Identifying resource-rational heuristics for risky choice.

Abstract

Perfectly rational decision making is almost always out of reach for people because their computational resources are limited. 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 theoretical framework that allows us to use methods from machine learning to automatically derive the best heuristic to use in any given situation by considering how to make the best use of limited cognitive resources. To demonstrate the generalizability and accuracy of our method, we compare the heuristics it discovers against those used by people across a wide range of multi-attribute risky choice environments in a behavioral experiment that is an order of magnitude larger than any previous experiments of its type. Our method rediscovered known heuristics, identifying them as rational strategies for specific environments, and discovered novel heuristics that had been previously overlooked. Our results show that people adapt their decision strategies to the structure of the environment and generally make good use of their limited cognitive resources, although their strategy choices do not always fully exploit the structure of the environment.

Older adults select different but not simpler strategies than younger adults in risky choice

“…First, although we considered a comprehensive set of decision strategies [ 34 , 35 ], we do not claim that it is exhaustive. An alternative approach would be to identify decision strategies in a data-driven way from process data [ 55 ] (but see [ 47 ] for a more critical perspective). Second, our operationalization of strategy cost follows the framework of elementary information processes [ 49 ], as is common in implementations of the resource-rational strategy selection model [ 32 , 39 , 48 ].…”