Reflections on the intersection of economics, history, politics, psychology and science

What makes someone an expert? And how is expertise different from intelligence? This podcast is all about expertise, how we acquire it, how do people view others’ expertise, and the notion of expertise transference from one domain to another. We touch on a number of psychological phenomena, including the Dunning-Kruger Effect, which is a cognitive bias whereby people with low ability at a task overestimate that ability.

Expertise is about deep knowledge and understanding of a particular topic. There is the famous notion from the book Outliers by Malcolm Gladwell, where he popularized what is known as the 10,000 hour rule, meaning that it takes 10,000 hours of intensive practice to achieve mastery of any complex skill. Despite that, people often underappreciate the level of study required to be an expert and fall into a trap believing that they can expand their own domains of expertise without putting in that scale of effort. 

The podcast also discusses the notion of expertise transference, a phenomenon where we expand our view of expertise in others to areas beyond where that person is currently an expert. This can include assuming celebrities have deep skills and knowledge when they only have cursory ones, or it can be about businesspeople who may have expertise in one area (function, industry, stage, etc.) of business but necessarily in many other areas. And of course, we see this in the political arena, where many believe that seemingly successful businesspeople should be effectively political leaders. 

The podcast ends with a case study on how this expertise transference can go horribly awry, and that is with former President Donald Trump. Even ignoring Trump’s personality and intellectual defects, understanding the notion of expertise must lead to the conclusion that his specific business experience actually doomed him to becoming an ineffective President.

Key Terms Used

Dunbar’s Number, Dunning-Kruger Effect, Hindsight Bias, Peter Principle, Risk

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