Gerd Gigerenzer is a strong advocate of the idea that simple heuristics can make us smart. We don’t need complex models of the world to make good decisions.
The classic example is the gaze heuristic. Rather than solving a complex equation to catch a ball, which requires us to know the ball’s speed and trajectory and the effect of the wind, a catcher can simply run to keep the ball at a constant angle in the air, leading them to the point where it will land.
Gigerenzer’s faith in heuristics is often taken to be based on the idea that people have limited processing capacity and are unable to solve the complex optimisation problems that would be needed in the absence of these rules. However, as Gigerenzer points out in Rationality for Mortals: How People Cope with Uncertainty, this is perhaps the weakest argument for heuristics:
[W]e will start off by mentioning the weakest reason. With simple heuristics we can be more confident that our brains are capable of performing the necessary calculations. The weakness of this argument is that it is hard to judge what complexity of calculation or memory a brain might achieve. At the lower levels of processing, some human capabilities apparently involve calculations that seem surprisingly difficult (e.g., Bayesian estimation in a sensorimotor context: Körding & Wolpert, 2004). So if we can perform these calculations at that level in the hierarchy (abilities), why should we not be able to evolve similar complex strategies to replace simple heuristics?
Rather, the advantage of heuristics lies in their low information requirements, their speed and, importantly, their accuracy:
One answer is that simple heuristics often need access to less information (i.e. they are frugal) and can thus make a decision faster, at least if information search is external. Another answer – and a more important argument for simple heuristics – is the high accuracy they exhibit in our simulations. This accuracy may be because of, not just in spite of, their simplicity. In particular, because they have few parameters they avoid overfitting data in a learning sample and, consequently, generalize better across other samples. The extra parameters of more complex models often fit the noise rather than the signal. Of course, we are not saying that all simple heuristics are good; only some simple heuristics will perform well in any given environment.
As the last sentence indicates, Gigerenzer is careful not to make any claims that heuristics generally outperform. A statement that a heuristic is “good” is ill-conceived without considering the environment in which it will be used. This is the major departure of Gigerenzer’s ecological rationality from the standard approach in the behavioural sciences, where the failure of a heuristic to perform in an environment is taken as evidence of bias or irrationality.
Once you have noted what heuristic is being used in what environment, you can have more predictive power than in a well-solved optimisation model. For example. an optimisation model to catch a ball will simply predict that the catcher will be at the place and time where the ball lands. Once you understand that they use the gaze heuristic to catch the ball, you can also predict the path that they will take to get to the ball – including that they won’t simply run in a straight line to catch it. If a baseball or cricket coach took the optimisation model too seriously, they would tell the catcher that they are running inefficiently by not going straight to where it will land. Instructions telling them to run is a straight line will likely make their performance worse.