Why gut feel matters

Early last month, the Washington Post linked to a remarkable mea culpa by Olivier Blanchard, chief economist at the International Monetary Fund (IMF). He admitted their predictions on which way the Greek economy would go had gone awry. [See the IMF paper.]

To tide over the economic crisis Greece is now going through, the European Central Bank and the European Union (EU) mandated the Greek government to cut spending. Econometrics wonks at the IMF got down to work and deployed mathematical models at their disposal to predict what could possibly happen. When the number-crunching was done, their models indicated if the Greeks cut spending by 100 percent, the economy would shrink 50 percent. As things turned out, it shrunk 150 percent. And that is the problem with mathematical models.

We all have this strange delusion that anything stated in numbers that has a formula or an algorithm must be objective and a statement of fact. Any data or model that is not put through the crucible of human judgement is useless, when it comes to making calls about the future.

The big picture can never be gleaned from just analytics. Models create sameness, while human judgement supported by analytics brings in enterprise. Models, people believe, cut down risks. But because of standardisation they actually amplify risk.

What do I mean by that? Think fixed telephone lines. If a mathematician were to build a model on how the business of telephony would evolve, it would perhaps take in all of the variables and come up with a set of numbers that a business can be built on. But could any model have predicted the emergence of cellular phones? For that matter, a hundred years ago, what mathematical model could have predicted air-planes would be the dominant mode of global travel?

Models anchor us to paradigms and inhibit innovation. However, human enterprise has the ability to bet on the success of a new technology. At worst, it puts to risk a single organisation, never the whole system.

Nobel Laureate and behavioural economist Daniel Kahneman dwells in detail about the futility of trusting the analytical mind to the exclusion of the intuitive mind in his book, Thinking Fast and Slow. He, through experiments, establishes that the analytical mind is useful in assessing risks under standard and repeatable conditions. He proves it is ineffective in assessing both risks and opportunities, when the environment is non-standard or involves assessing human behaviour or is volatile.

It is here that the intuitive judgement trumps over analytical models. He argues eloquently that markets and humans often behave irrationally. Hence over-dependence on rational models can lead us to missed opportunities or ignored threats.

Only in science can data be extrapolated into the future and reasonably valid predictions be made. Even here scientific models struggle to predict volatile events such as weather and earthquakes. This is where scientists who have a “nose” to support their inferences from the models, win over pure model-based ones. Try predicting the course of a sporting event based on any model, and the perils of model-based thinking will hit you hard.

We will call a few wrong but will also get a few correct. But if we call everything on the basis of the need for certainty of results, then we will call everything late and lose competitive advantage. When business leaders get stuck to models, especially those built by others or from the past, they will have nothing different and concrete to offer. Sticking to the past cuts risks or normalises it, but will not help call the future and break out. History tells us that no one learns from others or from their own past.