I like to think about and discuss business. It's what economics has helped me do, and it's why I was attracted to a career in economics in the first place. I was recently asked for advice by a (retail) tenant of a landlord that was planning to compete directly with the tenant's offering. I applied my mind, essentially following the money trail, for clues about the rationale for the landlord's intended action and the likely consequences. I also looked for arguments for the tenant to advance to persuade the landlord not to proceed, in the landlord's own interest (as well as in the tenant's).
Economic action is not a game played once off for amusement or small stakes. It is played over a lifetime of endeavour for cumulatively large stakes. The rational way to do it is to use the patterns gleaned from trial and error to plot a path forward that maximises present value over the long run. If it seems to work, the action is repeated until it doesn't, and the business, to survive or better still, prosper, adapts and innovates according to the continuous flow of evidence of sales and costs.
The assumption that Homo economicus will follow the money – if you can understand and describe it well enough – is essential for the analysis of any business enterprise. It's an essential tool for identifying the business model, the theory, and the strategic insights that drive the actions observed. The well-informed investment analysts who are rewarded for valuing any business will identify the so-called moats that protect the business from competition and the runway of future opportunities to grow profitably. Flowing from this, they will also recognise the key performance indicators that drive the remuneration of executives and, therefore, the actions of a business enterprise.
The answer came through almost immediately and repeated my own reasoning and conclusions almost exactly
I thought I would test my reasoning and logic by asking the same landlord-tenant question of an artificial intelligence (AI) tool. The answer came through almost immediately and repeated my own reasoning and conclusions almost exactly. This was no surprise, because the question had been asked before and answered similarly and the bot was able to recognise from the complete record of thought and action. AI did as well as I did, though more rapidly and conveniently, in answering an interesting but clearly not an original question. In other words, if you are having to cope with a complex issue with contractual implications that will affect future income, refer conveniently to the bot and take seriously what it comes up with.
What matters most in economics is the question asked. If the question is interesting and relevant, the answers then follow almost automatically. The best economists identify interesting questions to ask and provide their own compelling answers (which, by now, will be systematically picked up by the computers that power AI). The test of any student utilising AI is not the quality of the answers provided by AI, but rather whether the student has asked an interesting question and is capable of interpreting the answers.
Published research is for the record; it is public knowledge that is now systematically and comprehensively collected by bots
But what then of the role of true originality? New questions asked and answered can advance the frontier of knowledge, which then becomes part of the wisdom recorded digitally. Academic research is valued for its originality. Citations earned by scholars in the same field recognise the contribution of the inventor of an idea and its formulation. Published research is for the record; it is public knowledge that is now systematically and comprehensively collected by bots.
The reward for the original researcher comes in the form of salaries, research grants and promotion up the academic ladder. The laws that protect copyright and confer time-limited monopolies over intellectual property, such as patents, encourage originality. Patents limit property rights, but the limits are sensibly set so as not to inhibit invention. The payment for copyright can perhaps be extended to the actions of bots that are being monetised for their owners.
Solving the ownership of intellectual property issues raises another question. Can the bots do more than keep the record? Can they advance knowledge originally in ways that advance the human condition? Can they observe the world around them, analyse patterns and come up with helpful explanations and actionable theories that add to output and utility? This is what scientists and analysts of all kinds (including economists) do.
Much scientific enquiry is a mixture of observation and generalised theoretical explanations that hopefully can be tested with evidence from repeatable experiments. It's an interdependent mixture of induction (seeing the patterns) and deduction (making sense of them). The bots can record, measure and summarise the established questions and answers. But can they be creative in the way that the best scientists and analysts are? Can they see the world from their data centres and exercise the imagination of the intended experiments that is the stuff of true creativity? I would suggest not. Creativity and originality, rather than the literature review mastered by the bots, are the path to true excellence in science and the arts. This raises the further question: how can creativity be stimulated by individuals or teams? And what can educators do to foster creativity?
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