When Socrates claimed that true wisdom begins with acknowledging our own ignorance, he was unlikely thinking about India’s demonetisation experiment or monetary policy committees. Yet his ancient insight - that the more we know, the more we realise how much we don’t know - may be the most valuable lesson for today’s economic policymakers.
The Confidence Trap in Indian Economic Policy
On November 8, 2016, Prime Minister Narendra Modi announced the demonetisation of ₹500 and ₹1,000 banknotes, effectively invalidating 86% of India’s currency overnight. The policy was implemented with remarkable confidence, presented as a surgical strike against black money, counterfeit currency, and terrorist financing. Government economists projected minimal disruption and swift formalisation of the economy.
What followed reveals the danger of overconfidence in economic forecasting. The Reserve Bank of India’s own data eventually showed that 99.3% of the demonetised notes returned to the banking system - contradicting the assumption that significant “black money” would be extinguished. Meanwhile, GDP growth fell, with the informal sector bearing disproportionate costs.
What’s most telling isn’t the policy failure itself, but the epistemic failure that preceded it - a failure to recognise the limits of what policymakers could predict about complex economic systems. The confidence with which outcomes were forecasted stood in inverse proportion to actual predictive accuracy.
The Illusion of Complete Information
Economic policymaking operates under what behavioural economists call the “illusion of explanatory depth” - we believe we understand complex systems far better than we actually do. This illusion becomes particularly dangerous when combined with institutional power.
Consider India’s farm laws of 2020. Designed to modernise agricultural marketing, these reforms were technically sound according to mainstream economic models. Yet they failed to account for the complex socioeconomic realities of Indian agriculture - the regional variations in cropping patterns, the psychological security provided by the existing system, and the deeply unequal power relationships between different market participants.
After massive protests, the laws were repealed in 2021. The lesson wasn’t that economic liberalisation is inherently flawed, but that policy designed without acknowledging the limits of technocratic understanding is destined for trouble.
Paradoxical Expertise: When Knowledge Creates Blindness
Perhaps the most insidious aspect of policy expertise is that specialised knowledge can create its own blindspots. The Planning Commission is a good example of this. It housed India’s most brilliant economists over decades, yet its five-year plans consistently overestimated outcomes and underestimated implementation challenges.
When the Commission was replaced by NITI Aayog in 2015, it represented an implicit acknowledgment that the old model of centralised expertise had reached its limits. Yet the core challenge remains: how to combine technical knowledge with institutional humility.
The Complexity Problem
Why is humility so essential in economic policymaking? Because economies are complex adaptive systems where causality is non-linear, feedback loops are numerous, and unintended consequences are inevitable.
The GST implementation offers a perfect case study. Despite years of preparation and expertise, the initial rollout in 2017 faced significant hurdles that experts failed to anticipate: technology adoption challenges among small businesses, classification disputes, and compliance costs that disproportionately affected MSMEs.
There was an eventual course correction in the GST story, which is worth appreciating. The GST Council demonstrated institutional humility by creating mechanisms for continuous feedback and adaptation - acknowledging that perfect design from first principles was impossible.
Embracing Uncertainty: The Iterative Approach
The most successful economic policies in India have shared a common feature: they embraced uncertainty through iterative learning. The gradual liberalisation of India’s economy from 1991 onwards succeeded precisely because it wasn’t implemented as a single, confident big bang but as a series of experimental reforms with feedback mechanisms.
The Jan Dhan Yojana financial inclusion program illustrates this approach at its best. Rather than assuming expert knowledge about how the unbanked would respond, the program was designed with built-in learning mechanisms. When initial data showed high rates of zero-balance accounts, the policy evolved to include direct benefit transfers and other incentives for account usage.
The Way Forward: Epistemic Humility as Strength
How might economic policymaking look if it fully embraced the paradox that greater knowledge should lead to greater humility?
First, we would see more pilot programs before nationwide rollouts. Second, we would implement more sunset clauses and mandatory reviews in economic legislation, acknowledging that today’s optimal policy may be tomorrow’s constraint.
Finally, we would cultivate institutional cultures that reward intellectual honesty about uncertainty. When RBI Governor Raghuram Rajan publicly acknowledged in 2013 that “we don’t know exactly what shifts the growth rate,” he wasn’t displaying weakness but a sophisticated understanding of economic complexity.
The economist F.A. Hayek warned against what he called the “fatal conceit” - the belief that anyone can know enough to plan complex systems with confidence. India’s economic policy history provides ample evidence supporting his concern. But it also offers hopeful examples of adaptive, humble approaches that acknowledge the limits of what we can know.
True policy wisdom begins not with certainty but with its opposite: a deep appreciation for what we don’t - and often cannot - know. In this paradoxical truth lies the path to more effective, resilient economic governance.