# Bounded rational decision-making from elementary computations that reduce uncertainty

arXiv: https://arxiv.org/abs/1904.03964 · HTML: https://mlsanity.com/paper/1904.03964/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Apr 2019.

## Built on by (most cited first, 4 of 4)

- [Bayesian Reinforcement Learning with Limited Cognitive Load](https://mlsanity.com/paper/2305.03263/) (2023)
- [Bounded rationality for relaxing best response and mutual consistency: The Quantal Hierarchy model of decision-making](https://mlsanity.com/paper/2106.15844/) (2021)
- [The Alignment Bottleneck](https://mlsanity.com/paper/2509.15932/) (2025)
- [Exploiting Expertise of Non-Expert and Diverse Agents in Social Bandit Learning: A Free Energy Approach](https://mlsanity.com/paper/2603.11757/) (2026)

## Abstract (arXiv)

In its most basic form, decision-making can be viewed as a computational process that progressively eliminates alternatives, thereby reducing uncertainty. Such processes are generally costly, meaning that the amount of uncertainty that can be reduced is limited by the amount of available computational resources. Here, we introduce the notion of elementary computation based on a fundamental principle for probability transfers that reduce uncertainty. Elementary computations can be considered as the inverse of Pigou-Dalton transfers applied to probability distributions, closely related to the concepts of majorization, T-transforms, and generalized entropies that induce a preorder on the space of probability distributions. As a consequence we can define resource cost functions that are order-preserving and therefore monotonic with respect to the uncertainty reduction. This leads to a comprehensive notion of decision-making processes with limited resources. Along the way, we prove several new results on majorization theory, as well as on entropy and divergence measures.