How should we think and decide to make the best possible use of our precious time and
limited cognitive resources? And how do people's cognitive strategies compare to this ideal?
We study these questions in the domain of multi-alternative risky choice using the
methodology of resource-rational analysis. To answer the first question, we leverage a new
meta-level reinforcement learning algorithm to derive optimal heuristics for four different
risky choice environments. We find that our method rediscovers two fast-and-frugal …

For computationally limited agents such as humans, perfectly rational decision-making is
almost always out of reach. Instead, people may rely on computationally frugal heuristics
that usually yield good outcomes. Although previous research has identified many such
heuristics, discovering good heuristics and predicting when they will be used remains
challenging. Here, we present a machine learning method that identifies the best heuristics
to use in any given situation. To demonstrate the generalizability and accuracy of our …