Experts versus Crowds
In his best-selling book “Outliers”, Malcolm Gladwell studies some of the human outliers in our midst. People such as highly successful sports players come to mind. Gladwell postulates that to become an expert, an individual must devote a minimum of 10,000 hours to their discipline. For those individuals that have been highly successful, often if we look back at their upbringing, we will find a best-case environment that allowed them to quickly get the 10,000 hours, setting them ahead of their peers earlier in life. That head-start can sometimes be overwhelming and un-surpassable to competitors.
But Nassim Taleb, the outspoken author of ‘The Black Swan’, states that experts, due to the time and focus required to achieve a level of expertise, will effectively tunnel, or evolve into narrowly-focused individuals of limited value in complex decision-making systems. In an interesting study, psychologist James Shanteau discovered that some disciplines have experts and others, due to their very fluid and transitory nature do not usually support experts for long.
This suggests that experts can only survive in disciplines where Black Swans are inconsequential: think of chess masters, figure-skating judges and astronomers – does anyone really get hurt in these fields? In finance, where a Black Swan might occur once or more within the 10,000 hour timeframe required to achieve expertise, and are extremely consequential to people’s savings, we rarely see experts surviving for long.
Taleb goes on to suggest that we need not always question the expertise delivered, but rather we need to question the error rate of experts. An overconfident expert, or one with a lack of appreciation of their own error rate, is a dangerous expert indeed.
In the 21st century, a kind of anti-expert decision-making theory has evolved. The Wisdom of Crowds, a book by James Surowiecki, popularized the concept. Numerous attempts, some successful, others not, have been made to profit from the collective wisdom of the crowd. Complex text mining systems are trolling the internet as you read this, attempting to decipher news stories, blogs and other possible sources of investor sentiment for collective wisdom. Unfortunately, information overload and the challenges of signal-to-noise ratio lead to similar hurdles when attempting to derive wisdom from large quantities of crowd data using automated methods.
Actionable Intelligence and The Spies Amongst Us.
The intelligence community has adopted a different approach to information. For reasons of national security, intelligence gathering is all about averting Black Swans.
Unlike the financial markets that contain an abundance of structured data available for sale or free, the spying business is heavily dependent on unstructured information gathering. Both communities have at their disposal massive amounts of real-time data, with sophisticated information infrastructures feeding analytical and visualization tools that improve human cognition. The goal in both the financial and intelligence communities is to generate actionable intelligence – information that facilitates timely decision-making under conditions of uncertainty.
Spies have a concept called “sense-making”, or the ability to create situational awareness or “make sense” of an ambiguous or highly complex situation. When we evaluate opportunities for investment, we are conducting a sense-making exercise to improve our decisions.
Understanding information overload and its effect on us is important to investors and traders. We need to be more aware of some of the human weaknesses that affect our judgments. By using tools that control and filter data flow, we can expect to generate more actionable intelligence, heighten our tradable idea flow, and add some structure to our trading. If you begin by developing a systematic way to optimize idea flow, then that approach can be subsequently monitored for effectiveness.
If you are a beginner investor, you are well advised to commence tracking all your activities related to trading – including the trades themselves – in a trading diary. This is an important first step to develop a self-reliant, structured, monitored and improvable trading methodology.