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The 10,000-Hour Myth?

The 10,000-Hour Myth?

Why 90% of active managers underperform the stock market

Logo of Thinksightful: Counterintuitive ideas from the world's best minds

Thinksightful

Thinksightful

May 12, 2023

We’ve all heard of the 10,000-hour rule, claiming that it takes that long for anyone to develop expertise in their craft. But is it really that simple?

Data shows that over a 15-year time horizon, 90% of active managers underperformed the stock market, even though they were experts in their field, with well over 10,000 hours of experience under their belt. So, why does this trend repeat over and over again? When should we trust the experts, and when should we not?

To answer these questions, we must first take a look at how we learn and what we can learn.

It’s All About the Type of Learning Environment

To determine why experts get things wrong, it is important to first understand how expertise is built. In fact, it is essential to know where expertise can be built.

There are two kinds of learning environments: Kind and Wicked. Kind learning environments have pre-determined rules that don’t change dynamically.

Chess is an example of a kind learning environment, where tactical patterns can be learnt, and mastery over them makes you a better player. Feedback is also immediate, allowing you to analyze mistakes precisely and learn from them.

On the other hand, wicked learning environments, like the stock market, are much more complex, with multiple factors involved, and dynamic rules. In wicked learning environments, the feedback is not immediate, and there is a great degree of uncertainty. Experience can lead experts astray, as there is a tendency to overgeneralize and fall prey to recency bias.

So, in making decisions, it is important to consider the type of learning environment involved, and what kind of expertise is needed to navigate it.

The Relevance of Experience Matters More Than Quantity

In “The Myth of Experience”, Soyer and Hogarth suggest a way to filter perspectives by asking two main questions.

First, what is missing from the experience?

Often, experiences can be thought of as the “scenario that played out,” rather than the possible cases that could have played out. Just because a scenario played out in a particular way in the past doesn’t suggest that it will play out the same way in the future.

Second, what is irrelevant?

Some opinions may be formed due to certain scenarios experienced by the experts you are consulting that may not be relevant anymore. This is also true for scenarios that appear very infrequently, or as Nassim Taleb calls them “Black Swans”.

In “Range: How Generalists Triumph in a Specialized World”, David Epstein gives the example of the Challenger disaster.

The engineers and management team working on the Challenger mission at NASA were both competent and experienced. However, when deciding where to go ahead with the launch on a colder-than-normal day, they missed a crucial fact

Out of all the launch data they had, only 7 data points were truly valid for their existing launch condition. These data indicated that there was an overwhelming chance of failure for the O-rings, which eventually did lead to disaster.

In this case, the engineers’ experience worked against them. The decades of experience didn’t matter- the relevance would have made the difference.

Thus, to evaluate possible scenarios, it is important to ask for details of previous experiences, and to have as much context as possible, because, in a wicked learning environment, what is available to experience is rarely all there is that is relevant.

Breadth of Experiences Can Be Crucial in Improving Decision-Making

Epstein observed something particularly interesting in experts. They specialized later than most people and often had a broad range of interests.

Understanding a diverse range of topics and forming connections between them help develop true expertise in most real world scenarios. These integrators referred to as foxes, also perform better at longer term predictions than specialists referred to as hedgehogs.

So, when trying to understand when to take advice and when not to, try evaluating the breadth of the experience of the person giving advice, especially across fields that will affect your decision.

Be Curious

Experience does help, but it can also create biases.

A healthy curiosity and some skepticism can go a long way in avoiding these blind spots. Treat experiences- others as well as yours- as data, and critically analyze whether parts of it may be irrelevant or some information may be missing from it. So, be curious about the world.

Take nothing at face value. Open yourself to a breadth of experiences and develop your “Range”. Be humble about the fact that your own experiences may also have taught you the wrong lessons. Maybe the real experts are those who understand that they are not experts at all, and that they can be wrong too.

We’ve all heard of the 10,000-hour rule, claiming that it takes that long for anyone to develop expertise in their craft. But is it really that simple?

Data shows that over a 15-year time horizon, 90% of active managers underperformed the stock market, even though they were experts in their field, with well over 10,000 hours of experience under their belt. So, why does this trend repeat over and over again? When should we trust the experts, and when should we not?

To answer these questions, we must first take a look at how we learn and what we can learn.

It’s All About the Type of Learning Environment

To determine why experts get things wrong, it is important to first understand how expertise is built. In fact, it is essential to know where expertise can be built.

There are two kinds of learning environments: Kind and Wicked. Kind learning environments have pre-determined rules that don’t change dynamically.

Chess is an example of a kind learning environment, where tactical patterns can be learnt, and mastery over them makes you a better player. Feedback is also immediate, allowing you to analyze mistakes precisely and learn from them.

On the other hand, wicked learning environments, like the stock market, are much more complex, with multiple factors involved, and dynamic rules. In wicked learning environments, the feedback is not immediate, and there is a great degree of uncertainty. Experience can lead experts astray, as there is a tendency to overgeneralize and fall prey to recency bias.

So, in making decisions, it is important to consider the type of learning environment involved, and what kind of expertise is needed to navigate it.

The Relevance of Experience Matters More Than Quantity

In “The Myth of Experience”, Soyer and Hogarth suggest a way to filter perspectives by asking two main questions.

First, what is missing from the experience?

Often, experiences can be thought of as the “scenario that played out,” rather than the possible cases that could have played out. Just because a scenario played out in a particular way in the past doesn’t suggest that it will play out the same way in the future.

Second, what is irrelevant?

