‘Economics has never been a Science – and it is even less now than a few years ago.’ Paul Samuelson describes how economists prefer to refer to their theoretical models – those which have minimal affiliation to the real world, for they are unable to consider the irrational human behaviour on state or macroeconomic affairs. Redefining an array of fields in unprecedented ways is essentially how Artificial Intelligence has inflicted upon several industries, simply by using a set of techniques to analyse data to make more well-informed inferences and decisions about our world. To link two contrasting worlds – Economics and Artificial Intelligence – would perhaps poise an argument that Economists solving economic problems would have an arguably dramatic upheaval since Adam’s Smith inception of Economics, aiding humanity in solving a range of issues. As both developed and developing countries look forward to moving towards a mature economy, there will be several growing global problems that economists need to solve within the next 20 years. While it is undeniable that Economists will have to help solve several growing global problems within the next 20 years with economies shifting towards stability, it is, however, questionable and more correct to contemplate on whether Artificial Intelligence will be the vessel for our economy to be more sufficient and helpful in practice.
Considering the fast growth in the global population, there has been an increase in advocacy for the climate crisis and the limitations caused by over-exploitation of resources in our circular economy. All these are, incontestably, the ramification of the previous industrial exploitation and large-scale economic disparity where 19th-century colonial empires set out with the intention of depredating natural resources in foreign countries, ruthlessly exploiting capital and land – all without considering the effects of the rapid depletion of resources required to support the phenomenally large natural population growth in the affected region. While the Industrial exploitation is undeniably the main cause contributing to poverty and instability in developing countries, the main factor that is instrumental in large population growth is large due to illiteracy, social exploitation and lack of democratic governance in the country. Combatting the issue of over-population will ultimately see that ‘repentance’ due from the exhaustion of natural resources will be needed through changes in cultivation or transformation of the production of plants. While economists may argue that ‘nudge’, as a concept in behavioural economics, can positively reinforce and indirectly suggest behaviours within groups or individuals through positive advertising, Artificial Intelligence can, feasibly, act as a potentially effective system that analyses data concerning agriculture to provide valuable outputs, for example – ‘precision farming’. Using Machine Learning algorithms – an aspect of Artificial Intelligence, the technology can be used to collect both real-time and historical data to make specific decisions concerning the area or type of farming. The provision of such technologies will increase the total yield of crops, improving the general standard living conditions to allow humans to look towards becoming productive relative to conceiving at a higher rate. Detailed research conducted in Bangladesh, Indonesia and Nigeria has also suggested that degenerating living conditions increase the likelihood of reproduction rather than decreasing it. As aforementioned, a deep study in AI can also reinforce and control overpopulation by determining areas for improvement using sustainable technology such that the productivity and GDP growth will increase without compromising the total employment rate in the area, providing local economies with a circular flow of money to better their well-being. Therefore, Artificial Intelligence can perhaps be a substitute to the current analysis technologies by providing one that has much more profound, yet subtle impact to use as a guide for solutions – it enlightens what changes can be made to the daily lives in an economy, on both individual and market perspectives.
The recent 2019-nCoV epidemic is an example of how insufficient supply and demand can lead to a stagnant shortage in an economy, or in such cases – economies, as both local and multinational firms are unable to keep up with the increase in demand for sanitation products. At the standard supply and demand graph, prior to the spread of the virus, the general equilibrium theory explains how consumers interact with the sanitation market, ceteris paribus. However, with the increase in media attention on the infestation, the increase in shortage has, according to the law of supply and demand, cause prices to shoot up at a high rate, inevitably, lower-income families are unable to purchase them for their personal well-being, leading to significant welfare loss as the provision of such necessary products become limited and in high demand. Perhaps, such supply is low in regions or provinces with low income due to the Friedman Theory which states people will make decisions on consumption based on their income over time; thus, suppliers choose not to supply products at required areas, leading to biases and prejudices in where such products are supplied – all without taking into a rational account of how human behaviour may respond in such conditions. The predictable nature of human beings perhaps could allow artificial intelligence to estimate individualized demand and supply using the economic concept of game theory where it understands the current social environmental circumstances that inevitably cause individuals to decide, influencing a society’s microeconomic facets. On the contrary, it is also important to note that, very similar to those who are made aware of their biases, artificial intelligence could be fed statistical biases – which may skew the solution required to accurately target the output. Thus, AI could also have the ability to discriminate; for example, upon identifying that rural areas may have lower literacy rates, it may intensify the Lewis Turning Point situation where there is a surplus rural labour in the primary and secondary sector – hence an increasing in employment saturation of such jobs when there are other applicable job vacancies available or an economy without balanced growth policies. Despite this, when assessing the potential setbacks to using Artificial Intelligence as a data-analysis program to output an individual’s or firm’s interests, economists could perhaps considerable to say that the potential for biased data is, for now, negligible relative to describing our world using models.
In the economic impediment over 12 years ago in 2008, the global economy’s fiscal tightening motifs’ unfavourable effects were magnified, simply due to the government’s belief for an austerity – this inevitably caused several frauds in both the banking and stock market sectors. The inability to regulate both fake news and frauds within neural network systems within an economy will be detrimental the country’s financial growth as both consumers and firms nolens volens lack the confidence to do so. For example, in 2018, a few unauthorised financial frauds in the UK summed to a value of approximately £844.8 million, despite the Payment Card Industry Data Security Standard (PCI DSS) being held effective in such events. Its limitations are in its ability to oppose frauds calls when there are unique changes in human behaviour; thus, the use of Artificial Intelligence in banking services can be used to counter unauthorised credit or debit cards. By its nature, artificial intelligence can help manage financial institutions overnight by monitoring any behavioural patterns, identifying anomalies and recording any atypical changes in the systems’ functions. Furthermore, Artificial Intelligence spans wides in its use due to its ability of counterfactual thinking, analysing the past or historical events and making potential deductions as responses to such. This would then decrease the possibility of such intolerable events to occur again, simultaneously increasing consumers’ confidence in the economy. While it is undeniable that there are potential limitations to AI’s uses, it is currently the best viable approach to invigilating the degrading spread of fake news, preventing our economy from falling into a greater recession that the previous ones and highlighting abnormal behaviour during banking frauds by churning out warning methods to our global economy.
Rationality has not been alive for some time, at least within the lifespan of humanity; its last presence once came and left when Homo Economicus once lived. Humans have never conceived absolute rational behaviours, such theoretical ideas to model their demands would then require a priori quantitative economic model – using the concept of Artificial Intelligence to solve problems within the next 20 years would then need to make an initial assumption of ceteris paribus. Perhaps, it is irrationality and emotions that lead to majority of the economists to believe that markets are prominent reflections of both groups and individuals’ rationality as they perform transactions based on their ability and willingness to, thus creating incompetent frameworks such as perfect competition, even when it is known that many social events are pervaded by inherent indeterminacy. Our humanity’s tragic flaw is its inability to recognise that such non-radical and irrational assumptions are simply what led to the advent of Economics’ neoliberalism and the global financial crisis in 2008; so, perhaps, Artificial Intelligence can help economists to more accurately light an inception of Economics striving to collect theories that more accurately model human behaviour: markets, firms, individuals, stakeholders and governments to solve the problems we will be facing in 20 years.
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