How bounded rationality, heuristics, and biases have been used to create effective public policy

WHAT IS HEURISTICS: 

A lot of research has been carried out on the ‘economic behaviour’ of man starting sometime in the 1950s, as opposed to the neo-classical theory about a “rational”  economic man. Three components of this discourse emerge viz. (i) thinking automatically against deliberative thinking, (ii) thinking socially i.e. how history, context, and society influence thinking & finally (iii) thinking with ‘Mental Models’ or frame of references. 

These elements are of prime importance in the design of public policies as against the Bayesian principles of economic modeling. Subjects as diverse as neuroscience, cognitive science, psychology, sociology and political science, etc are increasingly becoming part of the development, delivery and implementation of public policies. Earlier, “rational” decision-making, even under conditions of uncertainty (which cannot be statistically measured) as against risks (whose probability can be evaluated) was assumed to deliver intended outcomes, but seemed sometimes to have gone completely against them whereas policies designed with “bias” and “bounded rationality” aspects, with low cost and high implications, have in fact delivered in many cases when the “benefits” of the policy have outweighed the “perceived loss” due to these ‘biases and bounded rationality’. 

This is due to the fact that the world is becoming increasingly complex so that ever faster policy decisions and prompter implementation are required; complexity would require complex mathematics of probability taking a lot of time and resources and hence delaying the policy implementation and outcomes. Given the limited cognitive abilities of humans, digesting fragmentary information in a time-constrained world places a limit on their ability to make “rational decisions”. “Heuristics” thus play an increasingly important role as a policy-making tool when speed and time of delivery is paramount, as we will see in our case study below. 

COVID VACCINE: THINKING THE UNTHINKABLE 

An example that can be looked at through the prism of “bounded rationality” & “heuristics” is the production of Covid Vaccine by India. Since Covid-19 was a novel virus and a “once-in-a-hundred” years pandemic, nothing was known about it, its treatment or its prevention. Though the age-old wisdom of vaccination was available to fight viruses, the development cycle of any vaccine takes years and even a decade in R&D alone. Yet, could the development of Covid vaccine afford such a delay, given the relatively high fatality rates of Covid-19? The answer was a big “No”. In almost a year’s time, the vaccine was developed, tested and delivered in India. This was a case of “bounded rationality” embedded in a public policy decision affecting more than a billion people. So how were these “bounded rationality” and “heuristics” applied in developing the Covid Vaccine? The answer lies in the “bias” against the backdrop of “Information Asymmetry” but using this bias “towards developing a vaccine to save humanity”. So a “heuristics” based decision was taken by deploying the human and financial resources required to develop a vaccine as fast as possible. During its development phase too, the ‘Emergency Use Authorisation’ was used as “heuristics” in the testing phase on a sample population. 

THE DRIVING FORCES: UTILITY MAXIMISATION

“Utility maximisation” was the cornerstone of this policy and time was of the essence to fight the pandemic. India already had the expertise in vaccine R&D, vaccines manufacturing and also had the wherewithal to implement vaccine delivery to the last mile, based on the earlier polio vaccination drives. So, “Utility Maximised” was the axiom. Though some percentage of the population was very hesitant and concerned about possible side effects & efficacy, these suspicions were mostly overcome by “Framing” & “Anchoring” which was achieved when Prime Minister Modi was vaccinated on live TV, an event that was telecast even in the remotest corners of India. Overall it was a case of “satisficing” without “rationalising” the development costs, impact, efficacy, failure or success and several other factors which normally would have been considered rationally as in any other health policy project before approving the budget for R & D, human trials and subsequent launch on the market. This policy, dictated by expediency rather than prudence, allowed the Indian Government to vaccinate almost 1.5 billion people within two years from the time when the  Covid-19 epidemic was detected.