Optimal allocation of finite sampling capacity in accumulator models of multi-alternative decision making

TL;DR

This paper investigates how to optimally allocate limited sampling resources among multiple options in accumulator models, revealing phase transitions and optimal strategies depending on capacity and discriminability.

Contribution

It introduces a model for optimal sampling allocation in multi-alternative decision making, highlighting phase transitions and the optimal number of options to sample based on capacity.

Findings

Optimal policies undergo a sharp transition with capacity.

For small capacity, sampling exactly five options is optimal.

For large capacity, the number of sampled options grows sub-linearly.

Abstract

When facing many options, we narrow down our focus to very few of them. Although behaviors like this can be a sign of heuristics, they can actually be optimal under limited cognitive resources. Here we study the problem of how to optimally allocate limited sampling time to multiple options, modelled as accumulators of noisy evidence, to determine the most profitable one. We show that the effective sampling capacity of an agent increases with both available time and the discriminability of the options, and optimal policies undergo a sharp transition as a function of it. For small capacity, it is best to allocate time evenly to exactly five options and to ignore all the others, regardless of the prior distribution of rewards. For large capacities, the optimal number of sampled accumulators grows sub-linearly, closely following a power law for a wide variety of priors. We find that allocating equal times to the sampled accumulators is better than using uneven time allocations. Our work highlights that multi-alternative decisions are endowed with breadth-depth tradeoffs, demonstrates how their optimal solutions depend on the amount of limited resources and the variability of the environment, and shows that narrowing down to a handful of options is always optimal for small capacities.