intelligence in Economics

  • What is Artificial Intelligence?

The main feature of economic theories is that they try to eliminate the effects of uncertainties by attempting to bring the future to the present. Artificial intelligence (AI) is the intelligence which is derived in a non-human manner out of synergy of working of individual units towards a specific direction with a defined objective, for example, in a room where many people are meditating out of their own choice without forcing others, a vibe is generated and motivates a newcomer to sit in meditation spontaneously. This vibe does not belong to any body in particular, but benefits everybody in performing the meditation activity smoothly. It is AI.

  • AI in Price Discovery in Real Sector

The determination of selling price in a market where innumerable transactions are happening with respect to a particular product be it a potato in the vegetable market or a share of some company in a stock exchange is an AI process. A process similar to the auctioning in the vegetable markets in India in the early morning is codified in the software programmes and is run on the trading platform of the exchange houses to track the tatonnement of bids and offers over a scrip.

  • AI in General Equilibrium

The earlier circular reference function in the spreadsheet helping simultaneous determination of output and interest rate together in real and monetary sectors has grown into the AI process where it is possible to determine volume and prices not only in all output markets but also at the same time in all input markets delivering a general equilibrium at a much-reduced time frame. 

  • AI, Big Data, Data Science, Analytics, Machine Learning and Algorithm

There is hardly any water tight compartmentalization among big data, data science, algorithm, analytics, machine learning and AI. We can say that one works with the others. Let us consider the process of forecasting admission or demand for treatment in a private hospital for some specific disease. If information are collected from all patients or their relatives regarding what prompted them to go to a particular private hospital in presence of many other hospitals offering the same treatment and thus such data are collected from all patients or their relatives of all private hospitals offering the said treatment that enormous data can be structured by big data process and then modelling can be conducted by analytics procedure.

Once the demand function is framed, suitable apps in android and other operating systems are developed as the channels of contacts between the points of demand and supply. In connection with microeconomics many of the apps like amazon and iiomart are playing the roles of online markets of final products in the real sector. The markets of the factors of production like capital are those like YONO SBI and NSE mobile and those like are for labour. 

Algorithm can help framing the demand function and then AI may conduct the forecasting process.

  • AI in Financial Sector

In financial economics there is widespread use of AI in making decisions of trading in financial securities like stocks and bonds based on prediction of their prices and also in making decisions of entering interest rate derivative contracts with speculative or hedging motives based on prediction of benchmark interest rates like LIBOR (short term) and 10-year government security yield (long term).  Algorithmic trading, automated trading etc are now common vocabularies in financial literature. The most spectacular contribution of AI is toward indicating a tail loss in the value at risk that was not available before the subprime crisis. That way AI can be useful in preventing systemic crisis.

  • AI to Prevent Loan Default

Application of big data to the details of loan defaulters of the all the banks and application of AI in detection of moral hazard underlying certain lending-borrowing decisions can provide an earlier signal about a prospective default.

Above all are about use of AI relating to profit-making or utility-maximizing decisions generally in the arena of microeconomics, financial economics, industrial economics and game theory.

  • Socio-economic Applications of AI

Relating to macroeconomics and development economics, big data, data science and AI can be useful, e.g. in predicting (i) the number of migrant labourers between two regions in urban economics, (ii) the interest losses to governments and the corresponding volumes of funds returned by the target users in public finance, (iii) the volumes of unaccounted transactions in the informal sector and the concerned behaviour of the economic agents involved in those transactions in Indian economy, (iv) loss of incomes of farmers disconnected from the electronic national agricultural market (eNAM) in agricultural economics, (vi) inflation and unemployment in macroeconomics and so on.

  • AI for Economic Research

Theorizing economic behaviour is a major part of economic research. The process of collection of data on economic behaviour has been evolving toward being more and more automated since the ICT (information, communication and technology) revolution. Over last two decades in India the researchers have been finding their hard discs deluged with big data collected through internet portals and electronic payments. Analysis and interpretation of these data using AI ushered in a new age of economic research.

  • Caveat

Unfortunately the Inability to track the use of each and every coin and paper note of a conventional currency or fiat money in an emerging economy causes cavity in the database of transactions in a sizeable informal sector as a result of which application of AI and the associated tools may not be able to yield the desired results in absence of computer literacy, financial inclusion and technology-oriented mindset of the entire population.

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