Prioritisation is overlooked. Here’s how the next 20 years could look for economists

Scarcity. A word that is at the heart of our pursuit of economic enlightenment, the idea of which sharpens the blurred lines that we construct between the discipline of economics and neighbouring psychology and political science. Economic thought has often been shepherded by the age-old problem of organising limited resources in a world where economic agents have their own agendas in fulfilling wants and needs which is, and always will be, at the heart of any relevant economic problem.

As we venture into the new decade, scarcity has again reared its inexorable head. It is hardly a polemic idea that scarcity and social problems exists around us, but a much more contentious topic is prioritising urgent and critical economic problems. So much so, that the prioritisation of research can absolutely be an urgent and critical economic problem itself! To establish an order of the most crucial problems economists have to solve within the next 20 years, evaluations of the urgency, scale, and relevancy (to economics) of the problem would be conducted. Utilising this composite metric, three clear economic problems can be identified: The disruptive nature of AI, climate change, and finally, with more of an introspective theme, the systemic flaws in modern economics.

In the midst of an age dubbed the ‘fourth industrial revolution’ by Charles Schwab in his book of the same name, it is arduous to not be overwhelmed by the pace of technological advancement. Harder still, is to analyse the complex relations technology has with society, and to predict its implications. This conundrum has increased the complexity of socio-economic problems, such as the concerns of privacy arising from the internet of things. Thankfully, it is often the case that complex problems have more potential for solutions. Enter artificial intelligence, a technology so revolutionary, that “it has the potential to incrementally add 16 percent by 2030” to global economic output, a number equivocal to the current GDP of China. In the next 20 years, AI has the potential to slash the costs of prediction (Agrawal, 2018) improving productivity (and potentially relieving some social problems). For economists, (narrow) AI’s capabilities are able, to put it simply, precisely model positive economics and predict trends. After 20 years, however, AI has the potential to be so much more. AGI, or artificial general intelligence is predicted by most AI experts to appear after 2040 (Mueller and Bostrom, 2014).

By far the most significant problem in the next 20 years is dealing with the disruption AI is inevitably going to cause. The scale of its implications are immense – AI could potentially automate 47% of jobs and the digitised economy could lead to unequal development as the less technologically literate are excluded, including the 4 billion people still offline(Manyika, 2017). Moreover, artificial intelligence has the potential to empower certain groups of individuals. Brynjolfsson argues that “technology-driven economy greatly favours a small group of successful individuals by amplifying their talent and luck”. As wealthier individuals become the ones who can afford the education to achieve mastery of technology, which in turn gives them greater career opportunities, we can observe technology’s role in magnifying wealth inequalities. At the same time, Brynjolfsson and McAfee argue that AI could render many people unemployed, as it could automate analytic and predictive jobs. Harari, author of the bestselling Homo Deus, argues that AI will outperform our cognition, which is what makes humans distinct from any known entity. He recognises there have been similar threats before, but the dawn of AGI, he proposes, will eclipse any other human function. As employment opportunities are taken away, livelihoods are lost and some areas never recover. The workers of the rust belt areas of the US are ill-equipped for other kinds of work, and the dissent, bred by unemployment, has burst out to the political sphere, a factor that some attribute to the populist phenomenon. Due to the unpredictable nature of AI, it is difficult to pinpoint when AI can replace humans. Recursive self-improvement is the ability for intelligent beings to exponentially increase their knowledge. As beings get smarter, they can learn quicker. This would eventually result in what Irving John Good dubbed the “intelligence explosion”, a point in time where the the intellectual gains made by AI would be incomprehensible. If the median experts’ estimate is to be trusted, and AGI becomes a reality in 20 years, it is imperative for economists to analyse and overcome the challenges, as the alternative is chillingly unthinkable.

The rise of environmental movements, such as the extinction rebellion, signify a shift in public attitude towards the negative externalities of production. No longer are the social spillover effects ignored by a generation that are more likely to be outspoken on such issues. The scale of the issue is clear and the science is well-known. From an economic perspective, it cost North America 415 billion in the last 3 years, and is set to increase. It also impacts poorer communities disproportionately. A study conducted by Nature revealed that climate change reduces average income in the poorest countries by 75%, while some wealthier countries gain income. This is due to the fact that agriculture is the industry most affected by climate change as unpredictable weather decreases crop yields, crippling agricultural economies, which tend to be LEDCs. They also do not have the infrastructure, or the capital to invest in infrastructure allowing them to adapt to our changing climate. Without flood walls, or weather-resistant crops, it is a herculean task for these communities to face climate change alone. The central issue of scarcity here, is the fact that resources become more scarce, as the cost of production increases through the realisation of negative externalities. Moreover, these negative externalities only have a minute effect on the producer, and it is often the case that the people who benefit the least from climate-changing activity. This raises the question: How do we incentivise climate-changing actors to give up their private gains for an altruistic purpose? The fact that scarcity is central to the climate change problem makes it very much a relevant economic issue. How can we allocate resources to prevent, or reduce the effects of inevitable climate change? How do we support LEDCs, who are disproportionately affected by climate change? How do we encourage sustainable development? Climate change remains undeniably an economic problem.

Whilst it is certain that disruptive technologies leave social scientists unable to adapt to relentless technological progress by perhaps challenging our traditional axioms of economics, the failure of our discipline to resolve certain social issues owes to the systemic flaws in modern economics itself. For example, Anderson, in ‘The fate of economics and the role of economists’ argued that “economists have become more partisan and mathematical in their training”. Consequently, they produce “quantitative partisan perspectives” which are manipulated by political agents with agendas, leading to disingenuous ‘truths’. Instead, he argued, that we should focus on multi-disciplinary approaches, which adds subjectivity and context to a discipline that demands it, imputable to the complex nature of the subject. The criticisms do not end there. One quick google search would yield thousands of calls to end the current monetary policies, or articles spilling the beans on the realities of neoliberalism. There is some truth to these claims. Social issues such as inequality, or housing crises, is, to a substantial extent caused by systemic flaws in the discipline. Naturally, this is a divisive topic amongst economists. On one hand, libertarian thinkers such as the Cato Institute argue that wealth inequality is caused by cronyism. They argue that “businesses gained privileges from the government that undermined the public interest” which generates inequality. On the other, some argue that firms hold too much power, and taxes are needed to redistribute wealth. Nonetheless, both sides imply that the reason for inequality are previous economists’ failure to produce and apply good economics.

Likewise, artificial intelligence, if not applied properly, could not only be ineffective in solving our current issues, but could create more problems than it solves – the accuracy of AI predictions are, crucially, subject to the way economists use this powerful tool. This is an undeniable threat, shown through the claims by alarmed experts in the previous paragraphs. These are internal problems, problems that are symptomatic of systemic flaws (rather than external factors of science and politics) which is too large of a problem that infects, and simultaneously is made worse by external problems.

In this seminal epoch of history, it is paramount that economists remember their purpose in society, which, ultimately, is to “improve the living conditions of people in their everyday lives”(Samuelson and Nordhaus, 1998). Whilst we should wholeheartedly celebrate the successes of economics in advising global development, we must accept that it is yet an incomplete science, and not to treat is as a paragon of social organisation. To attend to the aforementioned problems is an imperative matter, but this can only be achieved by revising our practice of the discipline.


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