Covid-19 and the rise of assertive data-driven governments

Governments can use data-driven insights for corrections whenever laissez faire leads to unjust disbursements

Even during the most ordinary circumstances, individuals lack the capability to make optimal decisions. This is because in most situations, it is infeasible to traverse the large number of possible choices. Hence, in order to reduce the complexity, heuristic techniques are used that prune away options to reach effective conclusions. However, the trimming down of possibilities often truncates the best result, and that leads to converging to a less than ideal outcome. The Nobel prize winning economist Herbert Simon explained this phenomenon as bounded rationality which produces what are known as satisficing solutions.

In general, our tendency to fall short of the best outcome is exacerbated when individuals are striving for survival during crisis periods such as the current Covid-19 pandemic. In an environment where resources are in short supply, every entity views the other as an adversary and its actions singularly serve its own self-interest. Hence, during these uncertain conditions, sole reliance upon free market leads to unjust resource dispersal. Therefore, in order to fix the inevitable imbalances, we are witnessing higher levels of government intrusions using data-driven means to prioritise goods production and distribution. Hence, as governments get comfortable in operating in this new interventionist mode, it could arguably regard it as the new normal even after the pandemic period ends.

Any crisis-laden atmosphere results in a loss of trust which leads to intelligent people making unwise decisions. This phenomenon is best explained by the prisoner’s dilemma in the field of game theory. In this paradox, the two players act as prisoners who are charged with robbery and are locked up separately. They are then independently given an offer by the police which promises to reward them if they choose to defect. The option to defect could lead to immediate acquittal in case the other partner stays loyal who would then get the maximum punishment. If both partners defect, the outcome would be intermediate, whereas, the best situation occurs when both prisoners stay loyal and the police is compelled to file minimum set of charges. In situations when partners lack mutual confidence, the logical course for both the players is to defect since opting to remain loyal exposes them to the worst possible outcome. Therefore, due to untrustworthy environment such as the one we have now, people select a rational course of action that is in fact far less than the ideal outcome.

During the ongoing Covid-19 pandemic, there are many instances of prisoner’s dilemma taking place across a variety of situations. An often-quoted case is that of hoarding by shoppers despite knowing fully well that sensible buying would leave plenty for everyone. However, individual consumers choose to purchase disproportionately since level-headed shopping is deemed risky if others did not follow suit. Therefore, shortages became the order of the day as lack of mutual trust compelled everyone to shop excessively.

In dire situations, the compulsion to stockpile is not confined to individual shoppers but even states and countries succumb to this conduct. For example, in early April within the state of New York, the Governor announced that he would use National Guards to seize ventilators from health facilities in upstate New York and redistribute it within New York city where they were needed more. This is a classic case of prisoner’s dilemma where private health facilities overstocked on ventilators leading to their suboptimal distribution. Since the state government possessed patient data from across every part of New York, it justifiably felt that it is in a better position to determine the demand for ventilators and threatened intervention to correct the unfair distribution on the ground.

Another example of data-driven state intervention occurred when models were used by the US Federal Government to forecast the spread of coronavirus. This was done in order to ascertain the critical need for ventilators and develop a production plan to timely fulfil the requirements. To achieve this objective, the auto industry giants were asked to repurpose some of their manufacturing facilities to develop ventilators. As they pondered regarding the conversion and were seen to mutually drag their feet, President Trump had to reluctantly invoke the Defence Production Act to force the auto industry to comply.

As we transition out from the Covid-19 lockdown, data-driven decision-making will start to become mainstream for governments as they undertake changes across three dimensions. Firstly, governments will begin to pursuit access to all types of information including people’s data in order to timely react to opportunities to serve its population. This is important since a portion of required data is dispersed amongst various corporations, and in the past, they have been reluctant to share it with governments. For example, in order to fight the pandemic, countries such as China were quick to develop contact tracing applications due to their ready access to requisite information whereas other nations were unable match its speed. Obviously, the government’s broad access to citizen’s data will lead to concerns regarding freedom and privacy, however, I believe democratic societies will eventually enact appropriate legislations for it.

Secondly, there will be a large increase in investment on big-data platforms that support analytics and high-end modelling using machine learning. The glimpses of data-driven governments are already visible as national leaders across the world have publicly shared ‘models’ and how they are being used to drive actions to ‘flatten the curve’. Currently, the discussion is mostly confined to analysing best means to contain the Covid-19 spread. However, as the government starts to access broader set of data, its appetite for implementing model-based decisions will expand significantly to other business segments.

Finally, a new evolved version of public-private partnership model will emerge in which the government is positioned to play a dominant role. For example, after the Covid-19 scenario, model-based forecasts can be used to identify opportunities for interventions beyond the health sector. In other words, government’s accessibility to AI-based trends on big data will be used to forecast economic disparities, which could be rectified in partnership with the private sector.

Historically speaking, interventions by government organisations have mostly been frowned upon since they are regarded as wasteful and uneconomical. That view has some merit, however, the key difference between what happened in the past versus now is in the use of big data. Previously, informed decisions were not entirely possible due to missing or unreliable data. In addition, the technological infrastructure as well as software did not support its analysis. However, during the current crisis, governments have been pushed into the forefront to leverage information analytics in redefining their relationship with the private sector through positive interventions. Hence, what this has shown is that governments can use data-driven insights as the basis for making corrections when laissez faire leads to unjust disbursements.