Many everyday life decisions require allocating finite resources, such as attention or time, to examine multiple available options, like choosing a food supplier online. In cases like these, resources can be spread across many options (breadth) or focused on a few of them (depth). Whilst theoretical work has described how finite resources should be allocated to maximize utility in these problems, evidence about how humans balance breadth and depth is currently lacking. We introduce a novel experimental ...
Many everyday life decisions require allocating finite resources, such as attention or time, to examine multiple available options, like choosing a food supplier online. In cases like these, resources can be spread across many options (breadth) or focused on a few of them (depth). Whilst theoretical work has described how finite resources should be allocated to maximize utility in these problems, evidence about how humans balance breadth and depth is currently lacking. We introduce a novel experimental paradigm where humans make a many-alternative decision under finite resources. In an imaginary scenario, participants allocate a finite budget to sample amongst multiple apricot suppliers in order to estimate the quality of their fruits, and ultimately choose the best one. We found that at low budget capacity participants sample as many suppliers as possible, and thus prefer breadth, whereas at high capacities participants sample just a few chosen alternatives in depth, and intentionally ignore the rest. The number of alternatives sampled increases with capacity following a power law with an exponent close to 3/4. In richer environments, where good outcomes are more likely, humans further favour depth. Participants deviate from optimality and tend to allocate capacity amongst the selected alternatives more homogeneously than it would be optimal, but the impact on the outcome is small. Overall, our results undercover a rich phenomenology of closeto-optimal behaviour and biases in complex choices.
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