Written By: Diana Bailey – International Director
To understand reality and approach it better, we usually build mental interpretations of how things work. We use our abstraction abilities to form a picture in our minds. This visualization helps us to devise better approaches to fix the problems we are facing. But while those mental interpretations empower us and give us insight into possible solutions, they are not entirely flawless.
In the academic arena, those mental interpretations become conceptual frameworks and help us to develop theories when there is sufficient data to prove them. The theories that arise from them certainly do have their merits. However, those interpretations are hardly accurate representations of reality, and caution is always advised when developing solutions based on them.
Economic models and our bounded rationality
The shortcomings in conceptual frameworks (models) become obvious when we consider their original source: the human mind. The human mind is fallible and can only generate plausible solutions within its capacity to do so. This capacity is significantly constrained by various factors, which include the amount of information available, cognitive capacity and the time a person has to make a decision. Therefore, humans’ rationality is bounded by those factors, as proposed by Herbert Simon.
Our minds are prone to biases of different sorts, and so are our models. Those models often fall short of reality. They capture a fraction of the truth, but not the whole truth. They work only a percentage of the time. And the costs of our bounded rationality are too wide to quantify. They range from the micro to the macro, given that decision-makers use models to guide their decision-making. Those models can certainly be improved, but decision-makers need to be aware of the following flaws in them.
Economic models, among others, use the past to predict the future.
For researchers to prove that their theories hold, they need to support them with data. And our only source of data is the past,not the future. Due to this limitation, theories are bound to be imperfect, and they should not be written in stone. They are only valid as far as the past is concerned, but not the future. That is why we can know the future only by speculation. For example, projections of economic growth and other indicators are almost always adjusted with new developing events, a practice that perfectly captures the imperfections of our models.
As our understanding of reality improves, so should our models. The field of human-resources management, for example, has benefited from these improvements significantly, as the approaches taken by managers towards their employees have become more and more humane, by virtue of the advances in psychology and the understanding of human behavior.
More data should enable better models, which can be achieved by using artificial intelligence as well as human intelligence. If applied well, this should unlock enormous potential as new data keeps on challenging our existing ways of thinking.
Economic models are highly sensitive to the validity of their assumptions.
Any one invalid assumption in a theory renders the entire theory useless, or even harmful. As such, the cost of errors increases significantly. When talking about macroeconomic policy, such errors have serious implications on people’s lives and affairs, and they can cause disruptions in the entire socioeconomic system. Take, for example, countries that have tried to apply the Western model for achieving economic development and prosperity, and could only harvest social unrest.
Financial markets often do not respect our models.
The debate about whether passive investment or active investment yields higher return is a heated one among finance professionals. Such debate would not have existed had our economic models worked the way we intended them to. The behavior of markets has always defiedand continues to defy the best of the models devised by even the best of economists. For this reason, senior hedge-fund managers sometimes advise against them, and other money managers even advise the use of instinct as a good predictor of markets rather than models and preconceived notions.
Models assume closed systems.
In order for them to work, economic models, among others, need to assume a closed system. The Solow-Swan model of economic growth is one example. This model assumes no international trade and no government, among other unrealistic assumptions. What is more, it assumes neutral technical progress. In other words, it assumes that humans do not learn nor improve their methods. This lack of feedback loops has drawn some criticism to the model.
But the Solow-Swan model is not the only one, and examples of such models are plenty. Models by definition need to be limited and cannot accommodate too many variables, or they lose their simplistic value. So even the best models assume closed systems,and therefore when they are applied, they lead to entropy, which often spells chaos in societies.
Two entirely opposing economic models can both be valid.
Arguments between economists are contentious and tend to go on without end. The reason isthat economic thinkerswith opposing views can provide compelling arguments to support their opinions. Take, for example, Keynesian economics, which encourages governmental intervention in the economy, and the view of the Austrian School of Economics, which argues vehemently against deficit spending. Both schools have proven to be useful at different times. The financial crisis provided a lesson that government intervention is necessary for the economy to get out of the ruts, and thus it was the best realistic measure to counter the effects of the crisis. On the other hand, the Austrian School has shown how government intervention leads to cycles of boom and bust. Both views are correct, although they are contradictory in nature. What can be learned is that government intervention should be assessed differently in different socioeconomic conditionsand that we cannot give a final verdict about whether it is good or bad without considering other factors.
Conclusion
Economic models do provide insight into what is going on in reality, but rarely do they capture the whole image. Often they fall short of showing us the full picture, and they can lead decision-makers to make decisions out of context. Policy failure and difficulties steering organizations are among the few direct results. Our economic models can aid us in understanding economicphenomena, but should not replace sound judgement. Decision-makers need to consider that every case has its own peculiarities, and they should act accordingly. Otherwise, economic models provide only a “one size fits all” solution, which hardly ever yields the results we want.