creators—for instance, a good model of the Earth’s climate should be able to predict the

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 1s 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 1f 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 94 HOUSE_OVERSIGHT_016897