It’s surprising how often, in highly complex domains where we are trying to figure out what to do, an obvious or simple default can perform extremely well. This is sometimes even true when the defaults are clearly not optimal.
Here are simple defaults in four complex domains that can be surprisingly hard to outperform:
1. Charity: give money to the poorest non-drug addicted people you can find, and let them do whatever they want with the money.
GiveWell, which has spent years looking for the most effective, evidence-based giving opportunities, has concluded that Give Directly is among the best options it can find. But all it does is use smart ways to locate and give money to really poor people internationally.
And yet, a tremendous amount of effort goes into developing clever interventions that provide goods or services that people struggle to provide for themselves. Yet most of these don’t seem to perform that well when in terms of outcomes compared to simply giving money to very poor people.
2. Investing: put some money in long-term government bonds and the rest in the stock market (the ratio determined by risk tolerance and need for liquidity).
The significant majority of people who attempt to do better than this historically have underperformed this simple strategy (due largely to trading commissions, taxes, management fees, the existence of a small number of highly skilled players, and the fact that the average performance of market participants is the same as the average return of the market).
Yet, the market is not perfectly “efficient,” and there are many other asset classes beyond stocks and long-term government bonds that one could mix.
3. Predictions: train a simple linear regression model based on historical data to predict the variable of interest.
Evidence suggests that linear regression models (ordinary “least squares” regression) beat human experts at many types of forecasting (e.g., see Paul Meehl’s work on statistical prediction). Additionally, quite often, a simple linear regression does as well (or close to it) as attempts to use complex models on data-driven prediction problems.
And yet, linear regression is not anywhere close to the cutting-edge methods for making predictions.
4. Behavior: align people’s monetary incentives with the behavior that is desirable.
It’s remarkable how often the way people in a field behave seems to end up aligning with their monetary incentives (e.g., think about cases where employees at banks create tons of fake accounts on behalf of unsuspecting clients because of a monetary incentive to do so). As famed investor Charlie Munger put it, “I think I’ve been in the top 5% of my age cohort all my life in understanding the power of incentives, and all my life I’ve underestimated it. And never a year passes, but I get some surprise that pushes my limit a little farther.” Part of the power of monetary incentives is from people directly responding to the money itself, but another part is because those who aren’t responsive to the existing monetary incentives tend to be squeezed out of a field, advance less quickly, or don’t like the environment and so quit on their own accord. And since monetary incentives are usually more tangible than other forms of rewards, there is often a more reliable feedback loop for those trying to optimize for money than for other goods.
And yet, clearly, there are many motivators that people have beyond just money and work achievement, and money is sometimes a problematic or counterproductive motivator.
Sometimes, simple methods work almost unbelievably well.
This piece was first written on June 10, 2017, and first appeared on my website on March 12, 2025.
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