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creators—for instance, a good model of the Earth’s climate should be able to predict the
consequences of a rising global temperature even if this wasn’t something considered by
the scientists who designed it. However, when it comes to understanding the human
mind, these two goals—accuracy and generalizability—have long been at odds with each
other.

At the far extreme of generalizability are rational theories of cognition. These
theories describe human behavior as a rational response to a given situation. A rational
actor strives to maximize the expected reward produced by a sequence of actions—an
idea widely used in economics precisely because it produces such generalizable
predictions about human behavior. For the same reason, rationality is the standard
assumption in inverse-reinforcement-learning models that try to make inferences from
human behavior—perhaps with the concession that humans are not perfectly rational
agents and sometimes randomly choose to act in ways unaligned with or even opposed to
their best interests.

The problem with rationality as a basis for modeling human cognition is that it is
not accurate. In the domain of decision making, an extensive literature—spearheaded by
the work of cognitive psychologists Daniel Kahneman and Amos Tversky—has
documented the ways in which people deviate from the prescriptions of rational models.
Kahneman and Tversky proposed that in many situations people instead follow simple
heuristics that allow them to reach good solutions at low cognitive cost but sometimes
result in errors. To take one of their examples, if you ask somebody to evaluate the
probability of an event, they might rely on how easy it is to generate an example of such
an event from memory, consider whether they can come up with a causal story for that
event’s occurring, or assess how similar the event is to their expectations. Each heuristic
is a reasonable strategy for avoiding complex probabilistic computations, but also results
in errors. For instance, relying on the ease of generating an event from memory as a
guide to its probability leads us to overestimate the chances of extreme (hence extremely
memorable) events such as terrorist attacks.

Heuristics provide a more accurate model of human cognition but one that is not
easily generalizable. How do we know which heuristic people might use in a particular
situation? Are there other heuristics they use that we just haven’t discovered yet?
Knowing exactly how people will behave in a new situation is a challenge: Is this
situation one in which they would generate examples from memory, come up with causal
stories, or rely on similarity?

Ultimately, what we need is a way to describe how human minds work that has
the generalizability of rationality and the accuracy of heuristics. One way to achieve this
goal is to start with rationality and consider how to take it in a more realistic direction. A
problem with using rationality as a basis for describing the behavior of any real-world
agent is that, in many situations, calculating the rational action requires the agent to
possess a huge amount of computational resources. It might be worth expending those
resources if you’re making a highly consequential decision and have a lot of time to
evaluate your options, but most human decisions are made quickly and for relatively low
stakes. In any situation where the time you spend making a decision is costly—at the
very least because it’s time you could spend doing something else—the classic notion of
rationality is no longer a good prescription for how one should behave.

To develop a more realistic model of rational behavior, we need to take into

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