Some opinions may be formed due to certain scenarios experienced by the experts you are consulting that may not be relevant anymore. This is also true for scenarios that appear very infrequently, or as Nassim Taleb calls them “Black Swans”.

In “Range: How Generalists Triumph in a Specialized World”, David Epstein gives the example of the Challenger disaster.

The engineers and management team working on the Challenger mission at NASA were both competent and experienced. However, when deciding where to go ahead with the launch on a colder-than-normal day, they missed a crucial fact

Out of all the launch data they had, only 7 data points were truly valid for their existing launch condition. These data indicated that there was an overwhelming chance of failure for the O-rings, which eventually did lead to disaster.

In this case, the engineers’ experience worked against them. The decades of experience didn’t matter- the relevance would have made the difference.

Thus, to evaluate possible scenarios, it is important to ask for details of previous experiences, and to have as much context as possible, because, in a wicked learning environment, what is available to experience is rarely all there is that is relevant.

Breadth of Experiences Can Be Crucial in Improving Decision-Making

Epstein observed something particularly interesting in experts. They specialized later than most people and often had a broad range of interests.

Understanding a diverse range of topics and forming connections between them help develop true expertise in most real world scenarios. These integrators referred to as foxes, also perform better at longer term predictions than specialists referred to as hedgehogs.

So, when trying to understand when to take advice and when not to, try evaluating the breadth of the experience of the person giving advice, especially across fields that will affect your decision.

Be Curious

Experience does help, but it can also create biases.

A healthy curiosity and some skepticism can go a long way in avoiding these blind spots. Treat experiences- others as well as yours- as data, and critically analyze whether parts of it may be irrelevant or some information may be missing from it. So, be curious about the world.

Take nothing at face value. Open yourself to a breadth of experiences and develop your “Range”. Be humble about the fact that your own experiences may also have taught you the wrong lessons. Maybe the real experts are those who understand that they are not experts at all, and that they can be wrong too.

We’ve all heard of the 10,000-hour rule, claiming that it takes that long for anyone to develop expertise in their craft. But is it really that simple?

Data shows that over a 15-year time horizon, 90% of active managers underperformed the stock market, even though they were experts in their field, with well over 10,000 hours of experience under their belt. So, why does this trend repeat over and over again? When should we trust the experts, and when should we not?

To answer these questions, we must first take a look at how we learn and what we can learn.

It’s All About the Type of Learning Environment

To determine why experts get things wrong, it is important to first understand how expertise is built. In fact, it is essential to know where expertise can be built.

There are two kinds of learning environments: Kind and Wicked. Kind learning environments have pre-determined rules that don’t change dynamically.

Chess is an example of a kind learning environment, where tactical patterns can be learnt, and mastery over them makes you a better player. Feedback is also immediate, allowing you to analyze mistakes precisely and learn from them.

On the other hand, wicked learning environments, like the stock market, are much more complex, with multiple factors involved, and dynamic rules. In wicked learning environments, the feedback is not immediate, and there is a great degree of uncertainty. Experience can lead experts astray, as there is a tendency to overgeneralize and fall prey to recency bias.

So, in making decisions, it is important to consider the type of learning environment involved, and what kind of expertise is needed to navigate it.

The Relevance of Experience Matters More Than Quantity

In “The Myth of Experience”, Soyer and Hogarth suggest a way to filter perspectives by asking two main questions.

First, what is missing from the experience?

Often, experiences can be thought of as the “scenario that played out,” rather than the possible cases that could have played out. Just because a scenario played out in a particular way in the past doesn’t suggest that it will play out the same way in the future.

Second, what is irrelevant?

Some opinions may be formed due to certain scenarios experienced by the experts you are consulting that may not be relevant anymore. This is also true for scenarios that appear very infrequently, or as Nassim Taleb calls them “Black Swans”.

In “Range: How Generalists Triumph in a Specialized World”, David Epstein gives the example of the Challenger disaster.

The engineers and management team working on the Challenger mission at NASA were both competent and experienced. However, when deciding where to go ahead with the launch on a colder-than-normal day, they missed a crucial fact

Out of all the launch data they had, only 7 data points were truly valid for their existing launch condition. These data indicated that there was an overwhelming chance of failure for the O-rings, which eventually did lead to disaster.

In this case, the engineers’ experience worked against them. The decades of experience didn’t matter- the relevance would have made the difference.

Thus, to evaluate possible scenarios, it is important to ask for details of previous experiences, and to have as much context as possible, because, in a wicked learning environment, what is available to experience is rarely all there is that is relevant.

Breadth of Experiences Can Be Crucial in Improving Decision-Making

Epstein observed something particularly interesting in experts. They specialized later than most people and often had a broad range of interests.

Understanding a diverse range of topics and forming connections between them help develop true expertise in most real world scenarios. These integrators referred to as foxes, also perform better at longer term predictions than specialists referred to as hedgehogs.

So, when trying to understand when to take advice and when not to, try evaluating the breadth of the experience of the person giving advice, especially across fields that will affect your decision.

Be Curious

Experience does help, but it can also create biases.

A healthy curiosity and some skepticism can go a long way in avoiding these blind spots. Treat experiences- others as well as yours- as data, and critically analyze whether parts of it may be irrelevant or some information may be missing from it. So, be curious about the world.

Take nothing at face value. Open yourself to a breadth of experiences and develop your “Range”. Be humble about the fact that your own experiences may also have taught you the wrong lessons. Maybe the real experts are those who understand that they are not experts at all, and that they can be wrong too.