Have you ever wondered how some individuals always seem to be in the right place at the right time? Why do certain leaders get all the lucky breaks, while the rest of us have to work hard for every scrap? There is an old adage that says, "It's better to be lucky than good". And while that may certainly explain some things, I've honestly always believed that individuals can and often do create their own luck (see my post, "Good luck is the twin of hard work"). Rather than just being lucky, these leaders take advantage of the opportunities that present themselves. The television star Ashton Kutcher said it best in an acceptance speech at the 2013 Teen Choice Awards when he said, "Opportunity looks a lot like hard work." Janice Kaplan and Barnaby Marsh write (in a 2018 Wall Street Journal article, "To be successful, make your own luck") that "Luck is at the intersection of random chance, talent, and hard work...People who have a talent for making luck for themselves grab the unexpected opportunities that come along."
Whose right here? How much does luck play into success? Consider for a moment that the chance of becoming a CEO is heavily influenced by your month of birth (children born in June and July are less likely to become CEO's compared to children born in March or April). Did you know that individuals with last names earlier in the alphabet are more likely to receive tenure in top ranked economic departments? And Malcolm Gladwell discussed the well-known phenomenon that elite hockey players are generally born between the months of January and March in his book, Outliers: The Story of Success. Of course, all of these are mere associations (not cause-and-effect relationships), but it's hard to ignore how random these findings are when it comes to explaining success.
Ben Cohen wrote an article in the weekend edition of the Wall Street Journal a few weeks ago entitled "Winners Know How to Hardness Luck". Cohen talks about three scientists who recently won the 2022 Ig Nobel Prize in Management (these prizes were first awarded in 1991 by the Annals of Improbable Research to "honor achievements that first make people laugh, and then make them think") - Drs. Alessandro Pluchino, Alessio Emanuele Biondo, and Andrea Rapisarda, who were honored for their 2018 paper, "Talent vs Luck: The role of randomness in success and failure" (notably, Pluchino and Rapisarda won the 2010 Ig Nobel Prize in Management for their work on the so-called Peter Principle, a topic for a future post).
Pluchino and Raspisarda (who are both physicists) teamed up with Biondo (an economist) and proposed what is called a "toy mathematical model" in an attempt to model success as a function of talent and luck (which they called their "Talent vs Luck" or TvL model). Basically, they simulated the evolution of careers of a population over a worklife of 40 years (from age 20-60) using what I call fancy mathematics! Actually, as a non-mathematician, I was able to follow how they constructed their model fairly easily (and note, any success that I've had in mathematics is due more to luck than talent!).
They simulated a population consisting of N individuals with talent T (intelligence, skills, ability, etc), a normally distributed variable around the interval [0,1] with a given mean and standard deviation. Individuals were randomly distributed in fixed positions within a square world. At the beginning of the simulation, all individuals were endowed with the same amount of capital, C, which represented their success/wealth. During the next 40 years (work life between 20 and 60 years of age), these individuals were randomly exposed (approximately every six months) to events (lucky, unlucky, or no event). Lucky events doubled their capital with a probability proportional to their talent (i.e. the more talented the individuals were, the greater the increase in their capital), while unlucky events halved their capital. Exposure to a nonevent did not change the individuals' capital.
Pluchino, Raspisarda, and Biondo ran several simulations, and the results were very consistent. In general, the individuals with higher talent had a higher probability of success. However, talent alone could not explain success by itself, as the most talented individuals were rarely the most successful. As a matter of fact, mediocre-but-lucky people were much more successful than their more-talented-but-unlucky counterparts. And the most successful individuals tended to be the ones with just slightly above average talent but with above average luck.
It is a well-known fact that intelligence, as a proxy measure of talent, exhibits a Gaussian or normal distribution (the so-called bell-shaped curve with the mean at the peak of the curve). Wealth, as a proxy measure of success, typically follows a power law (also known as a Pareto law) such that very few individuals in a population account for the greatest share of overall wealth. In the TvL model, the 20 most successful individuals accounted for 44% of the total amout of success in the population (closely approximateing Pareto's 80/20 rule).
I've talked about something in the past known as the "Matthew Effect" (simply stated as "the rich get richer, the poor get poorer"). Meritocratic strategies are often used in society to assign honors, funds, or rewards based upon the past success (for example, a scientist's application for grant funding from the National Institutes of Health is more likely to be approved if that individual has a proven track record of successful research, as demonstrated by publications and previous grant funding). Pluchino, Raspisarda, and Biondo test this kind of strategy using their TvL model. Starting from the same parameters in their original model, they assigned external funds to individuals based using four different methods:
1. Egalitarian method (funds were distributed equally across the board)
2. Elitarian method (funds were distributed based upon past performance)
3. Mixed method (a given percentage of funds, or premium was distributed to the most successful individuals and the remaining amount in smaller equal parts to the remaining individuals)
4. Selective random method (funds were distributed to a given percentage of individuals who were randomly selected)
A fixed amount of external funds were distributed every five years, throughout the entire simulation period of 40 years. Again, in the absence of these external funds, the most successful individuals were the very lucky individuals with average talent. Surprisingly, the most effective funding strategy was the egalitarian method (distributing funds equally to every individual). The least effective strategy was the elitarian method of distributing funds based upon past performance (which, as stated above, is perhaps the most commonly used method in our society today). Pluchino, Raspisarda, and Biondo called the latter approach "naively meritocratic". Notably, similar findings have been previously reported by these same investigators as well as others (for a review, see the Scientific American blog post, "The role of luck in life success is far greater than we realized").
So, what are we to conclude from this study by Pluchino, Raspisarda, and Biondo? Most importantly, the old adage that it's better to be lucky than good is only mostly true. Repeating the quote from Janice Kaplan and Barnaby Marsh from their 2018 Wall Street Journal article ("To be successful, make your own luck"), "Luck is at the intersection of random chance, talent, and hard work." Success does not depend on just talent alone. Rather, success is a function of talent, hard work, and luck. With this in mind, perhaps we should reconsider some of the ways that society assigns honors, funds, and rewards. Past performance is not necessarily a sign of future success. I want to continue on this theme of success, talent, and luck in my next post.
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