by Tanveer Karim
Like its natural counterpart, social sciences also employ the use of the scientific method in order to explain phenomena pertaining to humans and society. While many of the social science disciplines explain various observed phenomena, many others, e.g. economics, tries to formulate first-principle theories on the basis of which it tries to predict future behaviours. The widespread usage of quantitative techniques in economics also makes it a suitable discipline to easily use mathematical and statistical techniques that natural scientists often deploy for their own research.
However, one frequent criticism of traditional economics is regarding its predictability, or lack thereof. Behavioural economists such as David Laibson point out that it could be due to the fact traditional economics uses very stringent assumptions, e.g. that all human beings are rational actors acting in good faith. But the lack of good predictive models in traditional economics led scholars like Dr. Laibson into questioning the underlying assumption itself. This is a critical step in rethinking about theory and is not limited to economics, but applicable to any discipline using the scientific method.
As a response to this confusion, in the 1980s the subfield of behavioural economics was founded that relaxes the assumption that all humans make rational choices rather takes the psychology of decision-making process into account. The underlying assumption in this case is that by using psychology to understand deviations from rational choices, one could construct a better predictive model in economics. An example of how this might be put into place is as follows - it is observed that most people attach more weight to short term gains, e.g. what you can do to achieve satisfaction now, than to long term gains. So watching Netflix now might be preferable to finishing a hard problem set that is not due a week later. Hence if one models the behaviour of the traditional rational with the help of such psychological weights, then they can start to better understand how people truly behave in economic terms. Put simply, while people might be motivated by economics, their overall behaviour is shaped by psychology and thus, psychology becomes a crucial tool for economists to make a better predictive system.
This important caveat can also be applied to understand the interface of economics and climate change. As the impacts of climate change are not apparent today in many parts of the world, people in those places are not motivated by the long-term economic incentive of renewable energy because using non-renewable energy in the short term leads to better gains. While a traditional economist might expect that as soon as rational actors find out about climate change, they will start to take it seriously, a behavioural economist understands the role of psychology and can use that to build a model that better mimics the real world.
Another key aspect that makes the argument against traditional economics and rational choice is what makes a person rational in the first place. In order to make rational decisions, one needs to have knowledge and understand the underlying principles before making a decision. However, as economics and financial models become more and more complicated, they go outside the realm of a layperson’s knowledge and so one must take that into account as well when considering how an average person behaves. This is applicable not only to economic knowledge but also to other policy-level topics such as climate change. For example, the way a layperson quantifies or understands the term uncertainty closely mimics a standard Gaussian curve, i.e. most of the time things will work as we expect and in extremely rare cases, we will see extreme events. But things such as economics or the global temperature may not indeed follow the Gaussian uncertainty, but could have a fatter tail distribution which would lead to more extreme events happening more frequently. Thus, even if a layperson makes the correct rational choice given their understanding, it may appear as irrational. Hence, one needs to accurately model these underlying phenomena before building a trustworthy economic model that has good predictive power.
All of these situations point to the fact that while natural sciences such as physics have high predictive power, in many cases they can be considered as rather simple due to the fact that the objects of interest that govern physics, e.g. atoms, forces, follow simple laws/theories and that yields highly predictive systems. On the other hand, disciplines such as economics try to interpret the aggregate behaviour of highly stochastic objects, in this case people’s decision choices, which makes the discipline inherently less predictive than physics. It is not a limitation of economics but simply a consequence of the subject matter of interest. One can however hope that as experts start to consider more of these interdisciplinary approaches, they might be able to use each other’s techniques in building a more robust theory of economics that we can use to study long-term economic behaviours.